Validation and Calibration of Nuclear Thermal Hydraulics Multiscale Multiphysics Models - Subcooled Flow Boiling Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anh Bui; Nam Dinh; Brian Williams

In addition to validation data plan, development of advanced techniques for calibration and validation of complex multiscale, multiphysics nuclear reactor simulation codes are a main objective of the CASL VUQ plan. Advanced modeling of LWR systems normally involves a range of physico-chemical models describing multiple interacting phenomena, such as thermal hydraulics, reactor physics, coolant chemistry, etc., which occur over a wide range of spatial and temporal scales. To a large extent, the accuracy of (and uncertainty in) overall model predictions is determined by the correctness of various sub-models, which are not conservation-laws based, but empirically derived from measurement data. Suchmore » sub-models normally require extensive calibration before the models can be applied to analysis of real reactor problems. This work demonstrates a case study of calibration of a common model of subcooled flow boiling, which is an important multiscale, multiphysics phenomenon in LWR thermal hydraulics. The calibration process is based on a new strategy of model-data integration, in which, all sub-models are simultaneously analyzed and calibrated using multiple sets of data of different types. Specifically, both data on large-scale distributions of void fraction and fluid temperature and data on small-scale physics of wall evaporation were simultaneously used in this work’s calibration. In a departure from traditional (or common-sense) practice of tuning/calibrating complex models, a modern calibration technique based on statistical modeling and Bayesian inference was employed, which allowed simultaneous calibration of multiple sub-models (and related parameters) using different datasets. Quality of data (relevancy, scalability, and uncertainty) could be taken into consideration in the calibration process. This work presents a step forward in the development and realization of the “CIPS Validation Data Plan” at the Consortium for Advanced Simulation of LWRs to enable quantitative assessment of the CASL modeling of Crud-Induced Power Shift (CIPS) phenomenon, in particular, and the CASL advanced predictive capabilities, in general. This report is prepared for the Department of Energy’s Consortium for Advanced Simulation of LWRs program’s VUQ Focus Area.« less

A Bayesian alternative for multi-objective ecohydrological model specification

NASA Astrophysics Data System (ADS)

Tang, Yating; Marshall, Lucy; Sharma, Ashish; Ajami, Hoori

2018-01-01

Recent studies have identified the importance of vegetation processes in terrestrial hydrologic systems. Process-based ecohydrological models combine hydrological, physical, biochemical and ecological processes of the catchments, and as such are generally more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analysis are essential for ecohydrological modeling. In recent years, Bayesian inference has become one of the most popular tools for quantifying the uncertainties in hydrological modeling with the development of Markov chain Monte Carlo (MCMC) techniques. The Bayesian approach offers an appealing alternative to traditional multi-objective hydrologic model calibrations by defining proper prior distributions that can be considered analogous to the ad-hoc weighting often prescribed in multi-objective calibration. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological modeling framework based on a traditional Pareto-based model calibration technique. In our study, a Pareto-based multi-objective optimization and a formal Bayesian framework are implemented in a conceptual ecohydrological model that combines a hydrological model (HYMOD) and a modified Bucket Grassland Model (BGM). Simulations focused on one objective (streamflow/LAI) and multiple objectives (streamflow and LAI) with different emphasis defined via the prior distribution of the model error parameters. Results show more reliable outputs for both predicted streamflow and LAI using Bayesian multi-objective calibration with specified prior distributions for error parameters based on results from the Pareto front in the ecohydrological modeling. The methodology implemented here provides insight into the usefulness of multiobjective Bayesian calibration for ecohydrologic systems and the importance of appropriate prior distributions in such approaches.

Design of a Two-Step Calibration Method of Kinematic Parameters for Serial Robots

NASA Astrophysics Data System (ADS)

WANG, Wei; WANG, Lei; YUN, Chao

2017-03-01

Serial robots are used to handle workpieces with large dimensions, and calibrating kinematic parameters is one of the most efficient ways to upgrade their accuracy. Many models are set up to investigate how many kinematic parameters can be identified to meet the minimal principle, but the base frame and the kinematic parameter are indistinctly calibrated in a one-step way. A two-step method of calibrating kinematic parameters is proposed to improve the accuracy of the robot's base frame and kinematic parameters. The forward kinematics described with respect to the measuring coordinate frame are established based on the product-of-exponential (POE) formula. In the first step the robot's base coordinate frame is calibrated by the unit quaternion form. The errors of both the robot's reference configuration and the base coordinate frame's pose are equivalently transformed to the zero-position errors of the robot's joints. The simplified model of the robot's positioning error is established in second-power explicit expressions. Then the identification model is finished by the least square method, requiring measuring position coordinates only. The complete subtasks of calibrating the robot's 39 kinematic parameters are finished in the second step. It's proved by a group of calibration experiments that by the proposed two-step calibration method the average absolute accuracy of industrial robots is updated to 0.23 mm. This paper presents that the robot's base frame should be calibrated before its kinematic parameters in order to upgrade its absolute positioning accuracy.

Surrogate Based Uni/Multi-Objective Optimization and Distribution Estimation Methods

NASA Astrophysics Data System (ADS)

Gong, W.; Duan, Q.; Huo, X.

2017-12-01

Parameter calibration has been demonstrated as an effective way to improve the performance of dynamic models, such as hydrological models, land surface models, weather and climate models etc. Traditional optimization algorithms usually cost a huge number of model evaluations, making dynamic model calibration very difficult, or even computationally prohibitive. With the help of a serious of recently developed adaptive surrogate-modelling based optimization methods: uni-objective optimization method ASMO, multi-objective optimization method MO-ASMO, and probability distribution estimation method ASMO-PODE, the number of model evaluations can be significantly reduced to several hundreds, making it possible to calibrate very expensive dynamic models, such as regional high resolution land surface models, weather forecast models such as WRF, and intermediate complexity earth system models such as LOVECLIM. This presentation provides a brief introduction to the common framework of adaptive surrogate-based optimization algorithms of ASMO, MO-ASMO and ASMO-PODE, a case study of Common Land Model (CoLM) calibration in Heihe river basin in Northwest China, and an outlook of the potential applications of the surrogate-based optimization methods.

Space-based infrared scanning sensor LOS determination and calibration using star observation

NASA Astrophysics Data System (ADS)

Chen, Jun; Xu, Zhan; An, Wei; Deng, Xin-Pu; Yang, Jun-Gang

2015-10-01

This paper provides a novel methodology for removing sensor bias from a space based infrared (IR) system (SBIRS) through the use of stars detected in the background field of the sensor. Space based IR system uses the LOS (line of sight) of target for target location. LOS determination and calibration is the key precondition of accurate location and tracking of targets in Space based IR system and the LOS calibration of scanning sensor is one of the difficulties. The subsequent changes of sensor bias are not been taking into account in the conventional LOS determination and calibration process. Based on the analysis of the imaging process of scanning sensor, a theoretical model based on the estimation of bias angles using star observation is proposed. By establishing the process model of the bias angles and the observation model of stars, using an extended Kalman filter (EKF) to estimate the bias angles, and then calibrating the sensor LOS. Time domain simulations results indicate that the proposed method has a high precision and smooth performance for sensor LOS determination and calibration. The timeliness and precision of target tracking process in the space based infrared (IR) tracking system could be met with the proposed algorithm.

Multi-site calibration, validation, and sensitivity analysis of the MIKE SHE Model for a large watershed in northern China

Treesearch

S. Wang; Z. Zhang; G. Sun; P. Strauss; J. Guo; Y. Tang; A. Yao

2012-01-01

Model calibration is essential for hydrologic modeling of large watersheds in a heterogeneous mountain environment. Little guidance is available for model calibration protocols for distributed models that aim at capturing the spatial variability of hydrologic processes. This study used the physically-based distributed hydrologic model, MIKE SHE, to contrast a lumped...

Bayesian Framework for Water Quality Model Uncertainty Estimation and Risk Management

EPA Science Inventory

A formal Bayesian methodology is presented for integrated model calibration and risk-based water quality management using Bayesian Monte Carlo simulation and maximum likelihood estimation (BMCML). The primary focus is on lucid integration of model calibration with risk-based wat...

The impact of modelling errors on interferometer calibration for 21 cm power spectra

NASA Astrophysics Data System (ADS)

Ewall-Wice, Aaron; Dillon, Joshua S.; Liu, Adrian; Hewitt, Jacqueline

2017-09-01

We study the impact of sky-based calibration errors from source mismodelling on 21 cm power spectrum measurements with an interferometer and propose a method for suppressing their effects. While emission from faint sources that are not accounted for in calibration catalogues is believed to be spectrally smooth, deviations of true visibilities from model visibilities are not, due to the inherent chromaticity of the interferometer's sky response (the 'wedge'). Thus, unmodelled foregrounds, below the confusion limit of many instruments, introduce frequency structure into gain solutions on the same line-of-sight scales on which we hope to observe the cosmological signal. We derive analytic expressions describing these errors using linearized approximations of the calibration equations and estimate the impact of this bias on measurements of the 21 cm power spectrum during the epoch of reionization. Given our current precision in primary beam and foreground modelling, this noise will significantly impact the sensitivity of existing experiments that rely on sky-based calibration. Our formalism describes the scaling of calibration with array and sky-model parameters and can be used to guide future instrument design and calibration strategy. We find that sky-based calibration that downweights long baselines can eliminate contamination in most of the region outside of the wedge with only a modest increase in instrumental noise.

Monte-Carlo-based uncertainty propagation with hierarchical models—a case study in dynamic torque

NASA Astrophysics Data System (ADS)

Klaus, Leonard; Eichstädt, Sascha

2018-04-01

For a dynamic calibration, a torque transducer is described by a mechanical model, and the corresponding model parameters are to be identified from measurement data. A measuring device for the primary calibration of dynamic torque, and a corresponding model-based calibration approach, have recently been developed at PTB. The complete mechanical model of the calibration set-up is very complex, and involves several calibration steps—making a straightforward implementation of a Monte Carlo uncertainty evaluation tedious. With this in mind, we here propose to separate the complete model into sub-models, with each sub-model being treated with individual experiments and analysis. The uncertainty evaluation for the overall model then has to combine the information from the sub-models in line with Supplement 2 of the Guide to the Expression of Uncertainty in Measurement. In this contribution, we demonstrate how to carry this out using the Monte Carlo method. The uncertainty evaluation involves various input quantities of different origin and the solution of a numerical optimisation problem.

Quantifying uncertainties in streamflow predictions through signature based inference of hydrological model parameters

NASA Astrophysics Data System (ADS)

Fenicia, Fabrizio; Reichert, Peter; Kavetski, Dmitri; Albert, Calro

2016-04-01

The calibration of hydrological models based on signatures (e.g. Flow Duration Curves - FDCs) is often advocated as an alternative to model calibration based on the full time series of system responses (e.g. hydrographs). Signature based calibration is motivated by various arguments. From a conceptual perspective, calibration on signatures is a way to filter out errors that are difficult to represent when calibrating on the full time series. Such errors may for example occur when observed and simulated hydrographs are shifted, either on the "time" axis (i.e. left or right), or on the "streamflow" axis (i.e. above or below). These shifts may be due to errors in the precipitation input (time or amount), and if not properly accounted in the likelihood function, may cause biased parameter estimates (e.g. estimated model parameters that do not reproduce the recession characteristics of a hydrograph). From a practical perspective, signature based calibration is seen as a possible solution for making predictions in ungauged basins. Where streamflow data are not available, it may in fact be possible to reliably estimate streamflow signatures. Previous research has for example shown how FDCs can be reliably estimated at ungauged locations based on climatic and physiographic influence factors. Typically, the goal of signature based calibration is not the prediction of the signatures themselves, but the prediction of the system responses. Ideally, the prediction of system responses should be accompanied by a reliable quantification of the associated uncertainties. Previous approaches for signature based calibration, however, do not allow reliable estimates of streamflow predictive distributions. Here, we illustrate how the Bayesian approach can be employed to obtain reliable streamflow predictive distributions based on signatures. A case study is presented, where a hydrological model is calibrated on FDCs and additional signatures. We propose an approach where the likelihood function for the signatures is derived from the likelihood for streamflow (rather than using an "ad-hoc" likelihood for the signatures as done in previous approaches). This likelihood is not easily tractable analytically and we therefore cannot apply "simple" MCMC methods. This numerical problem is solved using Approximate Bayesian Computation (ABC). Our result indicate that the proposed approach is suitable for producing reliable streamflow predictive distributions based on calibration to signature data. Moreover, our results provide indications on which signatures are more appropriate to represent the information content of the hydrograph.

Improving SWAT model prediction using an upgraded denitrification scheme and constrained auto calibration

USDA-ARS?s Scientific Manuscript database

The reliability of common calibration practices for process based water quality models has recently been questioned. A so-called “adequately calibrated model” may contain input errors not readily identifiable by model users, or may not realistically represent intra-watershed responses. These short...

A calibration hierarchy for risk models was defined: from utopia to empirical data.

PubMed

Van Calster, Ben; Nieboer, Daan; Vergouwe, Yvonne; De Cock, Bavo; Pencina, Michael J; Steyerberg, Ewout W

2016-06-01

Calibrated risk models are vital for valid decision support. We define four levels of calibration and describe implications for model development and external validation of predictions. We present results based on simulated data sets. A common definition of calibration is "having an event rate of R% among patients with a predicted risk of R%," which we refer to as "moderate calibration." Weaker forms of calibration only require the average predicted risk (mean calibration) or the average prediction effects (weak calibration) to be correct. "Strong calibration" requires that the event rate equals the predicted risk for every covariate pattern. This implies that the model is fully correct for the validation setting. We argue that this is unrealistic: the model type may be incorrect, the linear predictor is only asymptotically unbiased, and all nonlinear and interaction effects should be correctly modeled. In addition, we prove that moderate calibration guarantees nonharmful decision making. Finally, results indicate that a flexible assessment of calibration in small validation data sets is problematic. Strong calibration is desirable for individualized decision support but unrealistic and counter productive by stimulating the development of overly complex models. Model development and external validation should focus on moderate calibration. Copyright © 2016 Elsevier Inc. All rights reserved.

Sensitivity analysis and calibration of a dynamic physically based slope stability model

NASA Astrophysics Data System (ADS)

Zieher, Thomas; Rutzinger, Martin; Schneider-Muntau, Barbara; Perzl, Frank; Leidinger, David; Formayer, Herbert; Geitner, Clemens

2017-06-01

Physically based modelling of slope stability on a catchment scale is still a challenging task. When applying a physically based model on such a scale (1 : 10 000 to 1 : 50 000), parameters with a high impact on the model result should be calibrated to account for (i) the spatial variability of parameter values, (ii) shortcomings of the selected model, (iii) uncertainties of laboratory tests and field measurements or (iv) parameters that cannot be derived experimentally or measured in the field (e.g. calibration constants). While systematic parameter calibration is a common task in hydrological modelling, this is rarely done using physically based slope stability models. In the present study a dynamic, physically based, coupled hydrological-geomechanical slope stability model is calibrated based on a limited number of laboratory tests and a detailed multitemporal shallow landslide inventory covering two landslide-triggering rainfall events in the Laternser valley, Vorarlberg (Austria). Sensitive parameters are identified based on a local one-at-a-time sensitivity analysis. These parameters (hydraulic conductivity, specific storage, angle of internal friction for effective stress, cohesion for effective stress) are systematically sampled and calibrated for a landslide-triggering rainfall event in August 2005. The identified model ensemble, including 25 behavioural model runs with the highest portion of correctly predicted landslides and non-landslides, is then validated with another landslide-triggering rainfall event in May 1999. The identified model ensemble correctly predicts the location and the supposed triggering timing of 73.0 % of the observed landslides triggered in August 2005 and 91.5 % of the observed landslides triggered in May 1999. Results of the model ensemble driven with raised precipitation input reveal a slight increase in areas potentially affected by slope failure. At the same time, the peak run-off increases more markedly, suggesting that precipitation intensities during the investigated landslide-triggering rainfall events were already close to or above the soil's infiltration capacity.

FAST Model Calibration and Validation of the OC5-DeepCwind Floating Offshore Wind System Against Wave Tank Test Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wendt, Fabian F; Robertson, Amy N; Jonkman, Jason

During the course of the Offshore Code Comparison Collaboration, Continued, with Correlation (OC5) project, which focused on the validation of numerical methods through comparison against tank test data, the authors created a numerical FAST model of the 1:50-scale DeepCwind semisubmersible system that was tested at the Maritime Research Institute Netherlands ocean basin in 2013. This paper discusses several model calibration studies that were conducted to identify model adjustments that improve the agreement between the numerical simulations and the experimental test data. These calibration studies cover wind-field-specific parameters (coherence, turbulence), hydrodynamic and aerodynamic modeling approaches, as well as rotor model (blade-pitchmore » and blade-mass imbalances) and tower model (structural tower damping coefficient) adjustments. These calibration studies were conducted based on relatively simple calibration load cases (wave only/wind only). The agreement between the final FAST model and experimental measurements is then assessed based on more-complex combined wind and wave validation cases.« less

Calibrating cellular automaton models for pedestrians walking through corners

NASA Astrophysics Data System (ADS)

Dias, Charitha; Lovreglio, Ruggiero

2018-05-01

Cellular Automata (CA) based pedestrian simulation models have gained remarkable popularity as they are simpler and easier to implement compared to other microscopic modeling approaches. However, incorporating traditional floor field representations in CA models to simulate pedestrian corner navigation behavior could result in unrealistic behaviors. Even though several previous studies have attempted to enhance CA models to realistically simulate pedestrian maneuvers around bends, such modifications have not been calibrated or validated against empirical data. In this study, two static floor field (SFF) representations, namely 'discrete representation' and 'continuous representation', are calibrated for CA-models to represent pedestrians' walking behavior around 90° bends. Trajectory data collected through a controlled experiment are used to calibrate these model representations. Calibration results indicate that although both floor field representations can represent pedestrians' corner navigation behavior, the 'continuous' representation fits the data better. Output of this study could be beneficial for enhancing the reliability of existing CA-based models by representing pedestrians' corner navigation behaviors more realistically.

Gaussian process based modeling and experimental design for sensor calibration in drifting environments

PubMed Central

Geng, Zongyu; Yang, Feng; Chen, Xi; Wu, Nianqiang

2016-01-01

It remains a challenge to accurately calibrate a sensor subject to environmental drift. The calibration task for such a sensor is to quantify the relationship between the sensor’s response and its exposure condition, which is specified by not only the analyte concentration but also the environmental factors such as temperature and humidity. This work developed a Gaussian Process (GP)-based procedure for the efficient calibration of sensors in drifting environments. Adopted as the calibration model, GP is not only able to capture the possibly nonlinear relationship between the sensor responses and the various exposure-condition factors, but also able to provide valid statistical inference for uncertainty quantification of the target estimates (e.g., the estimated analyte concentration of an unknown environment). Built on GP’s inference ability, an experimental design method was developed to achieve efficient sampling of calibration data in a batch sequential manner. The resulting calibration procedure, which integrates the GP-based modeling and experimental design, was applied on a simulated chemiresistor sensor to demonstrate its effectiveness and its efficiency over the traditional method. PMID:26924894

The efficacy of calibrating hydrologic model using remotely sensed evapotranspiration and soil moisture for streamflow prediction

NASA Astrophysics Data System (ADS)

Kunnath-Poovakka, A.; Ryu, D.; Renzullo, L. J.; George, B.

2016-04-01

Calibration of spatially distributed hydrologic models is frequently limited by the availability of ground observations. Remotely sensed (RS) hydrologic information provides an alternative source of observations to inform models and extend modelling capability beyond the limits of ground observations. This study examines the capability of RS evapotranspiration (ET) and soil moisture (SM) in calibrating a hydrologic model and its efficacy to improve streamflow predictions. SM retrievals from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and daily ET estimates from the CSIRO MODIS ReScaled potential ET (CMRSET) are used to calibrate a simplified Australian Water Resource Assessment - Landscape model (AWRA-L) for a selection of parameters. The Shuffled Complex Evolution Uncertainty Algorithm (SCE-UA) is employed for parameter estimation at eleven catchments in eastern Australia. A subset of parameters for calibration is selected based on the variance-based Sobol' sensitivity analysis. The efficacy of 15 objective functions for calibration is assessed based on streamflow predictions relative to control cases, and relative merits of each are discussed. Synthetic experiments were conducted to examine the effect of bias in RS ET observations on calibration. The objective function containing the root mean square deviation (RMSD) of ET result in best streamflow predictions and the efficacy is superior for catchments with medium to high average runoff. Synthetic experiments revealed that accurate ET product can improve the streamflow predictions in catchments with low average runoff.

Coda Calibration Tool

DOE Office of Scientific and Technical Information (OSTI.GOV)

Addair, Travis; Barno, Justin; Dodge, Doug

CCT is a Java based application for calibrating 10 shear wave coda measurement models to observed data using a much smaller set of reference moment magnitudes (MWs) calculated from other means (waveform modeling, etc.). These calibrated measurement models can then be used in other tools to generate coda moment magnitude measurements, source spectra, estimated stress drop, and other useful measurements for any additional events and any new data collected in the calibrated region.

Research on orbit prediction for solar-based calibration proper satellite

NASA Astrophysics Data System (ADS)

Chen, Xuan; Qi, Wenwen; Xu, Peng

2018-03-01

Utilizing the mathematical model of the orbit mechanics, the orbit prediction is to forecast the space target's orbit information of a certain time based on the orbit of the initial moment. The proper satellite radiometric calibration and calibration orbit prediction process are introduced briefly. On the basis of the research of the calibration space position design method and the radiative transfer model, an orbit prediction method for proper satellite radiometric calibration is proposed to select the appropriate calibration arc for the remote sensor and to predict the orbit information of the proper satellite and the remote sensor. By analyzing the orbit constraint of the proper satellite calibration, the GF-1solar synchronous orbit is chose as the proper satellite orbit in order to simulate the calibration visible durance for different satellites to be calibrated. The results of simulation and analysis provide the basis for the improvement of the radiometric calibration accuracy of the satellite remote sensor, which lays the foundation for the high precision and high frequency radiometric calibration.

Low Frequency Error Analysis and Calibration for High-Resolution Optical Satellite's Uncontrolled Geometric Positioning

NASA Astrophysics Data System (ADS)

Wang, Mi; Fang, Chengcheng; Yang, Bo; Cheng, Yufeng

2016-06-01

The low frequency error is a key factor which has affected uncontrolled geometry processing accuracy of the high-resolution optical image. To guarantee the geometric quality of imagery, this paper presents an on-orbit calibration method for the low frequency error based on geometric calibration field. Firstly, we introduce the overall flow of low frequency error on-orbit analysis and calibration, which includes optical axis angle variation detection of star sensor, relative calibration among star sensors, multi-star sensor information fusion, low frequency error model construction and verification. Secondly, we use optical axis angle change detection method to analyze the law of low frequency error variation. Thirdly, we respectively use the method of relative calibration and information fusion among star sensors to realize the datum unity and high precision attitude output. Finally, we realize the low frequency error model construction and optimal estimation of model parameters based on DEM/DOM of geometric calibration field. To evaluate the performance of the proposed calibration method, a certain type satellite's real data is used. Test results demonstrate that the calibration model in this paper can well describe the law of the low frequency error variation. The uncontrolled geometric positioning accuracy of the high-resolution optical image in the WGS-84 Coordinate Systems is obviously improved after the step-wise calibration.

A methodology for reduced order modeling and calibration of the upper atmosphere

NASA Astrophysics Data System (ADS)

Mehta, Piyush M.; Linares, Richard

2017-10-01

Atmospheric drag is the largest source of uncertainty in accurately predicting the orbit of satellites in low Earth orbit (LEO). Accurately predicting drag for objects that traverse LEO is critical to space situational awareness. Atmospheric models used for orbital drag calculations can be characterized either as empirical or physics-based (first principles based). Empirical models are fast to evaluate but offer limited real-time predictive/forecasting ability, while physics based models offer greater predictive/forecasting ability but require dedicated parallel computational resources. Also, calibration with accurate data is required for either type of models. This paper presents a new methodology based on proper orthogonal decomposition toward development of a quasi-physical, predictive, reduced order model that combines the speed of empirical and the predictive/forecasting capabilities of physics-based models. The methodology is developed to reduce the high dimensionality of physics-based models while maintaining its capabilities. We develop the methodology using the Naval Research Lab's Mass Spectrometer Incoherent Scatter model and show that the diurnal and seasonal variations can be captured using a small number of modes and parameters. We also present calibration of the reduced order model using the CHAMP and GRACE accelerometer-derived densities. Results show that the method performs well for modeling and calibration of the upper atmosphere.

Evaluation of calibration efficacy under different levels of uncertainty

DOE PAGES

Heo, Yeonsook; Graziano, Diane J.; Guzowski, Leah; ...

2014-06-10

This study examines how calibration performs under different levels of uncertainty in model input data. It specifically assesses the efficacy of Bayesian calibration to enhance the reliability of EnergyPlus model predictions. A Bayesian approach can be used to update uncertain values of parameters, given measured energy-use data, and to quantify the associated uncertainty.We assess the efficacy of Bayesian calibration under a controlled virtual-reality setup, which enables rigorous validation of the accuracy of calibration results in terms of both calibrated parameter values and model predictions. Case studies demonstrate the performance of Bayesian calibration of base models developed from audit data withmore » differing levels of detail in building design, usage, and operation.« less

Performance of the air2stream model that relates air and stream water temperatures depends on the calibration method

NASA Astrophysics Data System (ADS)

Piotrowski, Adam P.; Napiorkowski, Jaroslaw J.

2018-06-01

A number of physical or data-driven models have been proposed to evaluate stream water temperatures based on hydrological and meteorological observations. However, physical models require a large amount of information that is frequently unavailable, while data-based models ignore the physical processes. Recently the air2stream model has been proposed as an intermediate alternative that is based on physical heat budget processes, but it is so simplified that the model may be applied like data-driven ones. However, the price for simplicity is the need to calibrate eight parameters that, although have some physical meaning, cannot be measured or evaluated a priori. As a result, applicability and performance of the air2stream model for a particular stream relies on the efficiency of the calibration method. The original air2stream model uses an inefficient 20-year old approach called Particle Swarm Optimization with inertia weight. This study aims at finding an effective and robust calibration method for the air2stream model. Twelve different optimization algorithms are examined on six different streams from northern USA (states of Washington, Oregon and New York), Poland and Switzerland, located in both high mountains, hilly and lowland areas. It is found that the performance of the air2stream model depends significantly on the calibration method. Two algorithms lead to the best results for each considered stream. The air2stream model, calibrated with the chosen optimization methods, performs favorably against classical streamwater temperature models. The MATLAB code of the air2stream model and the chosen calibration procedure (CoBiDE) are available as Supplementary Material on the Journal of Hydrology web page.

Improving a complex finite-difference ground water flow model through the use of an analytic element screening model

USGS Publications Warehouse

Hunt, R.J.; Anderson, M.P.; Kelson, V.A.

1998-01-01

This paper demonstrates that analytic element models have potential as powerful screening tools that can facilitate or improve calibration of more complicated finite-difference and finite-element models. We demonstrate how a two-dimensional analytic element model was used to identify errors in a complex three-dimensional finite-difference model caused by incorrect specification of boundary conditions. An improved finite-difference model was developed using boundary conditions developed from a far-field analytic element model. Calibration of a revised finite-difference model was achieved using fewer zones of hydraulic conductivity and lake bed conductance than the original finite-difference model. Calibration statistics were also improved in that simulated base-flows were much closer to measured values. The improved calibration is due mainly to improved specification of the boundary conditions made possible by first solving the far-field problem with an analytic element model.This paper demonstrates that analytic element models have potential as powerful screening tools that can facilitate or improve calibration of more complicated finite-difference and finite-element models. We demonstrate how a two-dimensional analytic element model was used to identify errors in a complex three-dimensional finite-difference model caused by incorrect specification of boundary conditions. An improved finite-difference model was developed using boundary conditions developed from a far-field analytic element model. Calibration of a revised finite-difference model was achieved using fewer zones of hydraulic conductivity and lake bed conductance than the original finite-difference model. Calibration statistics were also improved in that simulated base-flows were much closer to measured values. The improved calibration is due mainly to improved specification of the boundary conditions made possible by first solving the far-field problem with an analytic element model.

Effects of temporal and spatial resolution of calibration data on integrated hydrologic water quality model identification

NASA Astrophysics Data System (ADS)

Jiang, Sanyuan; Jomaa, Seifeddine; Büttner, Olaf; Rode, Michael

2014-05-01

Hydrological water quality modeling is increasingly used for investigating runoff and nutrient transport processes as well as watershed management but it is mostly unclear how data availablity determins model identification. In this study, the HYPE (HYdrological Predictions for the Environment) model, which is a process-based, semi-distributed hydrological water quality model, was applied in two different mesoscale catchments (Selke (463 km2) and Weida (99 km2)) located in central Germany to simulate discharge and inorganic nitrogen (IN) transport. PEST and DREAM(ZS) were combined with the HYPE model to conduct parameter calibration and uncertainty analysis. Split-sample test was used for model calibration (1994-1999) and validation (1999-2004). IN concentration and daily IN load were found to be highly correlated with discharge, indicating that IN leaching is mainly controlled by runoff. Both dynamics and balances of water and IN load were well captured with NSE greater than 0.83 during validation period. Multi-objective calibration (calibrating hydrological and water quality parameters simultaneously) was found to outperform step-wise calibration in terms of model robustness. Multi-site calibration was able to improve model performance at internal sites, decrease parameter posterior uncertainty and prediction uncertainty. Nitrogen-process parameters calibrated using continuous daily averages of nitrate-N concentration observations produced better and more robust simulations of IN concentration and load, lower posterior parameter uncertainty and IN concentration prediction uncertainty compared to the calibration against uncontinuous biweekly nitrate-N concentration measurements. Both PEST and DREAM(ZS) are efficient in parameter calibration. However, DREAM(ZS) is more sound in terms of parameter identification and uncertainty analysis than PEST because of its capability to evolve parameter posterior distributions and estimate prediction uncertainty based on global search and Bayesian inference schemes.

Role of Imaging Specrometer Data for Model-based Cross-calibration of Imaging Sensors

NASA Technical Reports Server (NTRS)

Thome, Kurtis John

2014-01-01

Site characterization benefits from imaging spectrometry to determine spectral bi-directional reflectance of a well-understood surface. Cross calibration approaches, uncertainties, role of imaging spectrometry, model-based site characterization, and application to product validation.

Financial model calibration using consistency hints.

PubMed

Abu-Mostafa, Y S

2001-01-01

We introduce a technique for forcing the calibration of a financial model to produce valid parameters. The technique is based on learning from hints. It converts simple curve fitting into genuine calibration, where broad conclusions can be inferred from parameter values. The technique augments the error function of curve fitting with consistency hint error functions based on the Kullback-Leibler distance. We introduce an efficient EM-type optimization algorithm tailored to this technique. We also introduce other consistency hints, and balance their weights using canonical errors. We calibrate the correlated multifactor Vasicek model of interest rates, and apply it successfully to Japanese Yen swaps market and US dollar yield market.

Linking Statistically- and Physically-Based Models for Improved Streamflow Simulation in Gaged and Ungaged Areas

NASA Astrophysics Data System (ADS)

Lafontaine, J.; Hay, L.; Archfield, S. A.; Farmer, W. H.; Kiang, J. E.

2014-12-01

The U.S. Geological Survey (USGS) has developed a National Hydrologic Model (NHM) to support coordinated, comprehensive and consistent hydrologic model development, and facilitate the application of hydrologic simulations within the continental US. The portion of the NHM located within the Gulf Coastal Plains and Ozarks Landscape Conservation Cooperative (GCPO LCC) is being used to test the feasibility of improving streamflow simulations in gaged and ungaged watersheds by linking statistically- and physically-based hydrologic models. The GCPO LCC covers part or all of 12 states and 5 sub-geographies, totaling approximately 726,000 km2, and is centered on the lower Mississippi Alluvial Valley. A total of 346 USGS streamgages in the GCPO LCC region were selected to evaluate the performance of this new calibration methodology for the period 1980 to 2013. Initially, the physically-based models are calibrated to measured streamflow data to provide a baseline for comparison. An enhanced calibration procedure then is used to calibrate the physically-based models in the gaged and ungaged areas of the GCPO LCC using statistically-based estimates of streamflow. For this application, the calibration procedure is adjusted to address the limitations of the statistically generated time series to reproduce measured streamflow in gaged basins, primarily by incorporating error and bias estimates. As part of this effort, estimates of uncertainty in the model simulations are also computed for the gaged and ungaged watersheds.

DEM Calibration Approach: design of experiment

NASA Astrophysics Data System (ADS)

Boikov, A. V.; Savelev, R. V.; Payor, V. A.

2018-05-01

The problem of DEM models calibration is considered in the article. It is proposed to divide models input parameters into those that require iterative calibration and those that are recommended to measure directly. A new method for model calibration based on the design of the experiment for iteratively calibrated parameters is proposed. The experiment is conducted using a specially designed stand. The results are processed with technical vision algorithms. Approximating functions are obtained and the error of the implemented software and hardware complex is estimated. The prospects of the obtained results are discussed.

Evaluation of Automated Model Calibration Techniques for Residential Building Energy Simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Robertson, J.; Polly, B.; Collis, J.

2013-09-01

This simulation study adapts and applies the general framework described in BESTEST-EX (Judkoff et al 2010) for self-testing residential building energy model calibration methods. BEopt/DOE-2.2 is used to evaluate four mathematical calibration methods in the context of monthly, daily, and hourly synthetic utility data for a 1960's-era existing home in a cooling-dominated climate. The home's model inputs are assigned probability distributions representing uncertainty ranges, random selections are made from the uncertainty ranges to define 'explicit' input values, and synthetic utility billing data are generated using the explicit input values. The four calibration methods evaluated in this study are: an ASHRAEmore » 1051-RP-based approach (Reddy and Maor 2006), a simplified simulated annealing optimization approach, a regression metamodeling optimization approach, and a simple output ratio calibration approach. The calibration methods are evaluated for monthly, daily, and hourly cases; various retrofit measures are applied to the calibrated models and the methods are evaluated based on the accuracy of predicted savings, computational cost, repeatability, automation, and ease of implementation.« less

Evaluation of Automated Model Calibration Techniques for Residential Building Energy Simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

and Ben Polly, Joseph Robertson; Polly, Ben; Collis, Jon

2013-09-01

This simulation study adapts and applies the general framework described in BESTEST-EX (Judkoff et al 2010) for self-testing residential building energy model calibration methods. BEopt/DOE-2.2 is used to evaluate four mathematical calibration methods in the context of monthly, daily, and hourly synthetic utility data for a 1960's-era existing home in a cooling-dominated climate. The home's model inputs are assigned probability distributions representing uncertainty ranges, random selections are made from the uncertainty ranges to define "explicit" input values, and synthetic utility billing data are generated using the explicit input values. The four calibration methods evaluated in this study are: an ASHRAEmore » 1051-RP-based approach (Reddy and Maor 2006), a simplified simulated annealing optimization approach, a regression metamodeling optimization approach, and a simple output ratio calibration approach. The calibration methods are evaluated for monthly, daily, and hourly cases; various retrofit measures are applied to the calibrated models and the methods are evaluated based on the accuracy of predicted savings, computational cost, repeatability, automation, and ease of implementation.« less

Photogrammetric Modeling and Image-Based Rendering for Rapid Virtual Environment Creation

DTIC Science & Technology

2004-12-01

area and different methods have been proposed. Pertinent methods include: Camera Calibration , Structure from Motion, Stereo Correspondence, and Image...Based Rendering 1.1.1 Camera Calibration Determining the 3D structure of a model from multiple views becomes simpler if the intrinsic (or internal...can introduce significant nonlinearities into the image. We have found that camera calibration is a straightforward process which can simplify the

Model calibration of a variable refrigerant flow system with a dedicated outdoor air system: A case study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Dongsu; Cox, Sam J.; Cho, Heejin

With increased use of variable refrigerant flow (VRF) systems in the U.S. building sector, interests in capability and rationality of various building energy modeling tools to simulate VRF systems are rising. This paper presents the detailed procedures for model calibration of a VRF system with a dedicated outdoor air system (DOAS) by comparing to detailed measured data from an occupancy emulated small office building. The building energy model is first developed based on as-built drawings, and building and system characteristics available. The whole building energy modeling tool used for the study is U.S. DOE’s EnergyPlus version 8.1. The initial modelmore » is, then, calibrated with the hourly measured data from the target building and VRF-DOAS system. In a detailed calibration procedures of the VRF-DOAS, the original EnergyPlus source code is modified to enable the modeling of the specific VRF-DOAS installed in the building. After a proper calibration during cooling and heating seasons, the VRF-DOAS model can reasonably predict the performance of the actual VRF-DOAS system based on the criteria from ASHRAE Guideline 14-2014. The calibration results show that hourly CV-RMSE and NMBE would be 15.7% and 3.8%, respectively, which is deemed to be calibrated. As a result, the whole-building energy usage after calibration of the VRF-DOAS model is 1.9% (78.8 kWh) lower than that of the measurements during comparison period.« less

Improving Hydrological Simulations by Incorporating GRACE Data for Parameter Calibration

NASA Astrophysics Data System (ADS)

Bai, P.

2017-12-01

Hydrological model parameters are commonly calibrated by observed streamflow data. This calibration strategy is questioned when the modeled hydrological variables of interest are not limited to streamflow. Well-performed streamflow simulations do not guarantee the reliable reproduction of other hydrological variables. One of the reasons is that hydrological model parameters are not reasonably identified. The Gravity Recovery and Climate Experiment (GRACE) satellite-derived total water storage change (TWSC) data provide an opportunity to constrain hydrological model parameterizations in combination with streamflow observations. We constructed a multi-objective calibration scheme based on GRACE-derived TWSC and streamflow observations, with the aim of improving the parameterizations of hydrological models. The multi-objective calibration scheme was compared with the traditional single-objective calibration scheme, which is based only on streamflow observations. Two monthly hydrological models were employed on 22 Chinese catchments with different hydroclimatic conditions. The model evaluation was performed using observed streamflows, GRACE-derived TWSC, and evapotranspiraiton (ET) estimates from flux towers and from the water balance approach. Results showed that the multi-objective calibration provided more reliable TWSC and ET simulations without significant deterioration in the accuracy of streamflow simulations than the single-objective calibration. In addition, the improvements of TWSC and ET simulations were more significant in relatively dry catchments than in relatively wet catchments. This study highlights the importance of including additional constraints besides streamflow observations in the parameter estimation to improve the performances of hydrological models.

Sensitivity analysis, calibration, and testing of a distributed hydrological model using error‐based weighting and one objective function

USGS Publications Warehouse

Foglia, L.; Hill, Mary C.; Mehl, Steffen W.; Burlando, P.

2009-01-01

We evaluate the utility of three interrelated means of using data to calibrate the fully distributed rainfall‐runoff model TOPKAPI as applied to the Maggia Valley drainage area in Switzerland. The use of error‐based weighting of observation and prior information data, local sensitivity analysis, and single‐objective function nonlinear regression provides quantitative evaluation of sensitivity of the 35 model parameters to the data, identification of data types most important to the calibration, and identification of correlations among parameters that contribute to nonuniqueness. Sensitivity analysis required only 71 model runs, and regression required about 50 model runs. The approach presented appears to be ideal for evaluation of models with long run times or as a preliminary step to more computationally demanding methods. The statistics used include composite scaled sensitivities, parameter correlation coefficients, leverage, Cook's D, and DFBETAS. Tests suggest predictive ability of the calibrated model typical of hydrologic models.

New error calibration tests for gravity models using subset solutions and independent data - Applied to GEM-T3

NASA Technical Reports Server (NTRS)

Lerch, F. J.; Nerem, R. S.; Chinn, D. S.; Chan, J. C.; Patel, G. B.; Klosko, S. M.

1993-01-01

A new method has been developed to provide a direct test of the error calibrations of gravity models based on actual satellite observations. The basic approach projects the error estimates of the gravity model parameters onto satellite observations, and the results of these projections are then compared with data residual computed from the orbital fits. To allow specific testing of the gravity error calibrations, subset solutions are computed based on the data set and data weighting of the gravity model. The approach is demonstrated using GEM-T3 to show that the gravity error estimates are well calibrated and that reliable predictions of orbit accuracies can be achieved for independent orbits.

Regional estimation of response routine parameters

NASA Astrophysics Data System (ADS)

Tøfte, Lena S.

2015-04-01

Reducing the number of calibration parameters is of a considerable advantage when area distributed hydrological models are to be calibrated, both due to equifinality and over-parameterization of the model in general, and for making the calibration process more efficient. A simple non-threshold response model for drainage in natural catchments based on among others Kirchner's article in WRR 2009 is implemented in the gridded hydrological model in the ENKI framework. This response model takes only the hydrogram into account; it has one state and two parameters, and is adapted to catchments that are dominated by terrain drainage. In former analyses of natural discharge series from a large number of catchments in different regions of Norway, we found that these response model parameters can be calculated from some known catchment characteristics, as catchment area and lake percentage, found in maps or data bases, meaning that the parameters can easily be found also for ungauged catchments. In the presented work from the EU project COMPLEX a large region in Mid-Norway containing 27 simulated catchments of different sizes and characteristics is calibrated. Results from two different calibration strategies are compared: 1) removing the response parameters from the calibration by calculating them in advance, based on the results from our former studies, and 2) including the response parameters in the calibration, both as maps with different values for each catchment, and as a constant number for the total region. The resulting simulation performances are compared and discussed.

An Improved Interferometric Calibration Method Based on Independent Parameter Decomposition

NASA Astrophysics Data System (ADS)

Fan, J.; Zuo, X.; Li, T.; Chen, Q.; Geng, X.

2018-04-01

Interferometric SAR is sensitive to earth surface undulation. The accuracy of interferometric parameters plays a significant role in precise digital elevation model (DEM). The interferometric calibration is to obtain high-precision global DEM by calculating the interferometric parameters using ground control points (GCPs). However, interferometric parameters are always calculated jointly, making them difficult to decompose precisely. In this paper, we propose an interferometric calibration method based on independent parameter decomposition (IPD). Firstly, the parameters related to the interferometric SAR measurement are determined based on the three-dimensional reconstruction model. Secondly, the sensitivity of interferometric parameters is quantitatively analyzed after the geometric parameters are completely decomposed. Finally, each interferometric parameter is calculated based on IPD and interferometric calibration model is established. We take Weinan of Shanxi province as an example and choose 4 TerraDEM-X image pairs to carry out interferometric calibration experiment. The results show that the elevation accuracy of all SAR images is better than 2.54 m after interferometric calibration. Furthermore, the proposed method can obtain the accuracy of DEM products better than 2.43 m in the flat area and 6.97 m in the mountainous area, which can prove the correctness and effectiveness of the proposed IPD based interferometric calibration method. The results provide a technical basis for topographic mapping of 1 : 50000 and even larger scale in the flat area and mountainous area.

On Inertial Body Tracking in the Presence of Model Calibration Errors

PubMed Central

Miezal, Markus; Taetz, Bertram; Bleser, Gabriele

2016-01-01

In inertial body tracking, the human body is commonly represented as a biomechanical model consisting of rigid segments with known lengths and connecting joints. The model state is then estimated via sensor fusion methods based on data from attached inertial measurement units (IMUs). This requires the relative poses of the IMUs w.r.t. the segments—the IMU-to-segment calibrations, subsequently called I2S calibrations—to be known. Since calibration methods based on static poses, movements and manual measurements are still the most widely used, potentially large human-induced calibration errors have to be expected. This work compares three newly developed/adapted extended Kalman filter (EKF) and optimization-based sensor fusion methods with an existing EKF-based method w.r.t. their segment orientation estimation accuracy in the presence of model calibration errors with and without using magnetometer information. While the existing EKF-based method uses a segment-centered kinematic chain biomechanical model and a constant angular acceleration motion model, the newly developed/adapted methods are all based on a free segments model, where each segment is represented with six degrees of freedom in the global frame. Moreover, these methods differ in the assumed motion model (constant angular acceleration, constant angular velocity, inertial data as control input), the state representation (segment-centered, IMU-centered) and the estimation method (EKF, sliding window optimization). In addition to the free segments representation, the optimization-based method also represents each IMU with six degrees of freedom in the global frame. In the evaluation on simulated and real data from a three segment model (an arm), the optimization-based method showed the smallest mean errors, standard deviations and maximum errors throughout all tests. It also showed the lowest dependency on magnetometer information and motion agility. Moreover, it was insensitive w.r.t. I2S position and segment length errors in the tested ranges. Errors in the I2S orientations were, however, linearly propagated into the estimated segment orientations. In the absence of magnetic disturbances, severe model calibration errors and fast motion changes, the newly developed IMU centered EKF-based method yielded comparable results with lower computational complexity. PMID:27455266

Augmented classical least squares multivariate spectral analysis

DOEpatents

Haaland, David M.; Melgaard, David K.

2004-02-03

A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

Augmented Classical Least Squares Multivariate Spectral Analysis

DOEpatents

Haaland, David M.; Melgaard, David K.

2005-07-26

A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

Augmented Classical Least Squares Multivariate Spectral Analysis

DOEpatents

Haaland, David M.; Melgaard, David K.

2005-01-11

A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

Model Robust Calibration: Method and Application to Electronically-Scanned Pressure Transducers

NASA Technical Reports Server (NTRS)

Walker, Eric L.; Starnes, B. Alden; Birch, Jeffery B.; Mays, James E.

2010-01-01

This article presents the application of a recently developed statistical regression method to the controlled instrument calibration problem. The statistical method of Model Robust Regression (MRR), developed by Mays, Birch, and Starnes, is shown to improve instrument calibration by reducing the reliance of the calibration on a predetermined parametric (e.g. polynomial, exponential, logarithmic) model. This is accomplished by allowing fits from the predetermined parametric model to be augmented by a certain portion of a fit to the residuals from the initial regression using a nonparametric (locally parametric) regression technique. The method is demonstrated for the absolute scale calibration of silicon-based pressure transducers.

FAST Model Calibration and Validation of the OC5- DeepCwind Floating Offshore Wind System Against Wave Tank Test Data: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wendt, Fabian F; Robertson, Amy N; Jonkman, Jason

During the course of the Offshore Code Comparison Collaboration, Continued, with Correlation (OC5) project, which focused on the validation of numerical methods through comparison against tank test data, the authors created a numerical FAST model of the 1:50-scale DeepCwind semisubmersible system that was tested at the Maritime Research Institute Netherlands ocean basin in 2013. This paper discusses several model calibration studies that were conducted to identify model adjustments that improve the agreement between the numerical simulations and the experimental test data. These calibration studies cover wind-field-specific parameters (coherence, turbulence), hydrodynamic and aerodynamic modeling approaches, as well as rotor model (blade-pitchmore » and blade-mass imbalances) and tower model (structural tower damping coefficient) adjustments. These calibration studies were conducted based on relatively simple calibration load cases (wave only/wind only). The agreement between the final FAST model and experimental measurements is then assessed based on more-complex combined wind and wave validation cases.« less

Streamflow characteristics from modelled runoff time series: Importance of calibration criteria selection

USGS Publications Warehouse

Poole, Sandra; Vis, Marc; Knight, Rodney; Seibert, Jan

2017-01-01

Ecologically relevant streamflow characteristics (SFCs) of ungauged catchments are often estimated from simulated runoff of hydrologic models that were originally calibrated on gauged catchments. However, SFC estimates of the gauged donor catchments and subsequently the ungauged catchments can be substantially uncertain when models are calibrated using traditional approaches based on optimization of statistical performance metrics (e.g., Nash–Sutcliffe model efficiency). An improved calibration strategy for gauged catchments is therefore crucial to help reduce the uncertainties of estimated SFCs for ungauged catchments. The aim of this study was to improve SFC estimates from modeled runoff time series in gauged catchments by explicitly including one or several SFCs in the calibration process. Different types of objective functions were defined consisting of the Nash–Sutcliffe model efficiency, single SFCs, or combinations thereof. We calibrated a bucket-type runoff model (HBV – Hydrologiska Byråns Vattenavdelning – model) for 25 catchments in the Tennessee River basin and evaluated the proposed calibration approach on 13 ecologically relevant SFCs representing major flow regime components and different flow conditions. While the model generally tended to underestimate the tested SFCs related to mean and high-flow conditions, SFCs related to low flow were generally overestimated. The highest estimation accuracies were achieved by a SFC-specific model calibration. Estimates of SFCs not included in the calibration process were of similar quality when comparing a multi-SFC calibration approach to a traditional model efficiency calibration. For practical applications, this implies that SFCs should preferably be estimated from targeted runoff model calibration, and modeled estimates need to be carefully interpreted.

Reducing equifinality of hydrological models by integrating Functional Streamflow Disaggregation

NASA Astrophysics Data System (ADS)

Lüdtke, Stefan; Apel, Heiko; Nied, Manuela; Carl, Peter; Merz, Bruno

2014-05-01

A universal problem of the calibration of hydrological models is the equifinality of different parameter sets derived from the calibration of models against total runoff values. This is an intrinsic problem stemming from the quality of the calibration data and the simplified process representation by the model. However, discharge data contains additional information which can be extracted by signal processing methods. An analysis specifically developed for the disaggregation of runoff time series into flow components is the Functional Streamflow Disaggregation (FSD; Carl & Behrendt, 2008). This method is used in the calibration of an implementation of the hydrological model SWIM in a medium sized watershed in Thailand. FSD is applied to disaggregate the discharge time series into three flow components which are interpreted as base flow, inter-flow and surface runoff. In addition to total runoff, the model is calibrated against these three components in a modified GLUE analysis, with the aim to identify structural model deficiencies, assess the internal process representation and to tackle equifinality. We developed a model dependent (MDA) approach calibrating the model runoff components against the FSD components, and a model independent (MIA) approach comparing the FSD of the model results and the FSD of calibration data. The results indicate, that the decomposition provides valuable information for the calibration. Particularly MDA highlights and discards a number of standard GLUE behavioural models underestimating the contribution of soil water to river discharge. Both, MDA and MIA yield to a reduction of the parameter ranges by a factor up to 3 in comparison to standard GLUE. Based on these results, we conclude that the developed calibration approach is able to reduce the equifinality of hydrological model parameterizations. The effect on the uncertainty of the model predictions is strongest by applying MDA and shows only minor reductions for MIA. Besides further validation of FSD, the next steps include an extension of the study to different catchments and other hydrological models with a similar structure.

Efficient calibration for imperfect computer models

DOE PAGES

Tuo, Rui; Wu, C. F. Jeff

2015-12-01

Many computer models contain unknown parameters which need to be estimated using physical observations. Furthermore, the calibration method based on Gaussian process models may lead to unreasonable estimate for imperfect computer models. In this work, we extend their study to calibration problems with stochastic physical data. We propose a novel method, called the L 2 calibration, and show its semiparametric efficiency. The conventional method of the ordinary least squares is also studied. Theoretical analysis shows that it is consistent but not efficient. Here, numerical examples show that the proposed method outperforms the existing ones.

A critical comparison of systematic calibration protocols for activated sludge models: a SWOT analysis.

PubMed

Sin, Gürkan; Van Hulle, Stijn W H; De Pauw, Dirk J W; van Griensven, Ann; Vanrolleghem, Peter A

2005-07-01

Modelling activated sludge systems has gained an increasing momentum after the introduction of activated sludge models (ASMs) in 1987. Application of dynamic models for full-scale systems requires essentially a calibration of the chosen ASM to the case under study. Numerous full-scale model applications have been performed so far which were mostly based on ad hoc approaches and expert knowledge. Further, each modelling study has followed a different calibration approach: e.g. different influent wastewater characterization methods, different kinetic parameter estimation methods, different selection of parameters to be calibrated, different priorities within the calibration steps, etc. In short, there was no standard approach in performing the calibration study, which makes it difficult, if not impossible, to (1) compare different calibrations of ASMs with each other and (2) perform internal quality checks for each calibration study. To address these concerns, systematic calibration protocols have recently been proposed to bring guidance to the modeling of activated sludge systems and in particular to the calibration of full-scale models. In this contribution four existing calibration approaches (BIOMATH, HSG, STOWA and WERF) will be critically discussed using a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis. It will also be assessed in what way these approaches can be further developed in view of further improving the quality of ASM calibration. In this respect, the potential of automating some steps of the calibration procedure by use of mathematical algorithms is highlighted.

A Bayesian procedure for evaluating the frequency of calibration factor updates in highway safety manual (HSM) applications.

PubMed

Saha, Dibakar; Alluri, Priyanka; Gan, Albert

2017-01-01

The Highway Safety Manual (HSM) presents statistical models to quantitatively estimate an agency's safety performance. The models were developed using data from only a few U.S. states. To account for the effects of the local attributes and temporal factors on crash occurrence, agencies are required to calibrate the HSM-default models for crash predictions. The manual suggests updating calibration factors every two to three years, or preferably on an annual basis. Given that the calibration process involves substantial time, effort, and resources, a comprehensive analysis of the required calibration factor update frequency is valuable to the agencies. Accordingly, the objective of this study is to evaluate the HSM's recommendation and determine the required frequency of calibration factor updates. A robust Bayesian estimation procedure is used to assess the variation between calibration factors computed annually, biennially, and triennially using data collected from over 2400 miles of segments and over 700 intersections on urban and suburban facilities in Florida. Bayesian model yields a posterior distribution of the model parameters that give credible information to infer whether the difference between calibration factors computed at specified intervals is credibly different from the null value which represents unaltered calibration factors between the comparison years or in other words, zero difference. The concept of the null value is extended to include the range of values that are practically equivalent to zero. Bayesian inference shows that calibration factors based on total crash frequency are required to be updated every two years in cases where the variations between calibration factors are not greater than 0.01. When the variations are between 0.01 and 0.05, calibration factors based on total crash frequency could be updated every three years. Copyright © 2016 Elsevier Ltd. All rights reserved.

Accurate evaluation of sensitivity for calibration between a LiDAR and a panoramic camera used for remote sensing

NASA Astrophysics Data System (ADS)

García-Moreno, Angel-Iván; González-Barbosa, José-Joel; Ramírez-Pedraza, Alfonso; Hurtado-Ramos, Juan B.; Ornelas-Rodriguez, Francisco-Javier

2016-04-01

Computer-based reconstruction models can be used to approximate urban environments. These models are usually based on several mathematical approximations and the usage of different sensors, which implies dependency on many variables. The sensitivity analysis presented in this paper is used to weigh the relative importance of each uncertainty contributor into the calibration of a panoramic camera-LiDAR system. Both sensors are used for three-dimensional urban reconstruction. Simulated and experimental tests were conducted. For the simulated tests we analyze and compare the calibration parameters using the Monte Carlo and Latin hypercube sampling techniques. Sensitivity analysis for each variable involved into the calibration was computed by the Sobol method, which is based on the analysis of the variance breakdown, and the Fourier amplitude sensitivity test method, which is based on Fourier's analysis. Sensitivity analysis is an essential tool in simulation modeling and for performing error propagation assessments.

Calibration to improve forward model simulation of microwave emissivity at GPM frequencies over the U.S. Southern Great Plains

PubMed Central

Harrison, Kenneth W.; Tian, Yudong; Peters-Lidard, Christa D.; Ringerud, Sarah; Kumar, Sujay V.

2018-01-01

Better estimation of land surface microwave emissivity promises to improve over-land precipitation retrievals in the GPM era. Forward models of land microwave emissivity are available but have suffered from poor parameter specification and limited testing. Here, forward models are calibrated and the accompanying change in predictive power is evaluated. With inputs (e.g., soil moisture) from the Noah land surface model and applying MODIS LAI data, two microwave emissivity models are tested, the Community Radiative Transfer Model (CRTM) and Community Microwave Emission Model (CMEM). The calibration is conducted with the NASA Land Information System (LIS) parameter estimation subsystem using AMSR-E based emissivity retrievals for the calibration dataset. The extent of agreement between the modeled and retrieved estimates is evaluated using the AMSR-E retrievals for a separate 7-year validation period. Results indicate that calibration can significantly improve the agreement, simulating emissivity with an across-channel average root-mean-square-difference (RMSD) of about 0.013, or about 20% lower than if relying on daily estimates based on climatology. The results also indicate that calibration of the microwave emissivity model alone, as was done in prior studies, results in as much as 12% higher across-channel average RMSD, as compared to joint calibration of the land surface and microwave emissivity models. It remains as future work to assess the extent to which the improvements in emissivity estimation translate into improvements in precipitation retrieval accuracy. PMID:29795962

Connections between survey calibration estimators and semiparametric models for incomplete data

PubMed Central

Lumley, Thomas; Shaw, Pamela A.; Dai, James Y.

2012-01-01

Survey calibration (or generalized raking) estimators are a standard approach to the use of auxiliary information in survey sampling, improving on the simple Horvitz–Thompson estimator. In this paper we relate the survey calibration estimators to the semiparametric incomplete-data estimators of Robins and coworkers, and to adjustment for baseline variables in a randomized trial. The development based on calibration estimators explains the ‘estimated weights’ paradox and provides useful heuristics for constructing practical estimators. We present some examples of using calibration to gain precision without making additional modelling assumptions in a variety of regression models. PMID:23833390

Highly parameterized model calibration with cloud computing: an example of regional flow model calibration in northeast Alberta, Canada

NASA Astrophysics Data System (ADS)

Hayley, Kevin; Schumacher, J.; MacMillan, G. J.; Boutin, L. C.

2014-05-01

Expanding groundwater datasets collected by automated sensors, and improved groundwater databases, have caused a rapid increase in calibration data available for groundwater modeling projects. Improved methods of subsurface characterization have increased the need for model complexity to represent geological and hydrogeological interpretations. The larger calibration datasets and the need for meaningful predictive uncertainty analysis have both increased the degree of parameterization necessary during model calibration. Due to these competing demands, modern groundwater modeling efforts require a massive degree of parallelization in order to remain computationally tractable. A methodology for the calibration of highly parameterized, computationally expensive models using the Amazon EC2 cloud computing service is presented. The calibration of a regional-scale model of groundwater flow in Alberta, Canada, is provided as an example. The model covers a 30,865-km2 domain and includes 28 hydrostratigraphic units. Aquifer properties were calibrated to more than 1,500 static hydraulic head measurements and 10 years of measurements during industrial groundwater use. Three regionally extensive aquifers were parameterized (with spatially variable hydraulic conductivity fields), as was the aerial recharge boundary condition, leading to 450 adjustable parameters in total. The PEST-based model calibration was parallelized on up to 250 computing nodes located on Amazon's EC2 servers.

Calibration of an Unsteady Groundwater Flow Model for a Complex, Strongly Heterogeneous Aquifer

NASA Astrophysics Data System (ADS)

Curtis, Z. K.; Liao, H.; Li, S. G.; Phanikumar, M. S.; Lusch, D.

2016-12-01

Modeling of groundwater systems characterized by complex three-dimensional structure and heterogeneity remains a significant challenge. Most of today's groundwater models are developed based on relatively simple conceptual representations in favor of model calibratibility. As more complexities are modeled, e.g., by adding more layers and/or zones, or introducing transient processes, more parameters have to be estimated and issues related to ill-posed groundwater problems and non-unique calibration arise. Here, we explore the use of an alternative conceptual representation for groundwater modeling that is fully three-dimensional and can capture complex 3D heterogeneity (both systematic and "random") without over-parameterizing the aquifer system. In particular, we apply Transition Probability (TP) geostatistics on high resolution borehole data from a water well database to characterize the complex 3D geology. Different aquifer material classes, e.g., `AQ' (aquifer material), `MAQ' (marginal aquifer material'), `PCM' (partially confining material), and `CM' (confining material), are simulated, with the hydraulic properties of each material type as tuning parameters during calibration. The TP-based approach is applied to simulate unsteady groundwater flow in a large, complex, and strongly heterogeneous glacial aquifer system in Michigan across multiple spatial and temporal scales. The resulting model is calibrated to observed static water level data over a time span of 50 years. The results show that the TP-based conceptualization enables much more accurate and robust calibration/simulation than that based on conventional deterministic layer/zone based conceptual representations.

Calibration of the ARID robot

NASA Technical Reports Server (NTRS)

Doty, Keith L

1992-01-01

The author has formulated a new, general model for specifying the kinematic properties of serial manipulators. The new model kinematic parameters do not suffer discontinuities when nominally parallel adjacent axes deviate from exact parallelism. From this new theory the author develops a first-order, lumped-parameter, calibration-model for the ARID manipulator. Next, the author develops a calibration methodology for the ARID based on visual and acoustic sensing. A sensor platform, consisting of a camera and four sonars attached to the ARID end frame, performs calibration measurements. A calibration measurement consists of processing one visual frame of an accurately placed calibration image and recording four acoustic range measurements. A minimum of two measurement protocols determine the kinematics calibration-model of the ARID for a particular region: assuming the joint displacements are accurately measured, the calibration surface is planar, and the kinematic parameters do not vary rapidly in the region. No theoretical or practical limitations appear to contra-indicate the feasibility of the calibration method developed here.

Calibration and LOD/LOQ estimation of a chemiluminescent hybridization assay for residual DNA in recombinant protein drugs expressed in E. coli using a four-parameter logistic model.

PubMed

Lee, K R; Dipaolo, B; Ji, X

2000-06-01

Calibration is the process of fitting a model based on reference data points (x, y), then using the model to estimate an unknown x based on a new measured response, y. In DNA assay, x is the concentration, and y is the measured signal volume. A four-parameter logistic model was used frequently for calibration of immunoassay when the response is optical density for enzyme-linked immunosorbent assay (ELISA) or adjusted radioactivity count for radioimmunoassay (RIA). Here, it is shown that the same model or a linearized version of the curve are equally useful for the calibration of a chemiluminescent hybridization assay for residual DNA in recombinant protein drugs and calculation of performance measures of the assay.

An adaptive modeling and simulation environment for combined-cycle data reconciliation and degradation estimation

NASA Astrophysics Data System (ADS)

Lin, Tsungpo

Performance engineers face the major challenge in modeling and simulation for the after-market power system due to system degradation and measurement errors. Currently, the majority in power generation industries utilizes the deterministic data matching method to calibrate the model and cascade system degradation, which causes significant calibration uncertainty and also the risk of providing performance guarantees. In this research work, a maximum-likelihood based simultaneous data reconciliation and model calibration (SDRMC) is used for power system modeling and simulation. By replacing the current deterministic data matching with SDRMC one can reduce the calibration uncertainty and mitigate the error propagation to the performance simulation. A modeling and simulation environment for a complex power system with certain degradation has been developed. In this environment multiple data sets are imported when carrying out simultaneous data reconciliation and model calibration. Calibration uncertainties are estimated through error analyses and populated to performance simulation by using principle of error propagation. System degradation is then quantified by performance comparison between the calibrated model and its expected new & clean status. To mitigate smearing effects caused by gross errors, gross error detection (GED) is carried out in two stages. The first stage is a screening stage, in which serious gross errors are eliminated in advance. The GED techniques used in the screening stage are based on multivariate data analysis (MDA), including multivariate data visualization and principal component analysis (PCA). Subtle gross errors are treated at the second stage, in which the serial bias compensation or robust M-estimator is engaged. To achieve a better efficiency in the combined scheme of the least squares based data reconciliation and the GED technique based on hypotheses testing, the Levenberg-Marquardt (LM) algorithm is utilized as the optimizer. To reduce the computation time and stabilize the problem solving for a complex power system such as a combined cycle power plant, meta-modeling using the response surface equation (RSE) and system/process decomposition are incorporated with the simultaneous scheme of SDRMC. The goal of this research work is to reduce the calibration uncertainties and, thus, the risks of providing performance guarantees arisen from uncertainties in performance simulation.

Metric Calibration of a Focused Plenoptic Camera Based on a 3d Calibration Target

NASA Astrophysics Data System (ADS)

Zeller, N.; Noury, C. A.; Quint, F.; Teulière, C.; Stilla, U.; Dhome, M.

2016-06-01

In this paper we present a new calibration approach for focused plenoptic cameras. We derive a new mathematical projection model of a focused plenoptic camera which considers lateral as well as depth distortion. Therefore, we derive a new depth distortion model directly from the theory of depth estimation in a focused plenoptic camera. In total the model consists of five intrinsic parameters, the parameters for radial and tangential distortion in the image plane and two new depth distortion parameters. In the proposed calibration we perform a complete bundle adjustment based on a 3D calibration target. The residual of our optimization approach is three dimensional, where the depth residual is defined by a scaled version of the inverse virtual depth difference and thus conforms well to the measured data. Our method is evaluated based on different camera setups and shows good accuracy. For a better characterization of our approach we evaluate the accuracy of virtual image points projected back to 3D space.

Research on camera on orbit radial calibration based on black body and infrared calibration stars

NASA Astrophysics Data System (ADS)

Wang, YuDu; Su, XiaoFeng; Zhang, WanYing; Chen, FanSheng

2018-05-01

Affected by launching process and space environment, the response capability of a space camera must be attenuated. So it is necessary for a space camera to have a spaceborne radiant calibration. In this paper, we propose a method of calibration based on accurate Infrared standard stars was proposed for increasing infrared radiation measurement precision. As stars can be considered as a point target, we use them as the radiometric calibration source and establish the Taylor expansion method and the energy extrapolation model based on WISE catalog and 2MASS catalog. Then we update the calibration results from black body. Finally, calibration mechanism is designed and the technology of design is verified by on orbit test. The experimental calibration result shows the irradiance extrapolation error is about 3% and the accuracy of calibration methods is about 10%, the results show that the methods could satisfy requirements of on orbit calibration.

Solid laboratory calibration of a nonimaging spectroradiometer.

PubMed

Schaepman, M E; Dangel, S

2000-07-20

Field-based nonimaging spectroradiometers are often used in vicarious calibration experiments for airborne or spaceborne imaging spectrometers. The calibration uncertainties associated with these ground measurements contribute substantially to the overall modeling error in radiance- or reflectance-based vicarious calibration experiments. Because of limitations in the radiometric stability of compact field spectroradiometers, vicarious calibration experiments are based primarily on reflectance measurements rather than on radiance measurements. To characterize the overall uncertainty of radiance-based approaches and assess the sources of uncertainty, we carried out a full laboratory calibration. This laboratory calibration of a nonimaging spectroradiometer is based on a measurement plan targeted at achieving a

Parameter Estimation of Computationally Expensive Watershed Models Through Efficient Multi-objective Optimization and Interactive Decision Analytics

NASA Astrophysics Data System (ADS)

Akhtar, Taimoor; Shoemaker, Christine

2016-04-01

Watershed model calibration is inherently a multi-criteria problem. Conflicting trade-offs exist between different quantifiable calibration criterions indicating the non-existence of a single optimal parameterization. Hence, many experts prefer a manual approach to calibration where the inherent multi-objective nature of the calibration problem is addressed through an interactive, subjective, time-intensive and complex decision making process. Multi-objective optimization can be used to efficiently identify multiple plausible calibration alternatives and assist calibration experts during the parameter estimation process. However, there are key challenges to the use of multi objective optimization in the parameter estimation process which include: 1) multi-objective optimization usually requires many model simulations, which is difficult for complex simulation models that are computationally expensive; and 2) selection of one from numerous calibration alternatives provided by multi-objective optimization is non-trivial. This study proposes a "Hybrid Automatic Manual Strategy" (HAMS) for watershed model calibration to specifically address the above-mentioned challenges. HAMS employs a 3-stage framework for parameter estimation. Stage 1 incorporates the use of an efficient surrogate multi-objective algorithm, GOMORS, for identification of numerous calibration alternatives within a limited simulation evaluation budget. The novelty of HAMS is embedded in Stages 2 and 3 where an interactive visual and metric based analytics framework is available as a decision support tool to choose a single calibration from the numerous alternatives identified in Stage 1. Stage 2 of HAMS provides a goodness-of-fit measure / metric based interactive framework for identification of a small subset (typically less than 10) of meaningful and diverse set of calibration alternatives from the numerous alternatives obtained in Stage 1. Stage 3 incorporates the use of an interactive visual analytics framework for decision support in selection of one parameter combination from the alternatives identified in Stage 2. HAMS is applied for calibration of flow parameters of a SWAT model, (Soil and Water Assessment Tool) designed to simulate flow in the Cannonsville watershed in upstate New York. Results from the application of HAMS to Cannonsville indicate that efficient multi-objective optimization and interactive visual and metric based analytics can bridge the gap between the effective use of both automatic and manual strategies for parameter estimation of computationally expensive watershed models.

A New Calibration Method for Commercial RGB-D Sensors.

PubMed

Darwish, Walid; Tang, Shenjun; Li, Wenbin; Chen, Wu

2017-05-24

Commercial RGB-D sensors such as Kinect and Structure Sensors have been widely used in the game industry, where geometric fidelity is not of utmost importance. For applications in which high quality 3D is required, i.e., 3D building models of centimeter‑level accuracy, accurate and reliable calibrations of these sensors are required. This paper presents a new model for calibrating the depth measurements of RGB-D sensors based on the structured light concept. Additionally, a new automatic method is proposed for the calibration of all RGB-D parameters, including internal calibration parameters for all cameras, the baseline between the infrared and RGB cameras, and the depth error model. When compared with traditional calibration methods, this new model shows a significant improvement in depth precision for both near and far ranges.

Generic Raman-based calibration models enabling real-time monitoring of cell culture bioreactors.

PubMed

Mehdizadeh, Hamidreza; Lauri, David; Karry, Krizia M; Moshgbar, Mojgan; Procopio-Melino, Renee; Drapeau, Denis

2015-01-01

Raman-based multivariate calibration models have been developed for real-time in situ monitoring of multiple process parameters within cell culture bioreactors. Developed models are generic, in the sense that they are applicable to various products, media, and cell lines based on Chinese Hamster Ovarian (CHO) host cells, and are scalable to large pilot and manufacturing scales. Several batches using different CHO-based cell lines and corresponding proprietary media and process conditions have been used to generate calibration datasets, and models have been validated using independent datasets from separate batch runs. All models have been validated to be generic and capable of predicting process parameters with acceptable accuracy. The developed models allow monitoring multiple key bioprocess metabolic variables, and hence can be utilized as an important enabling tool for Quality by Design approaches which are strongly supported by the U.S. Food and Drug Administration. © 2015 American Institute of Chemical Engineers.

Accounting for Test Variability through Sizing Local Domains in Sequential Design Optimization with Concurrent Calibration-Based Model Validation

DTIC Science & Technology

2013-08-01

in Sequential Design Optimization with Concurrent Calibration-Based Model Validation Dorin Drignei 1 Mathematics and Statistics Department...Validation 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Dorin Drignei; Zissimos Mourelatos; Vijitashwa Pandey

A Novel Error Model of Optical Systems and an On-Orbit Calibration Method for Star Sensors.

PubMed

Wang, Shuang; Geng, Yunhai; Jin, Rongyu

2015-12-12

In order to improve the on-orbit measurement accuracy of star sensors, the effects of image-plane rotary error, image-plane tilt error and distortions of optical systems resulting from the on-orbit thermal environment were studied in this paper. Since these issues will affect the precision of star image point positions, in this paper, a novel measurement error model based on the traditional error model is explored. Due to the orthonormal characteristics of image-plane rotary-tilt errors and the strong nonlinearity among these error parameters, it is difficult to calibrate all the parameters simultaneously. To solve this difficulty, for the new error model, a modified two-step calibration method based on the Extended Kalman Filter (EKF) and Least Square Methods (LSM) is presented. The former one is used to calibrate the main point drift, focal length error and distortions of optical systems while the latter estimates the image-plane rotary-tilt errors. With this calibration method, the precision of star image point position influenced by the above errors is greatly improved from 15.42% to 1.389%. Finally, the simulation results demonstrate that the presented measurement error model for star sensors has higher precision. Moreover, the proposed two-step method can effectively calibrate model error parameters, and the calibration precision of on-orbit star sensors is also improved obviously.

Multi-objective Calibration of DHSVM Based on Hydrologic Key Elements in Jinhua River Basin, East China

NASA Astrophysics Data System (ADS)

Pan, S.; Liu, L.; Xu, Y. P.

2017-12-01

Abstract: In physically based distributed hydrological model, large number of parameters, representing spatial heterogeneity of watershed and various processes in hydrologic cycle, are involved. For lack of calibration module in Distributed Hydrology Soil Vegetation Model, this study developed a multi-objective calibration module using Epsilon-Dominance Non-Dominated Sorted Genetic Algorithm II (ɛ-NSGAII) and based on parallel computing of Linux cluster for DHSVM (ɛP-DHSVM). In this study, two hydrologic key elements (i.e., runoff and evapotranspiration) are used as objectives in multi-objective calibration of model. MODIS evapotranspiration obtained by SEBAL is adopted to fill the gap of lack of observation for evapotranspiration. The results show that good performance of runoff simulation in single objective calibration cannot ensure good simulation performance of other hydrologic key elements. Self-developed ɛP-DHSVM model can make multi-objective calibration more efficiently and effectively. The running speed can be increased by more than 20-30 times via applying ɛP-DHSVM. In addition, runoff and evapotranspiration can be simulated very well simultaneously by ɛP-DHSVM, with superior values for two efficiency coefficients (0.74 for NS of runoff and 0.79 for NS of evapotranspiration, -10.5% and -8.6% for PBIAS of runoff and evapotranspiration respectively).

Hierarchical calibration and validation for modeling bench-scale solvent-based carbon capture. Part 1: Non-reactive physical mass transfer across the wetted wall column: Original Research Article: Hierarchical calibration and validation for modeling bench-scale solvent-based carbon capture

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Chao; Xu, Zhijie; Lai, Canhai

A hierarchical model calibration and validation is proposed for quantifying the confidence level of mass transfer prediction using a computational fluid dynamics (CFD) model, where the solvent-based carbon dioxide (CO2) capture is simulated and simulation results are compared to the parallel bench-scale experimental data. Two unit problems with increasing level of complexity are proposed to breakdown the complex physical/chemical processes of solvent-based CO2 capture into relatively simpler problems to separate the effects of physical transport and chemical reaction. This paper focuses on the calibration and validation of the first unit problem, i.e. the CO2 mass transfer across a falling ethanolaminemore » (MEA) film in absence of chemical reaction. This problem is investigated both experimentally and numerically using nitrous oxide (N2O) as a surrogate for CO2. To capture the motion of gas-liquid interface, a volume of fluid method is employed together with a one-fluid formulation to compute the mass transfer between the two phases. Bench-scale parallel experiments are designed and conducted to validate and calibrate the CFD models using a general Bayesian calibration. Two important transport parameters, e.g. Henry’s constant and gas diffusivity, are calibrated to produce the posterior distributions, which will be used as the input for the second unit problem to address the chemical adsorption of CO2 across the MEA falling film, where both mass transfer and chemical reaction are involved.« less

Evaluation of chiller modeling approaches and their usability for fault detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sreedharan, Priya

Selecting the model is an important and essential step in model based fault detection and diagnosis (FDD). Several factors must be considered in model evaluation, including accuracy, training data requirements, calibration effort, generality, and computational requirements. All modeling approaches fall somewhere between pure first-principles models, and empirical models. The objective of this study was to evaluate different modeling approaches for their applicability to model based FDD of vapor compression air conditioning units, which are commonly known as chillers. Three different models were studied: two are based on first-principles and the third is empirical in nature. The first-principles models are themore » Gordon and Ng Universal Chiller model (2nd generation), and a modified version of the ASHRAE Primary Toolkit model, which are both based on first principles. The DOE-2 chiller model as implemented in CoolTools{trademark} was selected for the empirical category. The models were compared in terms of their ability to reproduce the observed performance of an older chiller operating in a commercial building, and a newer chiller in a laboratory. The DOE-2 and Gordon-Ng models were calibrated by linear regression, while a direct-search method was used to calibrate the Toolkit model. The ''CoolTools'' package contains a library of calibrated DOE-2 curves for a variety of different chillers, and was used to calibrate the building chiller to the DOE-2 model. All three models displayed similar levels of accuracy. Of the first principles models, the Gordon-Ng model has the advantage of being linear in the parameters, which allows more robust parameter estimation methods to be used and facilitates estimation of the uncertainty in the parameter values. The ASHRAE Toolkit Model may have advantages when refrigerant temperature measurements are also available. The DOE-2 model can be expected to have advantages when very limited data are available to calibrate the model, as long as one of the previously identified models in the CoolTools library matches the performance of the chiller in question.« less

Decomposition of the Mean Squared Error and NSE Performance Criteria: Implications for Improving Hydrological Modelling

NASA Technical Reports Server (NTRS)

Gupta, Hoshin V.; Kling, Harald; Yilmaz, Koray K.; Martinez-Baquero, Guillermo F.

2009-01-01

The mean squared error (MSE) and the related normalization, the Nash-Sutcliffe efficiency (NSE), are the two criteria most widely used for calibration and evaluation of hydrological models with observed data. Here, we present a diagnostically interesting decomposition of NSE (and hence MSE), which facilitates analysis of the relative importance of its different components in the context of hydrological modelling, and show how model calibration problems can arise due to interactions among these components. The analysis is illustrated by calibrating a simple conceptual precipitation-runoff model to daily data for a number of Austrian basins having a broad range of hydro-meteorological characteristics. Evaluation of the results clearly demonstrates the problems that can be associated with any calibration based on the NSE (or MSE) criterion. While we propose and test an alternative criterion that can help to reduce model calibration problems, the primary purpose of this study is not to present an improved measure of model performance. Instead, we seek to show that there are systematic problems inherent with any optimization based on formulations related to the MSE. The analysis and results have implications to the manner in which we calibrate and evaluate environmental models; we discuss these and suggest possible ways forward that may move us towards an improved and diagnostically meaningful approach to model performance evaluation and identification.

Integrating satellite actual evapotranspiration patterns into distributed model parametrization and evaluation for a mesoscale catchment

NASA Astrophysics Data System (ADS)

Demirel, M. C.; Mai, J.; Stisen, S.; Mendiguren González, G.; Koch, J.; Samaniego, L. E.

2016-12-01

Distributed hydrologic models are traditionally calibrated and evaluated against observations of streamflow. Spatially distributed remote sensing observations offer a great opportunity to enhance spatial model calibration schemes. For that it is important to identify the model parameters that can change spatial patterns before the satellite based hydrologic model calibration. Our study is based on two main pillars: first we use spatial sensitivity analysis to identify the key parameters controlling the spatial distribution of actual evapotranspiration (AET). Second, we investigate the potential benefits of incorporating spatial patterns from MODIS data to calibrate the mesoscale Hydrologic Model (mHM). This distributed model is selected as it allows for a change in the spatial distribution of key soil parameters through the calibration of pedo-transfer function parameters and includes options for using fully distributed daily Leaf Area Index (LAI) directly as input. In addition the simulated AET can be estimated at the spatial resolution suitable for comparison to the spatial patterns observed using MODIS data. We introduce a new dynamic scaling function employing remotely sensed vegetation to downscale coarse reference evapotranspiration. In total, 17 parameters of 47 mHM parameters are identified using both sequential screening and Latin hypercube one-at-a-time sampling methods. The spatial patterns are found to be sensitive to the vegetation parameters whereas streamflow dynamics are sensitive to the PTF parameters. The results of multi-objective model calibration show that calibration of mHM against observed streamflow does not reduce the spatial errors in AET while they improve only the streamflow simulations. We will further examine the results of model calibration using only multi spatial objective functions measuring the association between observed AET and simulated AET maps and another case including spatial and streamflow metrics together.

Spatial calibration and temporal validation of flow for regional scale hydrologic modeling

USDA-ARS?s Scientific Manuscript database

Physically based regional scale hydrologic modeling is gaining importance for planning and management of water resources. Calibration and validation of such regional scale model is necessary before applying it for scenario assessment. However, in most regional scale hydrologic modeling, flow validat...

Demonstration of emulator-based Bayesian calibration of safety analysis codes: Theory and formulation

DOE PAGES

Yurko, Joseph P.; Buongiorno, Jacopo; Youngblood, Robert

2015-05-28

System codes for simulation of safety performance of nuclear plants may contain parameters whose values are not known very accurately. New information from tests or operating experience is incorporated into safety codes by a process known as calibration, which reduces uncertainty in the output of the code and thereby improves its support for decision-making. The work reported here implements several improvements on classic calibration techniques afforded by modern analysis techniques. The key innovation has come from development of code surrogate model (or code emulator) construction and prediction algorithms. Use of a fast emulator makes the calibration processes used here withmore » Markov Chain Monte Carlo (MCMC) sampling feasible. This study uses Gaussian Process (GP) based emulators, which have been used previously to emulate computer codes in the nuclear field. The present work describes the formulation of an emulator that incorporates GPs into a factor analysis-type or pattern recognition-type model. This “function factorization” Gaussian Process (FFGP) model allows overcoming limitations present in standard GP emulators, thereby improving both accuracy and speed of the emulator-based calibration process. Calibration of a friction-factor example using a Method of Manufactured Solution is performed to illustrate key properties of the FFGP based process.« less

Simulation and sensitivity analysis of carbon storage and fluxes in the New Jersey Pinelands

Treesearch

Zewei Miao; Richard G. Lathrop; Ming Xu; Inga P. La Puma; Kenneth L. Clark; John Hom; Nicholas Skowronski; Steve Van Tuyl

2011-01-01

A major challenge in modeling the carbon dynamics of vegetation communities is the proper parameterization and calibration of eco-physiological variables that are critical determinants of the ecosystem process-based model behavior. In this study, we improved and calibrated a biochemical process-based WxBGC model by using in situ AmeriFlux eddy covariance tower...

Calibration and analysis of genome-based models for microbial ecology.

PubMed

Louca, Stilianos; Doebeli, Michael

2015-10-16

Microbial ecosystem modeling is complicated by the large number of unknown parameters and the lack of appropriate calibration tools. Here we present a novel computational framework for modeling microbial ecosystems, which combines genome-based model construction with statistical analysis and calibration to experimental data. Using this framework, we examined the dynamics of a community of Escherichia coli strains that emerged in laboratory evolution experiments, during which an ancestral strain diversified into two coexisting ecotypes. We constructed a microbial community model comprising the ancestral and the evolved strains, which we calibrated using separate monoculture experiments. Simulations reproduced the successional dynamics in the evolution experiments, and pathway activation patterns observed in microarray transcript profiles. Our approach yielded detailed insights into the metabolic processes that drove bacterial diversification, involving acetate cross-feeding and competition for organic carbon and oxygen. Our framework provides a missing link towards a data-driven mechanistic microbial ecology.

Scalable methodology for large scale building energy improvement: Relevance of calibration in model-based retrofit analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heo, Yeonsook; Augenbroe, Godfried; Graziano, Diane

2015-05-01

The increasing interest in retrofitting of existing buildings is motivated by the need to make a major contribution to enhancing building energy efficiency and reducing energy consumption and CO2 emission by the built environment. This paper examines the relevance of calibration in model-based analysis to support decision-making for energy and carbon efficiency retrofits of individual buildings and portfolios of buildings. The authors formulate a set of real retrofit decision-making situations and evaluate the role of calibration by using a case study that compares predictions and decisions from an uncalibrated model with those of a calibrated model. The case study illustratesmore » both the mechanics and outcomes of a practical alternative to the expert- and time-intense application of dynamic energy simulation models for large-scale retrofit decision-making under uncertainty.« less

Stepwise calibration procedure for regional coupled hydrological-hydrogeological models

NASA Astrophysics Data System (ADS)

Labarthe, Baptiste; Abasq, Lena; de Fouquet, Chantal; Flipo, Nicolas

2014-05-01

Stream-aquifer interaction is a complex process depending on regional and local processes. Indeed, the groundwater component of hydrosystem and large scale heterogeneities control the regional flows towards the alluvial plains and the rivers. In second instance, the local distribution of the stream bed permeabilities controls the dynamics of stream-aquifer water fluxes within the alluvial plain, and therefore the near-river piezometric head distribution. In order to better understand the water circulation and pollutant transport in watersheds, the integration of these multi-dimensional processes in modelling platform has to be performed. Thus, the nested interfaces concept in continental hydrosystem modelling (where regional fluxes, simulated by large scale models, are imposed at local stream-aquifer interfaces) has been presented in Flipo et al (2014). This concept has been implemented in EauDyssée modelling platform for a large alluvial plain model (900km2) part of a 11000km2 multi-layer aquifer system, located in the Seine basin (France). The hydrosystem modelling platform is composed of four spatially distributed modules (Surface, Sub-surface, River and Groundwater), corresponding to four components of the terrestrial water cycle. Considering the large number of parameters to be inferred simultaneously, the calibration process of coupled models is highly computationally demanding and therefore hardly applicable to a real case study of 10000km2. In order to improve the efficiency of the calibration process, a stepwise calibration procedure is proposed. The stepwise methodology involves determining optimal parameters of all components of the coupled model, to provide a near optimum prior information for the global calibration. It starts with the surface component parameters calibration. The surface parameters are optimised based on the comparison between simulated and observed discharges (or filtered discharges) at various locations. Once the surface parameters have been determined, the groundwater component is calibrated. The calibration procedure is performed under steady state hypothesis (to minimize the procedure time length) using recharge rates given by the surface component calibration and imposed fluxes boundary conditions given by the regional model. The calibration is performed using pilot point where the prior variogram is calculated from observed transmissivities values. This procedure uses PEST (http//:www.pesthomepage.org/Home.php) as the inverse modelling tool and EauDyssée as the direct model. During the stepwise calibration process, each modules, even if they are actually dependant from each other, are run and calibrated independently, therefore contributions between each module have to be determined. For the surface module, groundwater and runoff contributions have been determined by hydrograph separation. Among the automated base-flow separation methods, the one-parameter Chapman filter (Chapman et al 1999) has been chosen. This filter is a decomposition of the actual base-flow between the previous base-flow and the discharge gradient weighted by functions of the recession coefficient. For the groundwater module, the recharge has been determined from surface and sub-surface module. References : Flipo, N., A. Mourhi, B. Labarthe, and S. Biancamaria (2014). Continental hydrosystem modelling : the concept of nested stream-aquifer interfaces. Hydrol. Earth Syst. Sci. Discuss. 11, 451-500. Chapman,TG. (1999). A comparison of algorithms for stream flow recession and base-flow separation. hydrological Processes 13, 701-714.

Features calibration of the dynamic force transducers

NASA Astrophysics Data System (ADS)

Sc., M. Yu Prilepko D.; Lysenko, V. G.

2018-04-01

The article discusses calibration methods of dynamic forces measuring instruments. The relevance of work is dictated by need to valid definition of the dynamic forces transducers metrological characteristics taking into account their intended application. The aim of this work is choice justification of calibration method, which provides the definition dynamic forces transducers metrological characteristics under simulation operating conditions for determining suitability for using in accordance with its purpose. The following tasks are solved: the mathematical model and the main measurements equation of calibration dynamic forces transducers by load weight, the main budget uncertainty components of calibration are defined. The new method of dynamic forces transducers calibration with use the reference converter “force-deformation” based on the calibrated elastic element and measurement of his deformation by a laser interferometer is offered. The mathematical model and the main measurements equation of the offered method is constructed. It is shown that use of calibration method based on measurements by the laser interferometer of calibrated elastic element deformations allows to exclude or to considerably reduce the uncertainty budget components inherent to method of load weight.

Modeling and Calibration of a Novel One-Mirror Galvanometric Laser Scanner

PubMed Central

Yu, Chengyi; Chen, Xiaobo; Xi, Juntong

2017-01-01

A laser stripe sensor has limited application when a point cloud of geometric samples on the surface of the object needs to be collected, so a galvanometric laser scanner is designed by using a one-mirror galvanometer element as its mechanical device to drive the laser stripe to sweep along the object. A novel mathematical model is derived for the proposed galvanometer laser scanner without any position assumptions and then a model-driven calibration procedure is proposed. Compared with available model-driven approaches, the influence of machining and assembly errors is considered in the proposed model. Meanwhile, a plane-constraint-based approach is proposed to extract a large number of calibration points effectively and accurately to calibrate the galvanometric laser scanner. Repeatability and accuracy of the galvanometric laser scanner are evaluated on the automobile production line to verify the efficiency and accuracy of the proposed calibration method. Experimental results show that the proposed calibration approach yields similar measurement performance compared with a look-up table calibration method. PMID:28098844

Simulating soil moisture change in a semiarid rangeland watershed with a process-based water-balance model

Treesearch

Howard Evan Canfield; Vicente L. Lopes

2000-01-01

A process-based, simulation model for evaporation, soil water and streamflow (BROOK903) was used to estimate soil moisture change on a semiarid rangeland watershed in southeastern Arizona. A sensitivity analysis was performed to select parameters affecting ET and soil moisture for calibration. Automatic parameter calibration was performed using a procedure based on a...

Calibration of a distributed hydrologic model for six European catchments using remote sensing data

NASA Astrophysics Data System (ADS)

Stisen, S.; Demirel, M. C.; Mendiguren González, G.; Kumar, R.; Rakovec, O.; Samaniego, L. E.

2017-12-01

While observed streamflow has been the single reference for most conventional hydrologic model calibration exercises, the availability of spatially distributed remote sensing observations provide new possibilities for multi-variable calibration assessing both spatial and temporal variability of different hydrologic processes. In this study, we first identify the key transfer parameters of the mesoscale Hydrologic Model (mHM) controlling both the discharge and the spatial distribution of actual evapotranspiration (AET) across six central European catchments (Elbe, Main, Meuse, Moselle, Neckar and Vienne). These catchments are selected based on their limited topographical and climatic variability which enables to evaluate the effect of spatial parameterization on the simulated evapotranspiration patterns. We develop a European scale remote sensing based actual evapotranspiration dataset at a 1 km grid scale driven primarily by land surface temperature observations from MODIS using the TSEB approach. Using the observed AET maps we analyze the potential benefits of incorporating spatial patterns from MODIS data to calibrate the mHM model. This model allows calibrating one-basin-at-a-time or all-basins-together using its unique structure and multi-parameter regionalization approach. Results will indicate any tradeoffs between spatial pattern and discharge simulation during model calibration and through validation against independent internal discharge locations. Moreover, added value on internal water balances will be analyzed.

Stormwater quality modelling in combined sewers: calibration and uncertainty analysis.

PubMed

Kanso, A; Chebbo, G; Tassin, B

2005-01-01

Estimating the level of uncertainty in urban stormwater quality models is vital for their utilization. This paper presents the results of application of a Monte Carlo Markov Chain method based on the Bayesian theory for the calibration and uncertainty analysis of a storm water quality model commonly used in available software. The tested model uses a hydrologic/hydrodynamic scheme to estimate the accumulation, the erosion and the transport of pollutants on surfaces and in sewers. It was calibrated for four different initial conditions of in-sewer deposits. Calibration results showed large variability in the model's responses in function of the initial conditions. They demonstrated that the model's predictive capacity is very low.

A New Calibration Method for Commercial RGB-D Sensors

PubMed Central

Darwish, Walid; Tang, Shenjun; Li, Wenbin; Chen, Wu

2017-01-01

Commercial RGB-D sensors such as Kinect and Structure Sensors have been widely used in the game industry, where geometric fidelity is not of utmost importance. For applications in which high quality 3D is required, i.e., 3D building models of centimeter-level accuracy, accurate and reliable calibrations of these sensors are required. This paper presents a new model for calibrating the depth measurements of RGB-D sensors based on the structured light concept. Additionally, a new automatic method is proposed for the calibration of all RGB-D parameters, including internal calibration parameters for all cameras, the baseline between the infrared and RGB cameras, and the depth error model. When compared with traditional calibration methods, this new model shows a significant improvement in depth precision for both near and far ranges. PMID:28538695

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kao, Kuo-Hsing; Meyer, Kristin De; Department of Electrical Engineering, KU Leuven, Leuven

Band-to-band tunneling parameters of strained indirect bandgap materials are not well-known, hampering the reliability of performance predictions of tunneling devices based on these materials. The nonlocal band-to-band tunneling model for compressively strained SiGe is calibrated based on a comparison of strained SiGe p-i-n tunneling diode measurements and doping-profile-based diode simulations. Dopant and Ge profiles of the diodes are determined by secondary ion mass spectrometry and capacitance-voltage measurements. Theoretical parameters of the band-to-band tunneling model are calculated based on strain-dependent properties such as bandgap, phonon energy, deformation-potential-based electron-phonon coupling, and hole effective masses of strained SiGe. The latter is determined withmore » a 6-band k·p model. The calibration indicates an underestimation of the theoretical electron-phonon coupling with nearly an order of magnitude. Prospects of compressively strained SiGe tunneling transistors are made by simulations with the calibrated model.« less

Probabilistic calibration of the SPITFIRE fire spread model using Earth observation data

NASA Astrophysics Data System (ADS)

Gomez-Dans, Jose; Wooster, Martin; Lewis, Philip; Spessa, Allan

2010-05-01

There is a great interest in understanding how fire affects vegetation distribution and dynamics in the context of global vegetation modelling. A way to include these effects is through the development of embedded fire spread models. However, fire is a complex phenomenon, thus difficult to model. Statistical models based on fire return intervals, or fire danger indices need large amounts of data for calibration, and are often prisoner to the epoch they were calibrated to. Mechanistic models, such as SPITFIRE, try to model the complete fire phenomenon based on simple physical rules, making these models mostly independent of calibration data. However, the processes expressed in models such as SPITFIRE require many parameters. These parametrisations are often reliant on site-specific experiments, or in some other cases, paremeters might not be measured directly. Additionally, in many cases, changes in temporal and/or spatial resolution result in parameters becoming effective. To address the difficulties with parametrisation and the often-used fitting methodologies, we propose using a probabilistic framework to calibrate some areas of the SPITFIRE fire spread model. We calibrate the model against Earth Observation (EO) data, a global and ever-expanding source of relevant data. We develop a methodology that tries to incorporate the limitations of the EO data, reasonable prior values for parameters and that results in distributions of parameters, which can be used to infer uncertainty due to parameter estimates. Additionally, the covariance structure of parameters and observations is also derived, whcih can help inform data gathering efforts and model development, respectively. For this work, we focus on Southern African savannas, an important ecosystem for fire studies, and one with a good amount of EO data relevnt to fire studies. As calibration datasets, we use burned area data, estimated number of fires and vegetation moisture dynamics.

Bayesian analysis of physiologically based toxicokinetic and toxicodynamic models.

PubMed

Hack, C Eric

2006-04-17

Physiologically based toxicokinetic (PBTK) and toxicodynamic (TD) models of bromate in animals and humans would improve our ability to accurately estimate the toxic doses in humans based on available animal studies. These mathematical models are often highly parameterized and must be calibrated in order for the model predictions of internal dose to adequately fit the experimentally measured doses. Highly parameterized models are difficult to calibrate and it is difficult to obtain accurate estimates of uncertainty or variability in model parameters with commonly used frequentist calibration methods, such as maximum likelihood estimation (MLE) or least squared error approaches. The Bayesian approach called Markov chain Monte Carlo (MCMC) analysis can be used to successfully calibrate these complex models. Prior knowledge about the biological system and associated model parameters is easily incorporated in this approach in the form of prior parameter distributions, and the distributions are refined or updated using experimental data to generate posterior distributions of parameter estimates. The goal of this paper is to give the non-mathematician a brief description of the Bayesian approach and Markov chain Monte Carlo analysis, how this technique is used in risk assessment, and the issues associated with this approach.

Calibration of the APEX Model to Simulate Management Practice Effects on Runoff, Sediment, and Phosphorus Loss.

PubMed

Bhandari, Ammar B; Nelson, Nathan O; Sweeney, Daniel W; Baffaut, Claire; Lory, John A; Senaviratne, Anomaa; Pierzynski, Gary M; Janssen, Keith A; Barnes, Philip L

2017-11-01

Process-based computer models have been proposed as a tool to generate data for Phosphorus (P) Index assessment and development. Although models are commonly used to simulate P loss from agriculture using managements that are different from the calibration data, this use of models has not been fully tested. The objective of this study is to determine if the Agricultural Policy Environmental eXtender (APEX) model can accurately simulate runoff, sediment, total P, and dissolved P loss from 0.4 to 1.5 ha of agricultural fields with managements that are different from the calibration data. The APEX model was calibrated with field-scale data from eight different managements at two locations (management-specific models). The calibrated models were then validated, either with the same management used for calibration or with different managements. Location models were also developed by calibrating APEX with data from all managements. The management-specific models resulted in satisfactory performance when used to simulate runoff, total P, and dissolved P within their respective systems, with > 0.50, Nash-Sutcliffe efficiency > 0.30, and percent bias within ±35% for runoff and ±70% for total and dissolved P. When applied outside the calibration management, the management-specific models only met the minimum performance criteria in one-third of the tests. The location models had better model performance when applied across all managements compared with management-specific models. Our results suggest that models only be applied within the managements used for calibration and that data be included from multiple management systems for calibration when using models to assess management effects on P loss or evaluate P Indices. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

An operational epidemiological model for calibrating agent-based simulations of pandemic influenza outbreaks.

PubMed

Prieto, D; Das, T K

2016-03-01

Uncertainty of pandemic influenza viruses continue to cause major preparedness challenges for public health policymakers. Decisions to mitigate influenza outbreaks often involve tradeoff between the social costs of interventions (e.g., school closure) and the cost of uncontrolled spread of the virus. To achieve a balance, policymakers must assess the impact of mitigation strategies once an outbreak begins and the virus characteristics are known. Agent-based (AB) simulation is a useful tool for building highly granular disease spread models incorporating the epidemiological features of the virus as well as the demographic and social behavioral attributes of tens of millions of affected people. Such disease spread models provide excellent basis on which various mitigation strategies can be tested, before they are adopted and implemented by the policymakers. However, to serve as a testbed for the mitigation strategies, the AB simulation models must be operational. A critical requirement for operational AB models is that they are amenable for quick and simple calibration. The calibration process works as follows: the AB model accepts information available from the field and uses those to update its parameters such that some of its outputs in turn replicate the field data. In this paper, we present our epidemiological model based calibration methodology that has a low computational complexity and is easy to interpret. Our model accepts a field estimate of the basic reproduction number, and then uses it to update (calibrate) the infection probabilities in a way that its effect combined with the effects of the given virus epidemiology, demographics, and social behavior results in an infection pattern yielding a similar value of the basic reproduction number. We evaluate the accuracy of the calibration methodology by applying it for an AB simulation model mimicking a regional outbreak in the US. The calibrated model is shown to yield infection patterns closely replicating the input estimates of the basic reproduction number. The calibration method is also tested to replicate an initial infection incidence trend for a H1N1 outbreak like that of 2009.

Neuromusculoskeletal model self-calibration for on-line sequential bayesian moment estimation

NASA Astrophysics Data System (ADS)

Bueno, Diana R.; Montano, L.

2017-04-01

Objective. Neuromusculoskeletal models involve many subject-specific physiological parameters that need to be adjusted to adequately represent muscle properties. Traditionally, neuromusculoskeletal models have been calibrated with a forward-inverse dynamic optimization which is time-consuming and unfeasible for rehabilitation therapy. Non self-calibration algorithms have been applied to these models. To the best of our knowledge, the algorithm proposed in this work is the first on-line calibration algorithm for muscle models that allows a generic model to be adjusted to different subjects in a few steps. Approach. In this paper we propose a reformulation of the traditional muscle models that is able to sequentially estimate the kinetics (net joint moments), and also its full self-calibration (subject-specific internal parameters of the muscle from a set of arbitrary uncalibrated data), based on the unscented Kalman filter. The nonlinearity of the model as well as its calibration problem have obliged us to adopt the sum of Gaussians filter suitable for nonlinear systems. Main results. This sequential Bayesian self-calibration algorithm achieves a complete muscle model calibration using as input only a dataset of uncalibrated sEMG and kinematics data. The approach is validated experimentally using data from the upper limbs of 21 subjects. Significance. The results show the feasibility of neuromusculoskeletal model self-calibration. This study will contribute to a better understanding of the generalization of muscle models for subject-specific rehabilitation therapies. Moreover, this work is very promising for rehabilitation devices such as electromyography-driven exoskeletons or prostheses.

Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

NASA Astrophysics Data System (ADS)

Demirel, Mehmet C.; Mai, Juliane; Mendiguren, Gorka; Koch, Julian; Samaniego, Luis; Stisen, Simon

2018-02-01

Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex evolution optimiser. The calibration results reveal a limited trade-off between streamflow dynamics and spatial patterns illustrating the benefit of combining separate observation types and objective functions. At the same time, the simulated spatial patterns of AET significantly improved when an objective function based on observed AET patterns and a novel spatial performance metric compared to traditional streamflow-only calibration were included. Since the overall water balance is usually a crucial goal in hydrologic modelling, spatial-pattern-oriented optimisation should always be accompanied by traditional discharge measurements. In such a multi-objective framework, the current study promotes the use of a novel bias-insensitive spatial pattern metric, which exploits the key information contained in the observed patterns while allowing the water balance to be informed by discharge observations.

[Fundamental aspects for accrediting medical equipment calibration laboratories in Colombia].

PubMed

Llamosa-Rincón, Luis E; López-Isaza, Giovanni A; Villarreal-Castro, Milton F

2010-02-01

Analysing the fundamental methodological aspects which should be considered when drawing up calibration procedure for electro-medical equipment, thereby permitting international standard-based accreditation of electro-medical metrology laboratories in Colombia. NTC-ISO-IEC 17025:2005 and GTC-51-based procedures for calibrating electro-medical equipment were implemented and then used as patterns. The mathematical model for determining the estimated uncertainty value when calibrating electro-medical equipment for accreditation by the Electrical Variable Metrology Laboratory's Electro-medical Equipment Calibration Area accredited in compliance with Superintendence of Industry and Commerce Resolution 25771 May 26th 2009 consists of two equations depending on the case; they are: E = (Ai + sigmaAi) - (Ar + sigmaAr + deltaAr1) and E = (Ai + sigmaAi) - (Ar + sigmaA + deltaAr1). The mathematical modelling implemented for measuring uncertainty in the Universidad Tecnológica de Pereira's Electrical Variable Metrology Laboratory (Electro-medical Equipment Calibration Area) will become a good guide for calibration initiated in other laboratories in Colombia and Latin-America.

An absolute photometric system at 10 and 20 microns

NASA Technical Reports Server (NTRS)

Rieke, G. H.; Lebofsky, M. J.; Low, F. J.

1985-01-01

Two new direct calibrations at 10 and 20 microns are presented in which terrestrial flux standards are referred to infrared standard stars. These measurements give both good agreement and higher accuracy when compared with previous direct calibrations. As a result, the absolute calibrations at 10 and 20 microns have now been determined with accuracies of 3 and 8 percent, respectively. A variety of absolute calibrations based on extrapolation of stellar spectra from the visible to 10 microns are reviewed. Current atmospheric models of A-type stars underestimate their fluxes by about 10 percent at 10 microns, whereas models of solar-type stars agree well with the direct calibrations. The calibration at 20 microns can probably be determined to about 5 percent by extrapolation from the more accurate result at 10 microns. The photometric system at 10 and 20 microns is updated to reflect the new absolute calibration, to base its zero point directly on the colors of A0 stars, and to improve the accuracy in the comparison of the standard stars.

Status of use of lunar irradiance for on-orbit calibration

USGS Publications Warehouse

Stone, T.C.; Kieffer, H.H.; Anderson, J.M.; ,

2002-01-01

Routine observations of the Moon have been acquired by the Robotic Lunar Observatory (ROLO) for over four years. The ROLO instruments measure lunar radiance in 23 VNIR (Moon diameter ???500 pixels) and 9 SWIR (???250 pixels) passbands every month when the Moon is at phase angle less than 90 degrees. These are converted to exoatmospheric values at standard distances using an atmospheric extinction model based on observations of standard stars and a NIST-traceable absolute calibration source. Reduction of the stellar images also provides an independent pathway for absolute calibration. Comparison of stellar-based and lamp-based absolute calibrations of the lunar images currently shows unacceptably large differences. An analytic model of lunar irradiance as a function of phase angle and viewing geometry is derived from the calibrated lunar images. Residuals from models which fit hundreds of observations at each wavelength average less than 2%. Comparison with SeaWiFS observations over three years reveals a small quasi-periodic change in SeaWiFS responsivity that correlates with distance from the Sun for the first two years, then departs from this correlation.

Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI

NASA Astrophysics Data System (ADS)

Ruiz-Pérez, Guiomar; Koch, Julian; Manfreda, Salvatore; Caylor, Kelly; Francés, Félix

2017-12-01

Ecohydrological modeling studies in developing countries, such as sub-Saharan Africa, often face the problem of extensive parametrical requirements and limited available data. Satellite remote sensing data may be able to fill this gap, but require novel methodologies to exploit their spatio-temporal information that could potentially be incorporated into model calibration and validation frameworks. The present study tackles this problem by suggesting an automatic calibration procedure, based on the empirical orthogonal function, for distributed ecohydrological daily models. The procedure is tested with the support of remote sensing data in a data-scarce environment - the upper Ewaso Ngiro river basin in Kenya. In the present application, the TETIS-VEG model is calibrated using only NDVI (Normalized Difference Vegetation Index) data derived from MODIS. The results demonstrate that (1) satellite data of vegetation dynamics can be used to calibrate and validate ecohydrological models in water-controlled and data-scarce regions, (2) the model calibrated using only satellite data is able to reproduce both the spatio-temporal vegetation dynamics and the observed discharge at the outlet and (3) the proposed automatic calibration methodology works satisfactorily and it allows for a straightforward incorporation of spatio-temporal data into the calibration and validation framework of a model.

A Practical Guide to Calibration of a GSSHA Hydrologic Model Using ERDC Automated Model Calibration Software - Effective and Efficient Stochastic Global Optimization

DTIC Science & Technology

2012-02-01

parameter estimation method, but rather to carefully describe how to use the ERDC software implementation of MLSL that accommodates the PEST model...model independent LM method based parameter estimation software PEST (Doherty, 2004, 2007a, 2007b), which quantifies model to measure- ment misfit...et al. (2011) focused on one drawback associated with LM-based model independent parameter estimation as implemented in PEST ; viz., that it requires

Spectral Irradiance Calibration in the Infrared. 11; Comparison of (alpha) Bootis and 1 Ceres with a Laboratory Standard

NASA Technical Reports Server (NTRS)

Witteborn, Fred C.; Cohen, Martin; Bregman, Jesse D.; Wooden, Diane H.; Heere, Karen; Shirley, Eric L.

1999-01-01

Infrared spectra of two celestial objects frequently used as flux standards are calibrated against an absolute laboratory flux standard at a spectral resolving power of 100 to 200. The spectrum of the KI.5 III star alpha Boo is measured from 3 to 30 microns, and that of the C-type asteroid 1 Ceres from 5 to 30 microns. While these "standard" spectra do not have the apparent precision of those based on calculated models, they do not require the assumptions involved in theoretical models of stars and asteroids. Specifically, they provide a model-independent means of calibrating celestial flux in the spectral range from 12 to 30 microns, where accurate absolute photometry is not available. The agreement found between the spectral shapes of alpha Boo and Ceres based on laboratory standards and those based on observed ratios to alpha CMa (Sirius) and alpha Lyr (Vega), flux-calibrated by theoretical modeling of these hot stars, strengthens our confidence in the applicability of the stellar models as primary irradiance standards.

Spectral Irradiance Calibration in the Infrared 11: Comparison of (alpha) Boo and 1 Ceres with a Laboratory Standard

NASA Technical Reports Server (NTRS)

Witteborn, Fred C.; Cohen, Martin; Bregman, Jess D.; Wooden, Diane; Heere, Karen; Shirley, Eric L.

1998-01-01

Infrared spectra of two celestial objects frequently used as flux standards are calibrated against an absolute laboratory flux standard at a spectral resolving power of 100 to 200. The spectrum of the K1.5III star, alpha Boo, is measured from 3 microns to 30 microns and that of the C-type asteroid, 1 Ceres, from 5 microns to 30 microns. While these 'standard' spectra do not have the apparent precision of those based on calculated models, they do not require the assumptions involved in theoretical models of stars and asteroids. Specifically they provide a model-independent means of calibrating celestial flux in the spectral range from 12 microns to 30 microns where accurate absolute photometry is not available. The agreement found between the spectral shapes of alpha Boo and Ceres based on laboratory standards, and those based on observed ratios to alpha CMa (Sirius) and alpha Lyr (Vega), flux calibrated by theoretical modeling of these hot stars strengthens our confidence in the applicability of the stellar models as primary irradiance standards.

Hydrologic Model Development and Calibration: Contrasting a Single- and Multi-Objective Approach for Comparing Model Performance

NASA Astrophysics Data System (ADS)

Asadzadeh, M.; Maclean, A.; Tolson, B. A.; Burn, D. H.

2009-05-01

Hydrologic model calibration aims to find a set of parameters that adequately simulates observations of watershed behavior, such as streamflow, or a state variable, such as snow water equivalent (SWE). There are different metrics for evaluating calibration effectiveness that involve quantifying prediction errors, such as the Nash-Sutcliffe (NS) coefficient and bias evaluated for the entire calibration period, on a seasonal basis, for low flows, or for high flows. Many of these metrics are conflicting such that the set of parameters that maximizes the high flow NS differs from the set of parameters that maximizes the low flow NS. Conflicting objectives are very likely when different calibration objectives are based on different fluxes and/or state variables (e.g., NS based on streamflow versus SWE). One of the most popular ways to balance different metrics is to aggregate them based on their importance and find the set of parameters that optimizes a weighted sum of the efficiency metrics. Comparing alternative hydrologic models (e.g., assessing model improvement when a process or more detail is added to the model) based on the aggregated objective might be misleading since it represents one point on the tradeoff of desired error metrics. To derive a more comprehensive model comparison, we solved a bi-objective calibration problem to estimate the tradeoff between two error metrics for each model. Although this approach is computationally more expensive than the aggregation approach, it results in a better understanding of the effectiveness of selected models at each level of every error metric and therefore provides a better rationale for judging relative model quality. The two alternative models used in this study are two MESH hydrologic models (version 1.2) of the Wolf Creek Research basin that differ in their watershed spatial discretization (a single Grouped Response Unit, GRU, versus multiple GRUs). The MESH model, currently under development by Environment Canada, is a coupled land-surface and hydrologic model. Results will demonstrate the conclusions a modeller might make regarding the value of additional watershed spatial discretization under both an aggregated (single-objective) and multi-objective model comparison framework.

Calibration by Hydrological Response Unit of a National Hydrologic Model to Improve Spatial Representation and Distribution of Parameters

NASA Astrophysics Data System (ADS)

Norton, P. A., II

2015-12-01

The U. S. Geological Survey is developing a National Hydrologic Model (NHM) to support consistent hydrologic modeling across the conterminous United States (CONUS). The Precipitation-Runoff Modeling System (PRMS) simulates daily hydrologic and energy processes in watersheds, and is used for the NHM application. For PRMS each watershed is divided into hydrologic response units (HRUs); by default each HRU is assumed to have a uniform hydrologic response. The Geospatial Fabric (GF) is a database containing initial parameter values for input to PRMS and was created for the NHM. The parameter values in the GF were derived from datasets that characterize the physical features of the entire CONUS. The NHM application is composed of more than 100,000 HRUs from the GF. Selected parameter values commonly are adjusted by basin in PRMS using an automated calibration process based on calibration targets, such as streamflow. Providing each HRU with distinct values that captures variability within the CONUS may improve simulation performance of the NHM. During calibration of the NHM by HRU, selected parameter values are adjusted for PRMS based on calibration targets, such as streamflow, snow water equivalent (SWE) and actual evapotranspiration (AET). Simulated SWE, AET, and runoff were compared to value ranges derived from multiple sources (e.g. the Snow Data Assimilation System, the Moderate Resolution Imaging Spectroradiometer (i.e. MODIS) Global Evapotranspiration Project, the Simplified Surface Energy Balance model, and the Monthly Water Balance Model). This provides each HRU with a distinct set of parameter values that captures the variability within the CONUS, leading to improved model performance. We present simulation results from the NHM after preliminary calibration, including the results of basin-level calibration for the NHM using: 1) default initial GF parameter values, and 2) parameter values calibrated by HRU.

Updated radiometric calibration for the Landsat-5 thematic mapper reflective bands

USGS Publications Warehouse

Helder, D.L.; Markham, B.L.; Thome, K.J.; Barsi, J.A.; Chander, G.; Malla, R.

2008-01-01

The Landsat-5 Thematic Mapper (TM) has been the workhorse of the Landsat system. Launched in 1984, it continues collecting data through the time frame of this paper. Thus, it provides an invaluable link to the past history of the land features of the Earth's surface, and it becomes imperative to provide an accurate radiometric calibration of the reflective bands to the user community. Previous calibration has been based on information obtained from prelaunch, the onboard calibrator, vicarious calibration attempts, and cross-calibration with Landsat-7. Currently, additional data sources are available to improve this calibration. Specifically, improvements in vicarious calibration methods and development of the use of pseudoinvariant sites for trending provide two additional independent calibration sources. The use of these additional estimates has resulted in a consistent calibration approach that ties together all of the available calibration data sources. Results from this analysis indicate a simple exponential, or a constant model may be used for all bands throughout the lifetime of Landsat-5 TM. Where previously time constants for the exponential models were approximately one year, the updated model has significantly longer time constants in bands 1-3. In contrast, bands 4, 5, and 7 are shown to be best modeled by a constant. The models proposed in this paper indicate calibration knowledge of 5% or better early in life, decreasing to nearly 2% later in life. These models have been implemented at the U.S. Geological Survey Earth Resources Observation and Science (EROS) and are the default calibration used for all Landsat TM data now distributed through EROS. ?? 2008 IEEE.

The benefits of using remotely sensed soil moisture in parameter identification of large-scale hydrological models

NASA Astrophysics Data System (ADS)

Wanders, N.; Bierkens, M. F. P.; de Jong, S. M.; de Roo, A.; Karssenberg, D.

2014-08-01

Large-scale hydrological models are nowadays mostly calibrated using observed discharge. As a result, a large part of the hydrological system, in particular the unsaturated zone, remains uncalibrated. Soil moisture observations from satellites have the potential to fill this gap. Here we evaluate the added value of remotely sensed soil moisture in calibration of large-scale hydrological models by addressing two research questions: (1) Which parameters of hydrological models can be identified by calibration with remotely sensed soil moisture? (2) Does calibration with remotely sensed soil moisture lead to an improved calibration of hydrological models compared to calibration based only on discharge observations, such that this leads to improved simulations of soil moisture content and discharge? A dual state and parameter Ensemble Kalman Filter is used to calibrate the hydrological model LISFLOOD for the Upper Danube. Calibration is done using discharge and remotely sensed soil moisture acquired by AMSR-E, SMOS, and ASCAT. Calibration with discharge data improves the estimation of groundwater and routing parameters. Calibration with only remotely sensed soil moisture results in an accurate identification of parameters related to land-surface processes. For the Upper Danube upstream area up to 40,000 km2, calibration on both discharge and soil moisture results in a reduction by 10-30% in the RMSE for discharge simulations, compared to calibration on discharge alone. The conclusion is that remotely sensed soil moisture holds potential for calibration of hydrological models, leading to a better simulation of soil moisture content throughout the catchment and a better simulation of discharge in upstream areas. This article was corrected on 15 SEP 2014. See the end of the full text for details.

Redundant Calibration: breaking the constraints of limited sky information

NASA Astrophysics Data System (ADS)

Joseph, Ronniy C.

2018-05-01

The latest generation of low frequency radio interferometers, e.g. LOFAR, MWA, PAPER, has been pushing down the detection limits on the hydrogen signal from the Epoch of Reionisation. However, due to the challenges posed by foregrounds and instrumental systematics the signal has eluded detection thus far. To overcome these challenges we require a detailed understanding of the calibration of these relatively new telescopes. This led to a renewed interest in redundant calibration. Classical calibration schemes depend on sky models based on limited knowledge of the low frequency sky. Redundant calibration, however, allows us to escape our ignorance as it is sky model independent. We will review the field of redundant calibration, and present work we have undertaken to understand the limitations of this calibration method.

Fast hydrological model calibration based on the heterogeneous parallel computing accelerated shuffled complex evolution method

NASA Astrophysics Data System (ADS)

Kan, Guangyuan; He, Xiaoyan; Ding, Liuqian; Li, Jiren; Hong, Yang; Zuo, Depeng; Ren, Minglei; Lei, Tianjie; Liang, Ke

2018-01-01

Hydrological model calibration has been a hot issue for decades. The shuffled complex evolution method developed at the University of Arizona (SCE-UA) has been proved to be an effective and robust optimization approach. However, its computational efficiency deteriorates significantly when the amount of hydrometeorological data increases. In recent years, the rise of heterogeneous parallel computing has brought hope for the acceleration of hydrological model calibration. This study proposed a parallel SCE-UA method and applied it to the calibration of a watershed rainfall-runoff model, the Xinanjiang model. The parallel method was implemented on heterogeneous computing systems using OpenMP and CUDA. Performance testing and sensitivity analysis were carried out to verify its correctness and efficiency. Comparison results indicated that heterogeneous parallel computing-accelerated SCE-UA converged much more quickly than the original serial version and possessed satisfactory accuracy and stability for the task of fast hydrological model calibration.

Calibration and accuracy analysis of a focused plenoptic camera

NASA Astrophysics Data System (ADS)

Zeller, N.; Quint, F.; Stilla, U.

2014-08-01

In this article we introduce new methods for the calibration of depth images from focused plenoptic cameras and validate the results. We start with a brief description of the concept of a focused plenoptic camera and how from the recorded raw image a depth map can be estimated. For this camera, an analytical expression of the depth accuracy is derived for the first time. In the main part of the paper, methods to calibrate a focused plenoptic camera are developed and evaluated. The optical imaging process is calibrated by using a method which is already known from the calibration of traditional cameras. For the calibration of the depth map two new model based methods, which make use of the projection concept of the camera are developed. These new methods are compared to a common curve fitting approach, which is based on Taylor-series-approximation. Both model based methods show significant advantages compared to the curve fitting method. They need less reference points for calibration than the curve fitting method and moreover, supply a function which is valid in excess of the range of calibration. In addition the depth map accuracy of the plenoptic camera was experimentally investigated for different focal lengths of the main lens and is compared to the analytical evaluation.

Design and performance evaluation of a simplified dynamic model for combined sewer overflows in pumped sewer systems

NASA Astrophysics Data System (ADS)

van Daal-Rombouts, Petra; Sun, Siao; Langeveld, Jeroen; Bertrand-Krajewski, Jean-Luc; Clemens, François

2016-07-01

Optimisation or real time control (RTC) studies in wastewater systems increasingly require rapid simulations of sewer systems in extensive catchments. To reduce the simulation time calibrated simplified models are applied, with the performance generally based on the goodness of fit of the calibration. In this research the performance of three simplified and a full hydrodynamic (FH) model for two catchments are compared based on the correct determination of CSO event occurrences and of the total discharged volumes to the surface water. Simplified model M1 consists of a rainfall runoff outflow (RRO) model only. M2 combines the RRO model with a static reservoir model for the sewer behaviour. M3 comprises the RRO model and a dynamic reservoir model. The dynamic reservoir characteristics were derived from FH model simulations. It was found that M2 and M3 are able to describe the sewer behaviour of the catchments, contrary to M1. The preferred model structure depends on the quality of the information (geometrical database and monitoring data) available for the design and calibration of the model. Finally, calibrated simplified models are shown to be preferable to uncalibrated FH models when performing optimisation or RTC studies.

Energy Performance Assessment of Radiant Cooling System through Modeling and Calibration at Component Level

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khan, Yasin; Mathur, Jyotirmay; Bhandari, Mahabir S

2016-01-01

The paper describes a case study of an information technology office building with a radiant cooling system and a conventional variable air volume (VAV) system installed side by side so that performancecan be compared. First, a 3D model of the building involving architecture, occupancy, and HVAC operation was developed in EnergyPlus, a simulation tool. Second, a different calibration methodology was applied to develop the base case for assessing the energy saving potential. This paper details the calibration of the whole building energy model to the component level, including lighting, equipment, and HVAC components such as chillers, pumps, cooling towers, fans,more » etc. Also a new methodology for the systematic selection of influence parameter has been developed for the calibration of a simulated model which requires large time for the execution. The error at the whole building level [measured in mean bias error (MBE)] is 0.2%, and the coefficient of variation of root mean square error (CvRMSE) is 3.2%. The total errors in HVAC at the hourly are MBE = 8.7% and CvRMSE = 23.9%, which meet the criteria of ASHRAE 14 (2002) for hourly calibration. Different suggestions have been pointed out to generalize the energy saving of radiant cooling system through the existing building system. So a base case model was developed by using the calibrated model for quantifying the energy saving potential of the radiant cooling system. It was found that a base case radiant cooling system integrated with DOAS can save 28% energy compared with the conventional VAV system.« less

Theoretical foundation, methods, and criteria for calibrating human vibration models using frequency response functions

PubMed Central

Dong, Ren G.; Welcome, Daniel E.; McDowell, Thomas W.; Wu, John Z.

2015-01-01

While simulations of the measured biodynamic responses of the whole human body or body segments to vibration are conventionally interpreted as summaries of biodynamic measurements, and the resulting models are considered quantitative, this study looked at these simulations from a different angle: model calibration. The specific aims of this study are to review and clarify the theoretical basis for model calibration, to help formulate the criteria for calibration validation, and to help appropriately select and apply calibration methods. In addition to established vibration theory, a novel theorem of mechanical vibration is also used to enhance the understanding of the mathematical and physical principles of the calibration. Based on this enhanced understanding, a set of criteria was proposed and used to systematically examine the calibration methods. Besides theoretical analyses, a numerical testing method is also used in the examination. This study identified the basic requirements for each calibration method to obtain a unique calibration solution. This study also confirmed that the solution becomes more robust if more than sufficient calibration references are provided. Practically, however, as more references are used, more inconsistencies can arise among the measured data for representing the biodynamic properties. To help account for the relative reliabilities of the references, a baseline weighting scheme is proposed. The analyses suggest that the best choice of calibration method depends on the modeling purpose, the model structure, and the availability and reliability of representative reference data. PMID:26740726

Calibrating Historical IR Sensors Using GEO, and AVHRR Infrared Tropical Mean Calibration Models

NASA Technical Reports Server (NTRS)

Scarino, Benjamin; Doelling, David R.; Minnis, Patrick; Gopalan, Arun; Haney, Conor; Bhatt, Rajendra

2014-01-01

Long-term, remote-sensing-based climate data records (CDRs) are highly dependent on having consistent, wellcalibrated satellite instrument measurements of the Earth's radiant energy. Therefore, by making historical satellite calibrations consistent with those of today's imagers, the Earth-observing community can benefit from a CDR that spans a minimum of 30 years. Most operational meteorological satellites rely on an onboard blackbody and space looks to provide on-orbit IR calibration, but neither target is traceable to absolute standards. The IR channels can also be affected by ice on the detector window, angle dependency of the scan mirror emissivity, stray-light, and detector-to-detector striping. Being able to quantify and correct such degradations would mean IR data from any satellite imager could contribute to a CDR. Recent efforts have focused on utilizing well-calibrated modern hyper-spectral sensors to intercalibrate concurrent operational IR imagers to a single reference. In order to consistently calibrate both historical and current IR imagers to the same reference, however, another strategy is needed. Large, well-characterized tropical-domain Earth targets have the potential of providing an Earth-view reference accuracy of within 0.5 K. To that effort, NASA Langley is developing an IR tropical mean calibration model in order to calibrate historical Advanced Very High Resolution Radiometer (AVHRR) instruments. Using Meteosat-9 (Met-9) as a reference, empirical models are built based on spatially/temporally binned Met-9 and AVHRR tropical IR brightness temperatures. By demonstrating the stability of the Met-9 tropical models, NOAA-18 AVHRR can be calibrated to Met-9 by matching the AVHRR monthly histogram averages with the Met-9 model. This method is validated with ray-matched AVHRR and Met-9 biasdifference time series. Establishing the validity of this empirical model will allow for the calibration of historical AVHRR sensors to within 0.5 K, and thereby establish a climate-quality IR data record.

A Report on the Validation of Beryllium Strength Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Armstrong, Derek Elswick

2016-02-05

This report discusses work on validating beryllium strength models with flyer plate and Taylor rod experimental data. Strength models are calibrated with Hopkinson bar and quasi-static data. The Hopkinson bar data for beryllium provides strain rates up to about 4000 per second. A limitation of the Hopkinson bar data for beryllium is that it only provides information on strain up to about 0.15. The lack of high strain data at high strain rates makes it difficult to distinguish between various strength model settings. The PTW model has been calibrated many different times over the last 12 years. The lack ofmore » high strain data for high strain rates has resulted in these calibrated PTW models for beryllium exhibiting significantly different behavior when extrapolated to high strain. For beryllium, the α parameter of PTW has recently been calibrated to high precision shear modulus data. In the past the α value for beryllium was set based on expert judgment. The new α value for beryllium was used in a calibration of the beryllium PTW model by Sky Sjue. The calibration by Sjue used EOS table information to model the temperature dependence of the heat capacity. Also, the calibration by Sjue used EOS table information to model the density changes of the beryllium sample during the Hopkinson bar and quasi-static experiments. In this paper, the calibrated PTW model by Sjue is compared against experimental data and other strength models. The other strength models being considered are a PTW model calibrated by Shuh- Rong Chen and a Steinberg-Guinan type model by John Pedicini. The three strength models are used in a comparison against flyer plate and Taylor rod data. The results show that the Chen PTW model provides better agreement to this data. The Chen PTW model settings have been previously adjusted to provide a better fit to flyer plate data, whereas the Sjue PTW model has not been changed based on flyer plate data. However, the Sjue model provides a reasonable fit to flyer plate and Taylor rod data, and also gives a better match to recently analyzed Z-machine data which has a strain of about 0.35 and a strain rate of 3e5 s -1.« less

Conceptual modelling to predict unobserved system states - the case of groundwater flooding in the UK Chalk

NASA Astrophysics Data System (ADS)

Hartmann, A. J.; Ireson, A. M.

2017-12-01

Chalk aquifers represent an important source of drinking water in the UK. Due to its fractured-porous structure, Chalk aquifers are characterized by highly dynamic groundwater fluctuations that enhance the risk of groundwater flooding. The risk of groundwater flooding can be assessed by physically-based groundwater models. But for reliable results, a-priori information about the distribution of hydraulic conductivities and porosities is necessary, which is often not available. For that reason, conceptual simulation models are often used to predict groundwater behaviour. They commonly require calibration by historic groundwater observations. Consequently, their prediction performance may reduce significantly, when it comes to system states that did not occur within the calibration time series. In this study, we calibrate a conceptual model to the observed groundwater level observations at several locations within a Chalk system in Southern England. During the calibration period, no groundwater flooding occurred. We then apply our model to predict the groundwater dynamics of the system at a time that includes a groundwater flooding event. We show that the calibrated model provides reasonable predictions before and after the flooding event but it over-estimates groundwater levels during the event. After modifying the model structure to include topographic information, the model is capable of prediction the groundwater flooding event even though groundwater flooding never occurred in the calibration period. Although straight forward, our approach shows how conceptual process-based models can be applied to predict system states and dynamics that did not occur in the calibration period. We believe such an approach can be transferred to similar cases, especially to regions where rainfall intensities are expected to trigger processes and system states that may have not yet been observed.

Thermal-depth matching in dynamic scene based on affine projection and feature registration

NASA Astrophysics Data System (ADS)

Wang, Hongyu; Jia, Tong; Wu, Chengdong; Li, Yongqiang

2018-03-01

This paper aims to study the construction of 3D temperature distribution reconstruction system based on depth and thermal infrared information. Initially, a traditional calibration method cannot be directly used, because the depth and thermal infrared camera is not sensitive to the color calibration board. Therefore, this paper aims to design a depth and thermal infrared camera calibration board to complete the calibration of the depth and thermal infrared camera. Meanwhile a local feature descriptors in thermal and depth images is proposed. The belief propagation matching algorithm is also investigated based on the space affine transformation matching and local feature matching. The 3D temperature distribution model is built based on the matching of 3D point cloud and 2D thermal infrared information. Experimental results show that the method can accurately construct the 3D temperature distribution model, and has strong robustness.

Calibration of the APEX model to simulate management practice effects on runoff, sediment, and phosphorus loss

USDA-ARS?s Scientific Manuscript database

Process-based computer models have been proposed as a tool to generate data for phosphorus-index assessment and development. Although models are commonly used to simulate phosphorus (P) loss from agriculture using managements that are different from the calibration data, this use of models has not ...

Multi-objective calibration and uncertainty analysis of hydrologic models; A comparative study between formal and informal methods

NASA Astrophysics Data System (ADS)

Shafii, M.; Tolson, B.; Matott, L. S.

2012-04-01

Hydrologic modeling has benefited from significant developments over the past two decades. This has resulted in building of higher levels of complexity into hydrologic models, which eventually makes the model evaluation process (parameter estimation via calibration and uncertainty analysis) more challenging. In order to avoid unreasonable parameter estimates, many researchers have suggested implementation of multi-criteria calibration schemes. Furthermore, for predictive hydrologic models to be useful, proper consideration of uncertainty is essential. Consequently, recent research has emphasized comprehensive model assessment procedures in which multi-criteria parameter estimation is combined with statistically-based uncertainty analysis routines such as Bayesian inference using Markov Chain Monte Carlo (MCMC) sampling. Such a procedure relies on the use of formal likelihood functions based on statistical assumptions, and moreover, the Bayesian inference structured on MCMC samplers requires a considerably large number of simulations. Due to these issues, especially in complex non-linear hydrological models, a variety of alternative informal approaches have been proposed for uncertainty analysis in the multi-criteria context. This study aims at exploring a number of such informal uncertainty analysis techniques in multi-criteria calibration of hydrological models. The informal methods addressed in this study are (i) Pareto optimality which quantifies the parameter uncertainty using the Pareto solutions, (ii) DDS-AU which uses the weighted sum of objective functions to derive the prediction limits, and (iii) GLUE which describes the total uncertainty through identification of behavioral solutions. The main objective is to compare such methods with MCMC-based Bayesian inference with respect to factors such as computational burden, and predictive capacity, which are evaluated based on multiple comparative measures. The measures for comparison are calculated both for calibration and evaluation periods. The uncertainty analysis methodologies are applied to a simple 5-parameter rainfall-runoff model, called HYMOD.

Calibration of limited-area ensemble precipitation forecasts for hydrological predictions

NASA Astrophysics Data System (ADS)

Diomede, Tommaso; Marsigli, Chiara; Montani, Andrea; Nerozzi, Fabrizio; Paccagnella, Tiziana

2015-04-01

The main objective of this study is to investigate the impact of calibration for limited-area ensemble precipitation forecasts, to be used for driving discharge predictions up to 5 days in advance. A reforecast dataset, which spans 30 years, based on the Consortium for Small Scale Modeling Limited-Area Ensemble Prediction System (COSMO-LEPS) was used for testing the calibration strategy. Three calibration techniques were applied: quantile-to-quantile mapping, linear regression, and analogs. The performance of these methodologies was evaluated in terms of statistical scores for the precipitation forecasts operationally provided by COSMO-LEPS in the years 2003-2007 over Germany, Switzerland, and the Emilia-Romagna region (northern Italy). The analog-based method seemed to be preferred because of its capability of correct position errors and spread deficiencies. A suitable spatial domain for the analog search can help to handle model spatial errors as systematic errors. However, the performance of the analog-based method may degrade in cases where a limited training dataset is available. A sensitivity test on the length of the training dataset over which to perform the analog search has been performed. The quantile-to-quantile mapping and linear regression methods were less effective, mainly because the forecast-analysis relation was not so strong for the available training dataset. A comparison between the calibration based on the deterministic reforecast and the calibration based on the full operational ensemble used as training dataset has been considered, with the aim to evaluate whether reforecasts are really worthy for calibration, given that their computational cost is remarkable. The verification of the calibration process was then performed by coupling ensemble precipitation forecasts with a distributed rainfall-runoff model. This test was carried out for a medium-sized catchment located in Emilia-Romagna, showing a beneficial impact of the analog-based method on the reduction of missed events for discharge predictions.

Simulation of daily streamflow for 12 river basins in western Iowa using the Precipitation-Runoff Modeling System

USGS Publications Warehouse

Christiansen, Daniel E.; Haj, Adel E.; Risley, John C.

2017-10-24

The U.S. Geological Survey, in cooperation with the Iowa Department of Natural Resources, constructed Precipitation-Runoff Modeling System models to estimate daily streamflow for 12 river basins in western Iowa that drain into the Missouri River. The Precipitation-Runoff Modeling System is a deterministic, distributed-parameter, physical-process-based modeling system developed to evaluate the response of streamflow and general drainage basin hydrology to various combinations of climate and land use. Calibration periods for each basin varied depending on the period of record available for daily mean streamflow measurements at U.S. Geological Survey streamflow-gaging stations.A geographic information system tool was used to delineate each basin and estimate initial values for model parameters based on basin physical and geographical features. A U.S. Geological Survey automatic calibration tool that uses a shuffled complex evolution algorithm was used for initial calibration, and then manual modifications were made to parameter values to complete the calibration of each basin model. The main objective of the calibration was to match daily discharge values of simulated streamflow to measured daily discharge values. The Precipitation-Runoff Modeling System model was calibrated at 42 sites located in the 12 river basins in western Iowa.The accuracy of the simulated daily streamflow values at the 42 calibration sites varied by river and by site. The models were satisfactory at 36 of the sites based on statistical results. Unsatisfactory performance at the six other sites can be attributed to several factors: (1) low flow, no flow, and flashy flow conditions in headwater subbasins having a small drainage area; (2) poor representation of the groundwater and storage components of flow within a basin; (3) lack of accounting for basin withdrawals and water use; and (4) limited availability and accuracy of meteorological input data. The Precipitation-Runoff Modeling System models of 12 river basins in western Iowa will provide water-resource managers with a consistent and documented method for estimating streamflow at ungaged sites and aid in environmental studies, hydraulic design, water management, and water-quality projects.

Automatically calibrating admittances in KATE's autonomous launch operations model

NASA Technical Reports Server (NTRS)

Morgan, Steve

1992-01-01

This report documents a 1000-line Symbolics LISP program that automatically calibrates all 15 fluid admittances in KATE's Autonomous Launch Operations (ALO) model. (KATE is Kennedy Space Center's Knowledge-based Autonomous Test Engineer, a diagnosis and repair expert system created for use on the Space Shuttle's various fluid flow systems.) As a new KATE application, the calibrator described here breaks new ground for KSC's Artificial Intelligence Lab by allowing KATE to both control and measure the hardware she supervises. By automating a formerly manual process, the calibrator: (1) saves the ALO model builder untold amounts of labor; (2) enables quick repairs after workmen accidently adjust ALO's hand valves; and (3) frees the modeler to pursue new KATE applications that previously were too complicated. Also reported are suggestions for enhancing the program: (1) to calibrate ALO's TV cameras, pumps, and sensor tolerances; and (2) to calibrate devices in other KATE models, such as the shuttle's LOX and Environment Control System (ECS).

Data Assimilation - Advances and Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Williams, Brian J.

2014-07-30

This presentation provides an overview of data assimilation (model calibration) for complex computer experiments. Calibration refers to the process of probabilistically constraining uncertain physics/engineering model inputs to be consistent with observed experimental data. An initial probability distribution for these parameters is updated using the experimental information. Utilization of surrogate models and empirical adjustment for model form error in code calibration form the basis for the statistical methodology considered. The role of probabilistic code calibration in supporting code validation is discussed. Incorporation of model form uncertainty in rigorous uncertainty quantification (UQ) analyses is also addressed. Design criteria used within a batchmore » sequential design algorithm are introduced for efficiently achieving predictive maturity and improved code calibration. Predictive maturity refers to obtaining stable predictive inference with calibrated computer codes. These approaches allow for augmentation of initial experiment designs for collecting new physical data. A standard framework for data assimilation is presented and techniques for updating the posterior distribution of the state variables based on particle filtering and the ensemble Kalman filter are introduced.« less

Hydrologic Modeling in the Kenai River Watershed using Event Based Calibration

NASA Astrophysics Data System (ADS)

Wells, B.; Toniolo, H. A.; Stuefer, S. L.

2015-12-01

Understanding hydrologic changes is key for preparing for possible future scenarios. On the Kenai Peninsula in Alaska the yearly salmon runs provide a valuable stimulus to the economy. It is the focus of a large commercial fishing fleet, but also a prime tourist attraction. Modeling of anadromous waters provides a tool that assists in the prediction of future salmon run size. Beaver Creek, in Kenai, Alaska, is a lowlands stream that has been modeled using the Army Corps of Engineers event based modeling package HEC-HMS. With the use of historic precipitation and discharge data, the model was calibrated to observed discharge values. The hydrologic parameters were measured in the field or calculated, while soil parameters were estimated and adjusted during the calibration. With the calibrated parameter for HEC-HMS, discharge estimates can be used by other researches studying the area and help guide communities and officials to make better-educated decisions regarding the changing hydrology in the area and the tied economic drivers.

Two statistics for evaluating parameter identifiability and error reduction

USGS Publications Warehouse

Doherty, John; Hunt, Randall J.

2009-01-01

Two statistics are presented that can be used to rank input parameters utilized by a model in terms of their relative identifiability based on a given or possible future calibration dataset. Identifiability is defined here as the capability of model calibration to constrain parameters used by a model. Both statistics require that the sensitivity of each model parameter be calculated for each model output for which there are actual or presumed field measurements. Singular value decomposition (SVD) of the weighted sensitivity matrix is then undertaken to quantify the relation between the parameters and observations that, in turn, allows selection of calibration solution and null spaces spanned by unit orthogonal vectors. The first statistic presented, "parameter identifiability", is quantitatively defined as the direction cosine between a parameter and its projection onto the calibration solution space. This varies between zero and one, with zero indicating complete non-identifiability and one indicating complete identifiability. The second statistic, "relative error reduction", indicates the extent to which the calibration process reduces error in estimation of a parameter from its pre-calibration level where its value must be assigned purely on the basis of prior expert knowledge. This is more sophisticated than identifiability, in that it takes greater account of the noise associated with the calibration dataset. Like identifiability, it has a maximum value of one (which can only be achieved if there is no measurement noise). Conceptually it can fall to zero; and even below zero if a calibration problem is poorly posed. An example, based on a coupled groundwater/surface-water model, is included that demonstrates the utility of the statistics. ?? 2009 Elsevier B.V.

Calibrating the Medical Council of Canada's Qualifying Examination Part I using an integrated item response theory framework: a comparison of models and designs.

PubMed

De Champlain, Andre F; Boulais, Andre-Philippe; Dallas, Andrew

2016-01-01

The aim of this research was to compare different methods of calibrating multiple choice question (MCQ) and clinical decision making (CDM) components for the Medical Council of Canada's Qualifying Examination Part I (MCCQEI) based on item response theory. Our data consisted of test results from 8,213 first time applicants to MCCQEI in spring and fall 2010 and 2011 test administrations. The data set contained several thousand multiple choice items and several hundred CDM cases. Four dichotomous calibrations were run using BILOG-MG 3.0. All 3 mixed item format (dichotomous MCQ responses and polytomous CDM case scores) calibrations were conducted using PARSCALE 4. The 2-PL model had identical numbers of items with chi-square values at or below a Type I error rate of 0.01 (83/3,499 or 0.02). In all 3 polytomous models, whether the MCQs were either anchored or concurrently run with the CDM cases, results suggest very poor fit. All IRT abilities estimated from dichotomous calibration designs correlated very highly with each other. IRT-based pass-fail rates were extremely similar, not only across calibration designs and methods, but also with regard to the actual reported decision to candidates. The largest difference noted in pass rates was 4.78%, which occurred between the mixed format concurrent 2-PL graded response model (pass rate= 80.43%) and the dichotomous anchored 1-PL calibrations (pass rate= 85.21%). Simpler calibration designs with dichotomized items should be implemented. The dichotomous calibrations provided better fit of the item response matrix than more complex, polytomous calibrations.

Automatic Calibration of a Semi-Distributed Hydrologic Model Using Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Bekele, E. G.; Nicklow, J. W.

2005-12-01

Hydrologic simulation models need to be calibrated and validated before using them for operational predictions. Spatially-distributed hydrologic models generally have a large number of parameters to capture the various physical characteristics of a hydrologic system. Manual calibration of such models is a very tedious and daunting task, and its success depends on the subjective assessment of a particular modeler, which includes knowledge of the basic approaches and interactions in the model. In order to alleviate these shortcomings, an automatic calibration model, which employs an evolutionary optimization technique known as Particle Swarm Optimizer (PSO) for parameter estimation, is developed. PSO is a heuristic search algorithm that is inspired by social behavior of bird flocking or fish schooling. The newly-developed calibration model is integrated to the U.S. Department of Agriculture's Soil and Water Assessment Tool (SWAT). SWAT is a physically-based, semi-distributed hydrologic model that was developed to predict the long term impacts of land management practices on water, sediment and agricultural chemical yields in large complex watersheds with varying soils, land use, and management conditions. SWAT was calibrated for streamflow and sediment concentration. The calibration process involves parameter specification, whereby sensitive model parameters are identified, and parameter estimation. In order to reduce the number of parameters to be calibrated, parameterization was performed. The methodology is applied to a demonstration watershed known as Big Creek, which is located in southern Illinois. Application results show the effectiveness of the approach and model predictions are significantly improved.

Calibration of a distributed hydrologic model using observed spatial patterns from MODIS data

NASA Astrophysics Data System (ADS)

Demirel, Mehmet C.; González, Gorka M.; Mai, Juliane; Stisen, Simon

2016-04-01

Distributed hydrologic models are typically calibrated against streamflow observations at the outlet of the basin. Along with these observations from gauging stations, satellite based estimates offer independent evaluation data such as remotely sensed actual evapotranspiration (aET) and land surface temperature. The primary objective of the study is to compare model calibrations against traditional downstream discharge measurements with calibrations against simulated spatial patterns and combinations of both types of observations. While the discharge based model calibration typically improves the temporal dynamics of the model, it seems to give rise to minimum improvement of the simulated spatial patterns. In contrast, objective functions specifically targeting the spatial pattern performance could potentially increase the spatial model performance. However, most modeling studies, including the model formulations and parameterization, are not designed to actually change the simulated spatial pattern during calibration. This study investigates the potential benefits of incorporating spatial patterns from MODIS data to calibrate the mesoscale hydrologic model (mHM). This model is selected as it allows for a change in the spatial distribution of key soil parameters through the optimization of pedo-transfer function parameters and includes options for using fully distributed daily Leaf Area Index (LAI) values directly as input. In addition the simulated aET can be estimated at a spatial resolution suitable for comparison to the spatial patterns observed with MODIS data. To increase our control on spatial calibration we introduced three additional parameters to the model. These new parameters are part of an empirical equation to the calculate crop coefficient (Kc) from daily LAI maps and used to update potential evapotranspiration (PET) as model inputs. This is done instead of correcting/updating PET with just a uniform (or aspect driven) factor used in the mHM model (version 5.3). We selected the 20 most important parameters out of 53 mHM parameters based on a comprehensive sensitivity analysis (Cuntz et al., 2015). We calibrated 1km-daily mHM for the Skjern basin in Denmark using the Shuffled Complex Evolution (SCE) algorithm and inputs at different spatial scales i.e. meteorological data at 10km and morphological data at 250 meters. We used correlation coefficients between observed monthly (summer months only) MODIS data calculated from cloud free days over the calibration period from 2001 to 2008 and simulated aET from mHM over the same period. Similarly other metrics, e.g mapcurves and fraction skill-score, are also included in our objective function to assess the co-location of the grid-cells. The preliminary results show that multi-objective calibration of mHM against observed streamflow and spatial patterns together does not significantly reduce the spatial errors in aET while it improves the streamflow simulations. This is a strong signal for further investigation of the multi parameter regionalization affecting spatial aET patterns and weighting the spatial metrics in the objective function relative to the streamflow metrics.

The on-orbit calibration of geometric parameters of the Tian-Hui 1 (TH-1) satellite

NASA Astrophysics Data System (ADS)

Wang, Jianrong; Wang, Renxiang; Hu, Xin; Su, Zhongbo

2017-02-01

The on-orbit calibration of geometric parameters is a key step in improving the location accuracy of satellite images without using Ground Control Points (GCPs). Most methods of on-orbit calibration are based on the self-calibration using additional parameters. When using additional parameters, different number of additional parameters may lead to different results. The triangulation bundle adjustment is another way to calibrate the geometric parameters of camera, which can describe the changes in each geometric parameter. When triangulation bundle adjustment method is applied to calibrate geometric parameters, a prerequisite is that the strip model can avoid systematic deformation caused by the rate of attitude changes. Concerning the stereo camera, the influence of the intersection angle should be considered during calibration. The Equivalent Frame Photo (EFP) bundle adjustment based on the Line-Matrix CCD (LMCCD) image can solve the systematic distortion of the strip model, and obtain high accuracy location without using GCPs. In this paper, the triangulation bundle adjustment is used to calibrate the geometric parameters of TH-1 satellite cameras based on LMCCD image. During the bundle adjustment, the three-line array cameras are reconstructed by adopting the principle of inverse triangulation. Finally, the geometric accuracy is validated before and after on-orbit calibration using 5 testing fields. After on-orbit calibration, the 3D geometric accuracy is improved to 11.8 m from 170 m. The results show that the location accuracy of TH-1 without using GCPs is significantly improved using the on-orbit calibration of the geometric parameters.

Development of a Regional Glycerol Dialkyl Glycerol Tetraether (GDGT) - Temperature Calibration for Antarctic and sub-Antarctic Lakes

NASA Astrophysics Data System (ADS)

Roberts, S. J.; Foster, L. C.; Pearson, E. J.; Steve, J.; Hodgson, D.; Saunders, K. M.; Verleyen, E.

2016-12-01

Temperature calibration models based on the relative abundances of sedimentary glycerol dialkyl glycerol tetraethers (GDGTs) have been used to reconstruct past temperatures in both marine and terrestrial environments, but have not been widely applied in high latitude environments. This is mainly because the performance of GDGT-temperature calibrations at lower temperatures and GDGT provenance in many lacustrine settings remains uncertain. To address these issues, we examined surface sediments from 32 Antarctic, sub-Antarctic and Southern Chilean lakes. First, we quantified GDGT compositions present and then investigated modern-day environmental controls on GDGT composition. GDGTs were found in all 32 lakes studied. Branched GDGTs (brGDGTs) were dominant in 31 lakes and statistical analyses showed that their composition was strongly correlated with mean summer air temperature (MSAT) rather than pH, conductivity or water depth. Second, we developed the first regional brGDGT-temperature calibration for Antarctic and sub-Antarctic lakes based on four brGDGT compounds (GDGT-Ib, GDGT-II, GDGT-III and GDGT-IIIb). Of these, GDGT-IIIb proved particularly important in cold lacustrine environments. Our brGDGT-Antarctic temperature calibration dataset has an improved statistical performance at low temperatures compared to previous global calibrations (r2=0.83, RMSE=1.45°C, RMSEP-LOO=1.68°C, n=36 samples), highlighting the importance of basing palaeotemperature reconstructions on regional GDGT-temperature calibrations, especially if specific compounds lead to improved model performance. Finally, we applied the new Antarctic brGDGT-temperature calibration to two key lake records from the Antarctic Peninsula and South Georgia. In both, downcore temperature reconstructions show similarities to known Holocene warm periods, providing proof of concept for the new Antarctic calibration model.

Comparison of "E-Rater"[R] Automated Essay Scoring Model Calibration Methods Based on Distributional Targets

ERIC Educational Resources Information Center

Zhang, Mo; Williamson, David M.; Breyer, F. Jay; Trapani, Catherine

2012-01-01

This article describes two separate, related studies that provide insight into the effectiveness of "e-rater" score calibration methods based on different distributional targets. In the first study, we developed and evaluated a new type of "e-rater" scoring model that was cost-effective and applicable under conditions of absent human rating and…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tuo, Rui; Wu, C. F. Jeff

Many computer models contain unknown parameters which need to be estimated using physical observations. Furthermore, the calibration method based on Gaussian process models may lead to unreasonable estimate for imperfect computer models. In this work, we extend their study to calibration problems with stochastic physical data. We propose a novel method, called the L 2 calibration, and show its semiparametric efficiency. The conventional method of the ordinary least squares is also studied. Theoretical analysis shows that it is consistent but not efficient. Here, numerical examples show that the proposed method outperforms the existing ones.

Enhancing model prediction reliability through improved soil representation and constrained model auto calibration - A paired waterhsed study

USDA-ARS?s Scientific Manuscript database

Process based and distributed watershed models possess a large number of parameters that are not directly measured in field and need to be calibrated through matching modeled in-stream fluxes with monitored data. Recently, there have been waves of concern about the reliability of this common practic...

Mixture EMOS model for calibrating ensemble forecasts of wind speed.

PubMed

Baran, S; Lerch, S

2016-03-01

Ensemble model output statistics (EMOS) is a statistical tool for post-processing forecast ensembles of weather variables obtained from multiple runs of numerical weather prediction models in order to produce calibrated predictive probability density functions. The EMOS predictive probability density function is given by a parametric distribution with parameters depending on the ensemble forecasts. We propose an EMOS model for calibrating wind speed forecasts based on weighted mixtures of truncated normal (TN) and log-normal (LN) distributions where model parameters and component weights are estimated by optimizing the values of proper scoring rules over a rolling training period. The new model is tested on wind speed forecasts of the 50 member European Centre for Medium-range Weather Forecasts ensemble, the 11 member Aire Limitée Adaptation dynamique Développement International-Hungary Ensemble Prediction System ensemble of the Hungarian Meteorological Service, and the eight-member University of Washington mesoscale ensemble, and its predictive performance is compared with that of various benchmark EMOS models based on single parametric families and combinations thereof. The results indicate improved calibration of probabilistic and accuracy of point forecasts in comparison with the raw ensemble and climatological forecasts. The mixture EMOS model significantly outperforms the TN and LN EMOS methods; moreover, it provides better calibrated forecasts than the TN-LN combination model and offers an increased flexibility while avoiding covariate selection problems. © 2016 The Authors Environmetrics Published by JohnWiley & Sons Ltd.

Finite Element Model Calibration Approach for Area I-X

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Reaves, Mercedes C.; Buehrle, Ralph D.; Templeton, Justin D.; Gaspar, James L.; Lazor, Daniel R.; Parks, Russell A.; Bartolotta, Paul A.

2010-01-01

Ares I-X is a pathfinder vehicle concept under development by NASA to demonstrate a new class of launch vehicles. Although this vehicle is essentially a shell of what the Ares I vehicle will be, efforts are underway to model and calibrate the analytical models before its maiden flight. Work reported in this document will summarize the model calibration approach used including uncertainty quantification of vehicle responses and the use of non-conventional boundary conditions during component testing. Since finite element modeling is the primary modeling tool, the calibration process uses these models, often developed by different groups, to assess model deficiencies and to update parameters to reconcile test with predictions. Data for two major component tests and the flight vehicle are presented along with the calibration results. For calibration, sensitivity analysis is conducted using Analysis of Variance (ANOVA). To reduce the computational burden associated with ANOVA calculations, response surface models are used in lieu of computationally intensive finite element solutions. From the sensitivity studies, parameter importance is assessed as a function of frequency. In addition, the work presents an approach to evaluate the probability that a parameter set exists to reconcile test with analysis. Comparisons of pretest predictions of frequency response uncertainty bounds with measured data, results from the variance-based sensitivity analysis, and results from component test models with calibrated boundary stiffness models are all presented.

Finite Element Model Calibration Approach for Ares I-X

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Reaves, Mercedes C.; Buehrle, Ralph D.; Templeton, Justin D.; Lazor, Daniel R.; Gaspar, James L.; Parks, Russel A.; Bartolotta, Paul A.

2010-01-01

Ares I-X is a pathfinder vehicle concept under development by NASA to demonstrate a new class of launch vehicles. Although this vehicle is essentially a shell of what the Ares I vehicle will be, efforts are underway to model and calibrate the analytical models before its maiden flight. Work reported in this document will summarize the model calibration approach used including uncertainty quantification of vehicle responses and the use of nonconventional boundary conditions during component testing. Since finite element modeling is the primary modeling tool, the calibration process uses these models, often developed by different groups, to assess model deficiencies and to update parameters to reconcile test with predictions. Data for two major component tests and the flight vehicle are presented along with the calibration results. For calibration, sensitivity analysis is conducted using Analysis of Variance (ANOVA). To reduce the computational burden associated with ANOVA calculations, response surface models are used in lieu of computationally intensive finite element solutions. From the sensitivity studies, parameter importance is assessed as a function of frequency. In addition, the work presents an approach to evaluate the probability that a parameter set exists to reconcile test with analysis. Comparisons of pre-test predictions of frequency response uncertainty bounds with measured data, results from the variance-based sensitivity analysis, and results from component test models with calibrated boundary stiffness models are all presented.

Calibration of the 7—Equation Transition Model for High Reynolds Flows at Low Mach

NASA Astrophysics Data System (ADS)

Colonia, S.; Leble, V.; Steijl, R.; Barakos, G.

2016-09-01

The numerical simulation of flows over large-scale wind turbine blades without considering the transition from laminar to fully turbulent flow may result in incorrect estimates of the blade loads and performance. Thanks to its relative simplicity and promising results, the Local-Correlation based Transition Modelling concept represents a valid way to include transitional effects into practical CFD simulations. However, the model involves coefficients that need tuning. In this paper, the γ—equation transition model is assessed and calibrated, for a wide range of Reynolds numbers at low Mach, as needed for wind turbine applications. An aerofoil is used to evaluate the original model and calibrate it; while a large scale wind turbine blade is employed to show that the calibrated model can lead to reliable solutions for complex three-dimensional flows. The calibrated model shows promising results for both two-dimensional and three-dimensional flows, even if cross-flow instabilities are neglected.

Calibration of a stochastic health evolution model using NHIS data

NASA Astrophysics Data System (ADS)

Gupta, Aparna; Li, Zhisheng

2011-10-01

This paper presents and calibrates an individual's stochastic health evolution model. In this health evolution model, the uncertainty of health incidents is described by a stochastic process with a finite number of possible outcomes. We construct a comprehensive health status index (HSI) to describe an individual's health status, as well as a health risk factor system (RFS) to classify individuals into different risk groups. Based on the maximum likelihood estimation (MLE) method and the method of nonlinear least squares fitting, model calibration is formulated in terms of two mixed-integer nonlinear optimization problems. Using the National Health Interview Survey (NHIS) data, the model is calibrated for specific risk groups. Longitudinal data from the Health and Retirement Study (HRS) is used to validate the calibrated model, which displays good validation properties. The end goal of this paper is to provide a model and methodology, whose output can serve as a crucial component of decision support for strategic planning of health related financing and risk management.

LiDAR-IMU Time Delay Calibration Based on Iterative Closest Point and Iterated Sigma Point Kalman Filter.

PubMed

Liu, Wanli

2017-03-08

The time delay calibration between Light Detection and Ranging (LiDAR) and Inertial Measurement Units (IMUs) is an essential prerequisite for its applications. However, the correspondences between LiDAR and IMU measurements are usually unknown, and thus cannot be computed directly for the time delay calibration. In order to solve the problem of LiDAR-IMU time delay calibration, this paper presents a fusion method based on iterative closest point (ICP) and iterated sigma point Kalman filter (ISPKF), which combines the advantages of ICP and ISPKF. The ICP algorithm can precisely determine the unknown transformation between LiDAR-IMU; and the ISPKF algorithm can optimally estimate the time delay calibration parameters. First of all, the coordinate transformation from the LiDAR frame to the IMU frame is realized. Second, the measurement model and time delay error model of LiDAR and IMU are established. Third, the methodology of the ICP and ISPKF procedure is presented for LiDAR-IMU time delay calibration. Experimental results are presented that validate the proposed method and demonstrate the time delay error can be accurately calibrated.

Calibration and Finite Element Implementation of an Energy-Based Material Model for Shape Memory Alloys

NASA Astrophysics Data System (ADS)

Junker, Philipp; Hackl, Klaus

2016-09-01

Numerical simulations are a powerful tool to analyze the complex thermo-mechanically coupled material behavior of shape memory alloys during product engineering. The benefit of the simulations strongly depends on the quality of the underlying material model. In this contribution, we discuss a variational approach which is based solely on energetic considerations and demonstrate that unique calibration of such a model is sufficient to predict the material behavior at varying ambient temperature. In the beginning, we recall the necessary equations of the material model and explain the fundamental idea. Afterwards, we focus on the numerical implementation and provide all information that is needed for programing. Then, we show two different ways to calibrate the model and discuss the results. Furthermore, we show how this model is used during real-life industrial product engineering.

Improved calibration-based non-uniformity correction method for uncooled infrared camera

NASA Astrophysics Data System (ADS)

Liu, Chengwei; Sui, Xiubao

2017-08-01

With the latest improvements of microbolometer focal plane arrays (FPA), uncooled infrared (IR) cameras are becoming the most widely used devices in thermography, especially in handheld devices. However the influences derived from changing ambient condition and the non-uniform response of the sensors make it more difficult to correct the nonuniformity of uncooled infrared camera. In this paper, based on the infrared radiation characteristic in the TEC-less uncooled infrared camera, a novel model was proposed for calibration-based non-uniformity correction (NUC). In this model, we introduce the FPA temperature, together with the responses of microbolometer under different ambient temperature to calculate the correction parameters. Based on the proposed model, we can work out the correction parameters with the calibration measurements under controlled ambient condition and uniform blackbody. All correction parameters can be determined after the calibration process and then be used to correct the non-uniformity of the infrared camera in real time. This paper presents the detail of the compensation procedure and the performance of the proposed calibration-based non-uniformity correction method. And our method was evaluated on realistic IR images obtained by a 384x288 pixels uncooled long wave infrared (LWIR) camera operated under changed ambient condition. The results show that our method can exclude the influence caused by the changed ambient condition, and ensure that the infrared camera has a stable performance.

Global Sensitivity Analysis and Parameter Calibration for an Ecosystem Carbon Model

NASA Astrophysics Data System (ADS)

Safta, C.; Ricciuto, D. M.; Sargsyan, K.; Najm, H. N.; Debusschere, B.; Thornton, P. E.

2013-12-01

We present uncertainty quantification results for a process-based ecosystem carbon model. The model employs 18 parameters and is driven by meteorological data corresponding to years 1992-2006 at the Harvard Forest site. Daily Net Ecosystem Exchange (NEE) observations were available to calibrate the model parameters and test the performance of the model. Posterior distributions show good predictive capabilities for the calibrated model. A global sensitivity analysis was first performed to determine the important model parameters based on their contribution to the variance of NEE. We then proceed to calibrate the model parameters in a Bayesian framework. The daily discrepancies between measured and predicted NEE values were modeled as independent and identically distributed Gaussians with prescribed daily variance according to the recorded instrument error. All model parameters were assumed to have uninformative priors with bounds set according to expert opinion. The global sensitivity results show that the rate of leaf fall (LEAFALL) is responsible for approximately 25% of the total variance in the average NEE for 1992-2005. A set of 4 other parameters, Nitrogen use efficiency (NUE), base rate for maintenance respiration (BR_MR), growth respiration fraction (RG_FRAC), and allocation to plant stem pool (ASTEM) contribute between 5% and 12% to the variance in average NEE, while the rest of the parameters have smaller contributions. The posterior distributions, sampled with a Markov Chain Monte Carlo algorithm, exhibit significant correlations between model parameters. However LEAFALL, the most important parameter for the average NEE, is not informed by the observational data, while less important parameters show significant updates between their prior and posterior densities. The Fisher information matrix values, indicating which parameters are most informed by the experimental observations, are examined to augment the comparison between the calibration and global sensitivity analysis results.

[Effect of algorithms for calibration set selection on quantitatively determining asiaticoside content in Centella total glucosides by near infrared spectroscopy].

PubMed

Zhan, Xue-yan; Zhao, Na; Lin, Zhao-zhou; Wu, Zhi-sheng; Yuan, Rui-juan; Qiao, Yan-jiang

2014-12-01

The appropriate algorithm for calibration set selection was one of the key technologies for a good NIR quantitative model. There are different algorithms for calibration set selection, such as Random Sampling (RS) algorithm, Conventional Selection (CS) algorithm, Kennard-Stone(KS) algorithm and Sample set Portioning based on joint x-y distance (SPXY) algorithm, et al. However, there lack systematic comparisons between two algorithms of the above algorithms. The NIR quantitative models to determine the asiaticoside content in Centella total glucosides were established in the present paper, of which 7 indexes were classified and selected, and the effects of CS algorithm, KS algorithm and SPXY algorithm for calibration set selection on the accuracy and robustness of NIR quantitative models were investigated. The accuracy indexes of NIR quantitative models with calibration set selected by SPXY algorithm were significantly different from that with calibration set selected by CS algorithm or KS algorithm, while the robustness indexes, such as RMSECV and |RMSEP-RMSEC|, were not significantly different. Therefore, SPXY algorithm for calibration set selection could improve the predicative accuracy of NIR quantitative models to determine asiaticoside content in Centella total glucosides, and have no significant effect on the robustness of the models, which provides a reference to determine the appropriate algorithm for calibration set selection when NIR quantitative models are established for the solid system of traditional Chinese medcine.

A holistic calibration method with iterative distortion compensation for stereo deflectometry

NASA Astrophysics Data System (ADS)

Xu, Yongjia; Gao, Feng; Zhang, Zonghua; Jiang, Xiangqian

2018-07-01

This paper presents a novel holistic calibration method for stereo deflectometry system to improve the system measurement accuracy. The reconstruction result of stereo deflectometry is integrated with the calculated normal data of the measured surface. The calculation accuracy of the normal data is seriously influenced by the calibration accuracy of the geometrical relationship of the stereo deflectometry system. Conventional calibration approaches introduce form error to the system due to inaccurate imaging model and distortion elimination. The proposed calibration method compensates system distortion based on an iterative algorithm instead of the conventional distortion mathematical model. The initial value of the system parameters are calculated from the fringe patterns displayed on the systemic LCD screen through a reflection of a markless flat mirror. An iterative algorithm is proposed to compensate system distortion and optimize camera imaging parameters and system geometrical relation parameters based on a cost function. Both simulation work and experimental results show the proposed calibration method can significantly improve the calibration and measurement accuracy of a stereo deflectometry. The PV (peak value) of measurement error of a flat mirror can be reduced to 69.7 nm by applying the proposed method from 282 nm obtained with the conventional calibration approach.

Application of iterative robust model-based optimal experimental design for the calibration of biocatalytic models.

PubMed

Van Daele, Timothy; Gernaey, Krist V; Ringborg, Rolf H; Börner, Tim; Heintz, Søren; Van Hauwermeiren, Daan; Grey, Carl; Krühne, Ulrich; Adlercreutz, Patrick; Nopens, Ingmar

2017-09-01

The aim of model calibration is to estimate unique parameter values from available experimental data, here applied to a biocatalytic process. The traditional approach of first gathering data followed by performing a model calibration is inefficient, since the information gathered during experimentation is not actively used to optimize the experimental design. By applying an iterative robust model-based optimal experimental design, the limited amount of data collected is used to design additional informative experiments. The algorithm is used here to calibrate the initial reaction rate of an ω-transaminase catalyzed reaction in a more accurate way. The parameter confidence region estimated from the Fisher Information Matrix is compared with the likelihood confidence region, which is not only more accurate but also a computationally more expensive method. As a result, an important deviation between both approaches is found, confirming that linearization methods should be applied with care for nonlinear models. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1278-1293, 2017. © 2017 American Institute of Chemical Engineers.

Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

DOE PAGES

Xi, Maolong; Lu, Dan; Gui, Dongwei; ...

2016-11-27

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so asmore » to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO 3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.« less

Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

NASA Astrophysics Data System (ADS)

Xi, Maolong; Lu, Dan; Gui, Dongwei; Qi, Zhiming; Zhang, Guannan

2017-01-01

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so as to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.

Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xi, Maolong; Lu, Dan; Gui, Dongwei

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so asmore » to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO 3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.« less

Evaluation of Hydrologic Simulations Developed Using Multi-Model Synthesis and Remotely-Sensed Data within a Portfolio of Calibration Strategies

NASA Astrophysics Data System (ADS)

Lafontaine, J.; Hay, L.; Markstrom, S. L.

2016-12-01

The United States Geological Survey (USGS) has developed a National Hydrologic Model (NHM) to support coordinated, comprehensive and consistent hydrologic model development, and facilitate the application of hydrologic simulations within the conterminous United States (CONUS). As many stream reaches in the CONUS are either not gaged, or are substantially impacted by water use or flow regulation, ancillary information must be used to determine reasonable parameter estimations for streamflow simulations. Hydrologic models for 1,576 gaged watersheds across the CONUS were developed to test the feasibility of improving streamflow simulations linking physically-based hydrologic models with remotely-sensed data products (i.e. snow water equivalent). Initially, the physically-based models were calibrated to measured streamflow data to provide a baseline for comparison across multiple calibration strategy tests. In addition, not all ancillary datasets are appropriate for application to all parts of the CONUS (e.g. snow water equivalent in the southeastern U.S., where snow is a rarity). As it is not expected that any one data product or model simulation will be sufficient for representing hydrologic behavior across the entire CONUS, a systematic evaluation of which data products improve hydrologic simulations for various regions across the CONUS was performed. The resulting portfolio of calibration strategies can be used to guide selection of an appropriate combination of modeled and measured information for hydrologic model development and calibration. In addition, these calibration strategies have been developed to be flexible so that new data products can be assimilated. This analysis provides a foundation to understand how well models work when sufficient streamflow data are not available and could be used to further inform hydrologic model parameter development for ungaged areas.

Optics-Only Calibration of a Neural-Net Based Optical NDE Method for Structural Health Monitoring

NASA Technical Reports Server (NTRS)

Decker, Arthur J.

2004-01-01

A calibration process is presented that uses optical measurements alone to calibrate a neural-net based NDE method. The method itself detects small changes in the vibration mode shapes of structures. The optics-only calibration process confirms previous work that the sensitivity to vibration-amplitude changes can be as small as 10 nanometers. A more practical value in an NDE service laboratory is shown to be 50 nanometers. Both model-generated and experimental calibrations are demonstrated using two implementations of the calibration technique. The implementations are based on previously published demonstrations of the NDE method and an alternative calibration procedure that depends on comparing neural-net and point sensor measurements. The optics-only calibration method, unlike the alternative method, does not require modifications of the structure being tested or the creation of calibration objects. The calibration process can be used to test improvements in the NDE process and to develop a vibration-mode-independence of damagedetection sensitivity. The calibration effort was intended to support NASA s objective to promote safety in the operations of ground test facilities or aviation safety, in general, by allowing the detection of the gradual onset of structural changes and damage.

Comparative study on ATR-FTIR calibration models for monitoring solution concentration in cooling crystallization

NASA Astrophysics Data System (ADS)

Zhang, Fangkun; Liu, Tao; Wang, Xue Z.; Liu, Jingxiang; Jiang, Xiaobin

2017-02-01

In this paper calibration model building based on using an ATR-FTIR spectroscopy is investigated for in-situ measurement of the solution concentration during a cooling crystallization process. The cooling crystallization of L-glutamic Acid (LGA) as a case is studied here. It was found that using the metastable zone (MSZ) data for model calibration can guarantee the prediction accuracy for monitoring the operating window of cooling crystallization, compared to the usage of undersaturated zone (USZ) spectra for model building as traditionally practiced. Calibration experiments were made for LGA solution under different concentrations. Four candidate calibration models were established using different zone data for comparison, by using a multivariate partial least-squares (PLS) regression algorithm for the collected spectra together with the corresponding temperature values. Experiments under different process conditions including the changes of solution concentration and operating temperature were conducted. The results indicate that using the MSZ spectra for model calibration can give more accurate prediction of the solution concentration during the crystallization process, while maintaining accuracy in changing the operating temperature. The primary reason of prediction error was clarified as spectral nonlinearity for in-situ measurement between USZ and MSZ. In addition, an LGA cooling crystallization experiment was performed to verify the sensitivity of these calibration models for monitoring the crystal growth process.

Multi-model analysis of terrestrial carbon cycles in Japan: limitations and implications of model calibration using eddy flux observations

NASA Astrophysics Data System (ADS)

Ichii, K.; Suzuki, T.; Kato, T.; Ito, A.; Hajima, T.; Ueyama, M.; Sasai, T.; Hirata, R.; Saigusa, N.; Ohtani, Y.; Takagi, K.

2010-07-01

Terrestrial biosphere models show large differences when simulating carbon and water cycles, and reducing these differences is a priority for developing more accurate estimates of the condition of terrestrial ecosystems and future climate change. To reduce uncertainties and improve the understanding of their carbon budgets, we investigated the utility of the eddy flux datasets to improve model simulations and reduce variabilities among multi-model outputs of terrestrial biosphere models in Japan. Using 9 terrestrial biosphere models (Support Vector Machine - based regressions, TOPS, CASA, VISIT, Biome-BGC, DAYCENT, SEIB, LPJ, and TRIFFID), we conducted two simulations: (1) point simulations at four eddy flux sites in Japan and (2) spatial simulations for Japan with a default model (based on original settings) and a modified model (based on model parameter tuning using eddy flux data). Generally, models using default model settings showed large deviations in model outputs from observation with large model-by-model variability. However, after we calibrated the model parameters using eddy flux data (GPP, RE and NEP), most models successfully simulated seasonal variations in the carbon cycle, with less variability among models. We also found that interannual variations in the carbon cycle are mostly consistent among models and observations. Spatial analysis also showed a large reduction in the variability among model outputs. This study demonstrated that careful validation and calibration of models with available eddy flux data reduced model-by-model differences. Yet, site history, analysis of model structure changes, and more objective procedure of model calibration should be included in the further analysis.

Comparison between a model-based and a conventional pyramid sensor reconstructor.

PubMed

Korkiakoski, Visa; Vérinaud, Christophe; Le Louarn, Miska; Conan, Rodolphe

2007-08-20

A model of a non-modulated pyramid wavefront sensor (P-WFS) based on Fourier optics has been presented. Linearizations of the model represented as Jacobian matrices are used to improve the P-WFS phase estimates. It has been shown in simulations that a linear approximation of the P-WFS is sufficient in closed-loop adaptive optics. Also a method to compute model-based synthetic P-WFS command matrices is shown, and its performance is compared to the conventional calibration. It was observed that in poor visibility the new calibration is better than the conventional.

Underwater 3D Surface Measurement Using Fringe Projection Based Scanning Devices

PubMed Central

Bräuer-Burchardt, Christian; Heinze, Matthias; Schmidt, Ingo; Kühmstedt, Peter; Notni, Gunther

2015-01-01

In this work we show the principle of optical 3D surface measurements based on the fringe projection technique for underwater applications. The challenges of underwater use of this technique are shown and discussed in comparison with the classical application. We describe an extended camera model which takes refraction effects into account as well as a proposal of an effective, low-effort calibration procedure for underwater optical stereo scanners. This calibration technique combines a classical air calibration based on the pinhole model with ray-based modeling and requires only a few underwater recordings of an object of known length and a planar surface. We demonstrate a new underwater 3D scanning device based on the fringe projection technique. It has a weight of about 10 kg and the maximal water depth for application of the scanner is 40 m. It covers an underwater measurement volume of 250 mm × 200 mm × 120 mm. The surface of the measurement objects is captured with a lateral resolution of 150 μm in a third of a second. Calibration evaluation results are presented and examples of first underwater measurements are given. PMID:26703624

Adequacy of satellite derived rainfall data for stream flow modeling

USGS Publications Warehouse

Artan, G.; Gadain, Hussein; Smith, Jodie; Asante, Kwasi; Bandaragoda, C.J.; Verdin, J.P.

2007-01-01

Floods are the most common and widespread climate-related hazard on Earth. Flood forecasting can reduce the death toll associated with floods. Satellites offer effective and economical means for calculating areal rainfall estimates in sparsely gauged regions. However, satellite-based rainfall estimates have had limited use in flood forecasting and hydrologic stream flow modeling because the rainfall estimates were considered to be unreliable. In this study we present the calibration and validation results from a spatially distributed hydrologic model driven by daily satellite-based estimates of rainfall for sub-basins of the Nile and Mekong Rivers. The results demonstrate the usefulness of remotely sensed precipitation data for hydrologic modeling when the hydrologic model is calibrated with such data. However, the remotely sensed rainfall estimates cannot be used confidently with hydrologic models that are calibrated with rain gauge measured rainfall, unless the model is recalibrated. ?? Springer Science+Business Media, Inc. 2007.

On the importance of appropriate precipitation gauge catch correction for hydrological modelling at mid to high latitudes

NASA Astrophysics Data System (ADS)

Stisen, S.; Højberg, A. L.; Troldborg, L.; Refsgaard, J. C.; Christensen, B. S. B.; Olsen, M.; Henriksen, H. J.

2012-11-01

Precipitation gauge catch correction is often given very little attention in hydrological modelling compared to model parameter calibration. This is critical because significant precipitation biases often make the calibration exercise pointless, especially when supposedly physically-based models are in play. This study addresses the general importance of appropriate precipitation catch correction through a detailed modelling exercise. An existing precipitation gauge catch correction method addressing solid and liquid precipitation is applied, both as national mean monthly correction factors based on a historic 30 yr record and as gridded daily correction factors based on local daily observations of wind speed and temperature. The two methods, named the historic mean monthly (HMM) and the time-space variable (TSV) correction, resulted in different winter precipitation rates for the period 1990-2010. The resulting precipitation datasets were evaluated through the comprehensive Danish National Water Resources model (DK-Model), revealing major differences in both model performance and optimised model parameter sets. Simulated stream discharge is improved significantly when introducing the TSV correction, whereas the simulated hydraulic heads and multi-annual water balances performed similarly due to recalibration adjusting model parameters to compensate for input biases. The resulting optimised model parameters are much more physically plausible for the model based on the TSV correction of precipitation. A proxy-basin test where calibrated DK-Model parameters were transferred to another region without site specific calibration showed better performance for parameter values based on the TSV correction. Similarly, the performances of the TSV correction method were superior when considering two single years with a much dryer and a much wetter winter, respectively, as compared to the winters in the calibration period (differential split-sample tests). We conclude that TSV precipitation correction should be carried out for studies requiring a sound dynamic description of hydrological processes, and it is of particular importance when using hydrological models to make predictions for future climates when the snow/rain composition will differ from the past climate. This conclusion is expected to be applicable for mid to high latitudes, especially in coastal climates where winter precipitation types (solid/liquid) fluctuate significantly, causing climatological mean correction factors to be inadequate.

An analytic model for accurate spring constant calibration of rectangular atomic force microscope cantilevers.

PubMed

Li, Rui; Ye, Hongfei; Zhang, Weisheng; Ma, Guojun; Su, Yewang

2015-10-29

Spring constant calibration of the atomic force microscope (AFM) cantilever is of fundamental importance for quantifying the force between the AFM cantilever tip and the sample. The calibration within the framework of thin plate theory undoubtedly has a higher accuracy and broader scope than that within the well-established beam theory. However, thin plate theory-based accurate analytic determination of the constant has been perceived as an extremely difficult issue. In this paper, we implement the thin plate theory-based analytic modeling for the static behavior of rectangular AFM cantilevers, which reveals that the three-dimensional effect and Poisson effect play important roles in accurate determination of the spring constants. A quantitative scaling law is found that the normalized spring constant depends only on the Poisson's ratio, normalized dimension and normalized load coordinate. Both the literature and our refined finite element model validate the present results. The developed model is expected to serve as the benchmark for accurate calibration of rectangular AFM cantilevers.

Stochastic calibration and learning in nonstationary hydroeconomic models

NASA Astrophysics Data System (ADS)

Maneta, M. P.; Howitt, R.

2014-05-01

Concern about water scarcity and adverse climate events over agricultural regions has motivated a number of efforts to develop operational integrated hydroeconomic models to guide adaptation and optimal use of water. Once calibrated, these models are used for water management and analysis assuming they remain valid under future conditions. In this paper, we present and demonstrate a methodology that permits the recursive calibration of economic models of agricultural production from noisy but frequently available data. We use a standard economic calibration approach, namely positive mathematical programming, integrated in a data assimilation algorithm based on the ensemble Kalman filter equations to identify the economic model parameters. A moving average kernel ensures that new and past information on agricultural activity are blended during the calibration process, avoiding loss of information and overcalibration for the conditions of a single year. A regularization constraint akin to the standard Tikhonov regularization is included in the filter to ensure its stability even in the presence of parameters with low sensitivity to observations. The results show that the implementation of the PMP methodology within a data assimilation framework based on the enKF equations is an effective method to calibrate models of agricultural production even with noisy information. The recursive nature of the method incorporates new information as an added value to the known previous observations of agricultural activity without the need to store historical information. The robustness of the method opens the door to the use of new remote sensing algorithms for operational water management.

Calibration and prediction of removal function in magnetorheological finishing.

PubMed

Dai, Yifan; Song, Ci; Peng, Xiaoqiang; Shi, Feng

2010-01-20

A calibrated and predictive model of the removal function has been established based on the analysis of a magnetorheological finishing (MRF) process. By introducing an efficiency coefficient of the removal function, the model can be used to calibrate the removal function in a MRF figuring process and to accurately predict the removal function of a workpiece to be polished whose material is different from the spot part. Its correctness and feasibility have been validated by simulations. Furthermore, applying this model to the MRF figuring experiments, the efficiency coefficient of the removal function can be identified accurately to make the MRF figuring process deterministic and controllable. Therefore, all the results indicate that the calibrated and predictive model of the removal function can improve the finishing determinacy and increase the model applicability in a MRF process.

Estimation of k-ε parameters using surrogate models and jet-in-crossflow data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lefantzi, Sophia; Ray, Jaideep; Arunajatesan, Srinivasan

2014-11-01

We demonstrate a Bayesian method that can be used to calibrate computationally expensive 3D RANS (Reynolds Av- eraged Navier Stokes) models with complex response surfaces. Such calibrations, conditioned on experimental data, can yield turbulence model parameters as probability density functions (PDF), concisely capturing the uncertainty in the parameter estimates. Methods such as Markov chain Monte Carlo (MCMC) estimate the PDF by sampling, with each sample requiring a run of the RANS model. Consequently a quick-running surrogate is used instead to the RANS simulator. The surrogate can be very difficult to design if the model's response i.e., the dependence of themore » calibration variable (the observable) on the parameter being estimated is complex. We show how the training data used to construct the surrogate can be employed to isolate a promising and physically realistic part of the parameter space, within which the response is well-behaved and easily modeled. We design a classifier, based on treed linear models, to model the "well-behaved region". This classifier serves as a prior in a Bayesian calibration study aimed at estimating 3 k - ε parameters ( C μ, C ε2 , C ε1 ) from experimental data of a transonic jet-in-crossflow interaction. The robustness of the calibration is investigated by checking its predictions of variables not included in the cal- ibration data. We also check the limit of applicability of the calibration by testing at off-calibration flow regimes. We find that calibration yield turbulence model parameters which predict the flowfield far better than when the nomi- nal values of the parameters are used. Substantial improvements are still obtained when we use the calibrated RANS model to predict jet-in-crossflow at Mach numbers and jet strengths quite different from those used to generate the ex- perimental (calibration) data. Thus the primary reason for poor predictive skill of RANS, when using nominal values of the turbulence model parameters, was parametric uncertainty, which was rectified by calibration. Post-calibration, the dominant contribution to model inaccuraries are due to the structural errors in RANS.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shen, Bo; Abdelaziz, Omar; Shrestha, Som S.

Based on the laboratory investigation in FY16, for R-22 and R-410A alternative low GWP refrigerants in two baseline rooftop air conditioners (RTU), we used the DOE/ORNL Heat Pump Design Model to model the two RTUs and calibrated the models against the experimental data. Using the calibrated equipment models, we compared the compressor efficiencies, heat exchanger performances. An efficiency-based compressor mapping method was developed, which is able to predict compressor performances of the alternative low GWP refrigerants accurately. Extensive model-based optimizations were conducted to provide a fair comparison between all the low GWP candidates by selecting their preferred configurations at themore » same cooling capacity and compressor efficiencies.« less

Hierarchical calibration and validation of computational fluid dynamics models for solid sorbent-based carbon capture

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lai, Canhai; Xu, Zhijie; Pan, Wenxiao

2016-01-01

To quantify the predictive confidence of a solid sorbent-based carbon capture design, a hierarchical validation methodology—consisting of basic unit problems with increasing physical complexity coupled with filtered model-based geometric upscaling has been developed and implemented. This paper describes the computational fluid dynamics (CFD) multi-phase reactive flow simulations and the associated data flows among different unit problems performed within the said hierarchical validation approach. The bench-top experiments used in this calibration and validation effort were carefully designed to follow the desired simple-to-complex unit problem hierarchy, with corresponding data acquisition to support model parameters calibrations at each unit problem level. A Bayesianmore » calibration procedure is employed and the posterior model parameter distributions obtained at one unit-problem level are used as prior distributions for the same parameters in the next-tier simulations. Overall, the results have demonstrated that the multiphase reactive flow models within MFIX can be used to capture the bed pressure, temperature, CO2 capture capacity, and kinetics with quantitative accuracy. The CFD modeling methodology and associated uncertainty quantification techniques presented herein offer a solid framework for estimating the predictive confidence in the virtual scale up of a larger carbon capture device.« less

Sample classification for improved performance of PLS models applied to the quality control of deep-frying oils of different botanic origins analyzed using ATR-FTIR spectroscopy.

PubMed

Kuligowski, Julia; Carrión, David; Quintás, Guillermo; Garrigues, Salvador; de la Guardia, Miguel

2011-01-01

The selection of an appropriate calibration set is a critical step in multivariate method development. In this work, the effect of using different calibration sets, based on a previous classification of unknown samples, on the partial least squares (PLS) regression model performance has been discussed. As an example, attenuated total reflection (ATR) mid-infrared spectra of deep-fried vegetable oil samples from three botanical origins (olive, sunflower, and corn oil), with increasing polymerized triacylglyceride (PTG) content induced by a deep-frying process were employed. The use of a one-class-classifier partial least squares-discriminant analysis (PLS-DA) and a rooted binary directed acyclic graph tree provided accurate oil classification. Oil samples fried without foodstuff could be classified correctly, independent of their PTG content. However, class separation of oil samples fried with foodstuff, was less evident. The combined use of double-cross model validation with permutation testing was used to validate the obtained PLS-DA classification models, confirming the results. To discuss the usefulness of the selection of an appropriate PLS calibration set, the PTG content was determined by calculating a PLS model based on the previously selected classes. In comparison to a PLS model calculated using a pooled calibration set containing samples from all classes, the root mean square error of prediction could be improved significantly using PLS models based on the selected calibration sets using PLS-DA, ranging between 1.06 and 2.91% (w/w).

Base flow calibration in a global hydrological model

NASA Astrophysics Data System (ADS)

van Beek, L. P.; Bierkens, M. F.

2006-12-01

Base flow constitutes an important water resource in many parts of the world. Its provenance and yield over time are governed by the storage capacity of local aquifers and the internal drainage paths, which are difficult to capture at the global scale. To represent the spatial and temporal variability in base flow adequately in a distributed global model at 0.5 degree resolution, we resorted to the conceptual model of aquifer storage of Kraaijenhoff- van de Leur (1958) that yields the reservoir coefficient for a linear groundwater store. This model was parameterised using global information on drainage density, climatology and lithology. Initial estimates of aquifer thickness, permeability and specific porosity from literature were linked to the latter two categories and calibrated to low flow data by means of simulated annealing so as to conserve the ordinal information contained by them. The observations used stem from the RivDis dataset of monthly discharge. From this dataset 324 stations were selected with at least 10 years of observations in the period 1958-1991 and an areal coverage of at least 10 cells of 0.5 degree. The dataset was split between basins into a calibration and validation set whilst preserving a representative distribution of lithology types and climate zones. Optimisation involved minimising the absolute differences between the simulated base flow and the lowest 10% of the observed monthly discharge. Subsequently, the reliability of the calibrated parameters was tested by reversing the calibration and validation sets.

Researches on hazard avoidance cameras calibration of Lunar Rover

NASA Astrophysics Data System (ADS)

Li, Chunyan; Wang, Li; Lu, Xin; Chen, Jihua; Fan, Shenghong

2017-11-01

Lunar Lander and Rover of China will be launched in 2013. It will finish the mission targets of lunar soft landing and patrol exploration. Lunar Rover has forward facing stereo camera pair (Hazcams) for hazard avoidance. Hazcams calibration is essential for stereo vision. The Hazcam optics are f-theta fish-eye lenses with a 120°×120° horizontal/vertical field of view (FOV) and a 170° diagonal FOV. They introduce significant distortion in images and the acquired images are quite warped, which makes conventional camera calibration algorithms no longer work well. A photogrammetric calibration method of geometric model for the type of optical fish-eye constructions is investigated in this paper. In the method, Hazcams model is represented by collinearity equations with interior orientation and exterior orientation parameters [1] [2]. For high-precision applications, the accurate calibration model is formulated with the radial symmetric distortion and the decentering distortion as well as parameters to model affinity and shear based on the fisheye deformation model [3] [4]. The proposed method has been applied to the stereo camera calibration system for Lunar Rover.

The site-scale saturated zone flow model for Yucca Mountain: Calibration of different conceptual models and their impact on flow paths

USGS Publications Warehouse

Zyvoloski, G.; Kwicklis, E.; Eddebbarh, A.-A.; Arnold, B.; Faunt, C.; Robinson, B.A.

2003-01-01

This paper presents several different conceptual models of the Large Hydraulic Gradient (LHG) region north of Yucca Mountain and describes the impact of those models on groundwater flow near the potential high-level repository site. The results are based on a numerical model of site-scale saturated zone beneath Yucca Mountain. This model is used for performance assessment predictions of radionuclide transport and to guide future data collection and modeling activities. The numerical model is calibrated by matching available water level measurements using parameter estimation techniques, along with more informal comparisons of the model to hydrologic and geochemical information. The model software (hydrologic simulation code FEHM and parameter estimation software PEST) and model setup allows for efficient calibration of multiple conceptual models. Until now, the Large Hydraulic Gradient has been simulated using a low-permeability, east-west oriented feature, even though direct evidence for this feature is lacking. In addition to this model, we investigate and calibrate three additional conceptual models of the Large Hydraulic Gradient, all of which are based on a presumed zone of hydrothermal chemical alteration north of Yucca Mountain. After examining the heads and permeabilities obtained from the calibrated models, we present particle pathways from the potential repository that record differences in the predicted groundwater flow regime. The results show that Large Hydraulic Gradient can be represented with the alternate conceptual models that include the hydrothermally altered zone. The predicted pathways are mildly sensitive to the choice of the conceptual model and more sensitive to the quality of calibration in the vicinity on the repository. These differences are most likely due to different degrees of fit of model to data, and do not represent important differences in hydrologic conditions for the different conceptual models. ?? 2002 Elsevier Science B.V. All rights reserved.

ANN-based calibration model of FTIR used in transformer online monitoring

NASA Astrophysics Data System (ADS)

Li, Honglei; Liu, Xian-yong; Zhou, Fangjie; Tan, Kexiong

2005-02-01

Recently, chromatography column and gas sensor have been used in online monitoring device of dissolved gases in transformer oil. But some disadvantages still exist in these devices: consumption of carrier gas, requirement of calibration, etc. Since FTIR has high accuracy, consume no carrier gas and require no calibration, the researcher studied the application of FTIR in such monitoring device. Experiments of "Flow gas method" were designed, and spectrum of mixture composed of different gases was collected with A BOMEM MB104 FTIR Spectrometer. A key question in the application of FTIR is that: the absorbance spectrum of 3 fault key gases, including C2H4, CH4 and C2H6, are overlapped seriously at 2700~3400cm-1. Because Absorbance Law is no longer appropriate, a nonlinear calibration model based on BP ANN was setup to in the quantitative analysis. The height absorbance of C2H4, CH4 and C2H6 were adopted as quantitative feature, and all the data were normalized before training the ANN. Computing results show that the calibration model can effectively eliminate the cross disturbance to measurement.

Multi-Dimensional Calibration of Impact Dynamic Models

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Reaves, Mercedes C.; Annett, Martin S.; Jackson, Karen E.

2011-01-01

NASA Langley, under the Subsonic Rotary Wing Program, recently completed two helicopter tests in support of an in-house effort to study crashworthiness. As part of this effort, work is on-going to investigate model calibration approaches and calibration metrics for impact dynamics models. Model calibration of impact dynamics problems has traditionally assessed model adequacy by comparing time histories from analytical predictions to test at only a few critical locations. Although this approach provides for a direct measure of the model predictive capability, overall system behavior is only qualitatively assessed using full vehicle animations. In order to understand the spatial and temporal relationships of impact loads as they migrate throughout the structure, a more quantitative approach is needed. In this work impact shapes derived from simulated time history data are used to recommend sensor placement and to assess model adequacy using time based metrics and orthogonality multi-dimensional metrics. An approach for model calibration is presented that includes metric definitions, uncertainty bounds, parameter sensitivity, and numerical optimization to estimate parameters to reconcile test with analysis. The process is illustrated using simulated experiment data.

Conceptualization and calibration of anisotropic, dynamic alluvial systems: Pitfalls and biases in current modelling practices

NASA Astrophysics Data System (ADS)

Gianni, Guillaume; Doherty, John; Perrochet, Pierre; Brunner, Philip

2017-04-01

Physical properties of alluvial environments are typically featuring a high degree of anisotropy and are characterized by dynamic interactions between the surface and the subsurface. A literature review on current modelling practice shows that hydrogeological models are often calibrated using isotropic hydraulic conductivity fields and steady state conditions. We aim at understanding how these simplifications affect the predictions of hydraulic heads and exchange fluxes using fully coupled, physically based synthetic models and advanced calibration approaches. Specifically, we present an analysis of the information content provided by averaged, steady state hydraulic data compared to transient data with respect to the determination of aquifer hydraulic properties. We show that the information content in average hydraulic heads is insufficient to inform anisotropic properties of alluvial aquifers and can lead to important biases on the calibrated parameters. We further explore the consequences of these biases on predictions of fluxes and water table dynamics. The results of this synthetic analysis are considered in the calibration of a highly dynamic and anisotropic alluvial aquifer system in Switzerland (the Rhône River). The results of the synthetic and real-world modelling and calibration exercises provide insight on future data acquisition, modelling and calibration strategies for these environments.

Calibration of two complex ecosystem models with different likelihood functions

NASA Astrophysics Data System (ADS)

Hidy, Dóra; Haszpra, László; Pintér, Krisztina; Nagy, Zoltán; Barcza, Zoltán

2014-05-01

The biosphere is a sensitive carbon reservoir. Terrestrial ecosystems were approximately carbon neutral during the past centuries, but they became net carbon sinks due to climate change induced environmental change and associated CO2 fertilization effect of the atmosphere. Model studies and measurements indicate that the biospheric carbon sink can saturate in the future due to ongoing climate change which can act as a positive feedback. Robustness of carbon cycle models is a key issue when trying to choose the appropriate model for decision support. The input parameters of the process-based models are decisive regarding the model output. At the same time there are several input parameters for which accurate values are hard to obtain directly from experiments or no local measurements are available. Due to the uncertainty associated with the unknown model parameters significant bias can be experienced if the model is used to simulate the carbon and nitrogen cycle components of different ecosystems. In order to improve model performance the unknown model parameters has to be estimated. We developed a multi-objective, two-step calibration method based on Bayesian approach in order to estimate the unknown parameters of PaSim and Biome-BGC models. Biome-BGC and PaSim are a widely used biogeochemical models that simulate the storage and flux of water, carbon, and nitrogen between the ecosystem and the atmosphere, and within the components of the terrestrial ecosystems (in this research the developed version of Biome-BGC is used which is referred as BBGC MuSo). Both models were calibrated regardless the simulated processes and type of model parameters. The calibration procedure is based on the comparison of measured data with simulated results via calculating a likelihood function (degree of goodness-of-fit between simulated and measured data). In our research different likelihood function formulations were used in order to examine the effect of the different model goodness metric on calibration. The different likelihoods are different functions of RMSE (root mean squared error) weighted by measurement uncertainty: exponential / linear / quadratic / linear normalized by correlation. As a first calibration step sensitivity analysis was performed in order to select the influential parameters which have strong effect on the output data. In the second calibration step only the sensitive parameters were calibrated (optimal values and confidence intervals were calculated). In case of PaSim more parameters were found responsible for the 95% of the output data variance than is case of BBGC MuSo. Analysis of the results of the optimized models revealed that the exponential likelihood estimation proved to be the most robust (best model simulation with optimized parameter, highest confidence interval increase). The cross-validation of the model simulations can help in constraining the highly uncertain greenhouse gas budget of grasslands.

Basin-scale geothermal model calibration: experience from the Perth Basin, Australia

NASA Astrophysics Data System (ADS)

Wellmann, Florian; Reid, Lynn

2014-05-01

The calibration of large-scale geothermal models for entire sedimentary basins is challenging as direct measurements of rock properties and subsurface temperatures are commonly scarce and the basal boundary conditions poorly constrained. Instead of the often applied "trial-and-error" manual model calibration, we examine here if we can gain additional insight into parameter sensitivities and model uncertainty with a model analysis and calibration study. Our geothermal model is based on a high-resolution full 3-D geological model, covering an area of more than 100,000 square kilometers and extending to a depth of 55 kilometers. The model contains all major faults (>80 ) and geological units (13) for the entire basin. This geological model is discretised into a rectilinear mesh with a lateral resolution of 500 x 500 m, and a variable resolution at depth. The highest resolution of 25 m is applied to a depth range of 1000-3000 m where most temperature measurements are available. The entire discretised model consists of approximately 50 million cells. The top thermal boundary condition is derived from surface temperature measurements on land and ocean floor. The base of the model extents below the Moho, and we apply the heat flux over the Moho as a basal heat flux boundary condition. Rock properties (thermal conductivity, porosity, and heat production) have been compiled from several existing data sets. The conductive geothermal forward simulation is performed with SHEMAT, and we then use the stand-alone capabilities of iTOUGH2 for sensitivity analysis and model calibration. Simulated temperatures are compared to 130 quality weighted bottom hole temperature measurements. The sensitivity analysis provided a clear insight into the most sensitive parameters and parameter correlations. This proved to be of value as strong correlations, for example between basal heat flux and heat production in deep geological units, can significantly influence the model calibration procedure. The calibration resulted in a better determination of subsurface temperatures, and, in addition, provided an insight into model quality. Furthermore, a detailed analysis of the measurements used for calibration highlighted potential outliers, and limitations with the model assumptions. Extending the previously existing large-scale geothermal simulation with iTOUGH2 provided us with a valuable insight into the sensitive parameters and data in the model, which would clearly not be possible with a simple trial-and-error calibration method. Using the gained knowledge, future work will include more detailed studies on the influence of advection and convection.

Spectral characterization and calibration of AOTF spectrometers and hyper-spectral imaging system

NASA Astrophysics Data System (ADS)

Katrašnik, Jaka; Pernuš, Franjo; Likar, Boštjan

2010-02-01

The goal of this article is to present a novel method for spectral characterization and calibration of spectrometers and hyper-spectral imaging systems based on non-collinear acousto-optical tunable filters. The method characterizes the spectral tuning curve (frequency-wavelength characteristic) of the AOTF (Acousto-Optic Tunable Filter) filter by matching the acquired and modeled spectra of the HgAr calibration lamp, which emits line spectrum that can be well modeled via AOTF transfer function. In this way, not only tuning curve characterization and corresponding spectral calibration but also spectral resolution assessment is performed. The obtained results indicated that the proposed method is efficient, accurate and feasible for routine calibration of AOTF spectrometers and hyper-spectral imaging systems and thereby a highly competitive alternative to the existing calibration methods.

Spectroradiometric calibration of the thematic mapper and multispectral scanner system

NASA Technical Reports Server (NTRS)

Slater, Philip N.; Palmer, James M.

1986-01-01

A list of personnel who have contributed to the program is provided. Sixteen publications and presentations are also listed. A preprint summarizing five in-flight absolute radiometric calibrations of the solar reflective bands of the LANDSAT-5 Thematic Mapper is presented. The 23 band calibrations made on the five dates show a 2.5% RMS variation from the mean as a percentage of the mean. A preprint is also presented that discusses the reflectance-based results of the above preprint. It proceeds to analyze and present results of a second, independent calibration method based on radiance measurements from a helicopter. Radiative transfer through the atmosphere, model atmospheres, the calibration methodology used at White Sands and the results of a sensitivity analysis of the reflectance-based approach is also discussed.

The effects of AVIRIS atmospheric calibration methodology on identification and quantitative mapping of surface mineralogy, Drum Mountains, Utah

NASA Technical Reports Server (NTRS)

Kruse, Fred A.; Dwyer, John L.

1993-01-01

The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) measures reflected light in 224 contiguous spectra bands in the 0.4 to 2.45 micron region of the electromagnetic spectrum. Numerous studies have used these data for mineralogic identification and mapping based on the presence of diagnostic spectral features. Quantitative mapping requires conversion of the AVIRIS data to physical units (usually reflectance) so that analysis results can be compared and validated with field and laboratory measurements. This study evaluated two different AVIRIS calibration techniques to ground reflectance: an empirically-based method and an atmospheric model based method to determine their effects on quantitative scientific analyses. Expert system analysis and linear spectral unmixing were applied to both calibrated data sets to determine the effect of the calibration on the mineral identification and quantitative mapping results. Comparison of the image-map results and image reflectance spectra indicate that the model-based calibrated data can be used with automated mapping techniques to produce accurate maps showing the spatial distribution and abundance of surface mineralogy. This has positive implications for future operational mapping using AVIRIS or similar imaging spectrometer data sets without requiring a priori knowledge.

Tropospheric and ionospheric media calibrations based on global navigation satellite system observation data

NASA Astrophysics Data System (ADS)

Feltens, Joachim; Bellei, Gabriele; Springer, Tim; Kints, Mark V.; Zandbergen, René; Budnik, Frank; Schönemann, Erik

2018-06-01

Context: Calibration of radiometric tracking data for effects in the Earth atmosphere is a crucial element in the field of deep-space orbit determination (OD). The troposphere can induce propagation delays in the order of several meters, the ionosphere up to the meter level for X-band signals and up to tens of meters, in extreme cases, for L-band ones. The use of media calibrations based on Global Navigation Satellite Systems (GNSS) measurement data can improve the accuracy of the radiometric observations modelling and, as a consequence, the quality of orbit determination solutions. Aims: ESOC Flight Dynamics employs ranging, Doppler and delta-DOR (Delta-Differential One-Way Ranging) data for the orbit determination of interplanetary spacecraft. Currently, the media calibrations for troposphere and ionosphere are either computed based on empirical models or, under mission specific agreements, provided by external parties such as the Jet Propulsion Laboratory (JPL) in Pasadena, California. In order to become independent from external models and sources, decision fell to establish a new in-house internal service to create these media calibrations based on GNSS measurements recorded at the ESA tracking sites and processed in-house by the ESOC Navigation Support Office with comparable accuracy and quality. Methods: For its concept, the new service was designed to be as much as possible depending on own data and resources and as less as possible depending on external models and data. Dedicated robust and simple algorithms, well suited for operational use, were worked out for that task. This paper describes the approach built up to realize this new in-house internal media calibration service. Results: Test results collected during three months of running the new media calibrations in quasi-operational mode indicate that GNSS-based tropospheric corrections can remove systematic signatures from the Doppler observations and biases from the range ones. For the ionosphere, a direct way of verification was not possible due to non-availability of independent third party data for comparison. Nevertheless, the tests for ionospheric corrections showed also slight improvements in the tracking data modelling, but not to an extent as seen for the tropospheric corrections. Conclusions: The validation results confirmed that the new approach meets the requirements upon accuracy and operational use for the tropospheric part, while some improvement is still ongoing for the ionospheric one. Based on these test results, green light was given to put the new in-house service for media calibrations into full operational mode in April 2017.

Fermentation process tracking through enhanced spectral calibration modeling.

PubMed

Triadaphillou, Sophia; Martin, Elaine; Montague, Gary; Norden, Alison; Jeffkins, Paul; Stimpson, Sarah

2007-06-15

The FDA process analytical technology (PAT) initiative will materialize in a significant increase in the number of installations of spectroscopic instrumentation. However, to attain the greatest benefit from the data generated, there is a need for calibration procedures that extract the maximum information content. For example, in fermentation processes, the interpretation of the resulting spectra is challenging as a consequence of the large number of wavelengths recorded, the underlying correlation structure that is evident between the wavelengths and the impact of the measurement environment. Approaches to the development of calibration models have been based on the application of partial least squares (PLS) either to the full spectral signature or to a subset of wavelengths. This paper presents a new approach to calibration modeling that combines a wavelength selection procedure, spectral window selection (SWS), where windows of wavelengths are automatically selected which are subsequently used as the basis of the calibration model. However, due to the non-uniqueness of the windows selected when the algorithm is executed repeatedly, multiple models are constructed and these are then combined using stacking thereby increasing the robustness of the final calibration model. The methodology is applied to data generated during the monitoring of broth concentrations in an industrial fermentation process from on-line near-infrared (NIR) and mid-infrared (MIR) spectrometers. It is shown that the proposed calibration modeling procedure outperforms traditional calibration procedures, as well as enabling the identification of the critical regions of the spectra with regard to the fermentation process.

Using passive capillary lysimeter water flux measurements to improve flow predictions in variably saturated soils.

USDA-ARS?s Scientific Manuscript database

Passive capillary lysimeters (PCLs) are uniquely suited for measuring water fluxes in variably-saturated soils. The objective of this work was to compare PCL flux measurements with simulated fluxes obtained with a calibrated unsaturated flow model. The Richards equation-based model was calibrated us...

Absolute Spectrophotometric Calibration to 1% from the FUV through the near-IR

NASA Astrophysics Data System (ADS)

Finley, David

2005-07-01

We propose a significant improvement to the existing HST calibration. The current calibration is based on three primary DA white dwarf standards, GD 71, GD 153, and G 191-B2B. The standard fluxes are calculated using NLTE models, with effective temperatures and gravities that were derived from Balmer line fits using LTE models. We propose to improve the accuracy and internal consistency of the calibration by deriving corrected effective temperatures and gravities based on fitting the observed line profiles with updated NLTE models, and including the fit results from multiple STIS spectra, rather than the {usually} 1 or 2 ground-based spectra used previously. We will also determine the fluxes for 5 new, fainter primary or secondary standards, extending the standard V magnitude lower limit from 13.4 to 16.5, and extending the wavelength coverage from 0.1 to 2.5 micron. The goal is to achieve an overall flux accuracy of 1%, which will be needed, for example, for the upcoming supernova survey missions to measure the equation of state of the dark energy that is accelerating the expansion of the universe.

Management of groundwater in-situ bioremediation system using reactive transport modelling under parametric uncertainty: field scale application

NASA Astrophysics Data System (ADS)

Verardo, E.; Atteia, O.; Rouvreau, L.

2015-12-01

In-situ bioremediation is a commonly used remediation technology to clean up the subsurface of petroleum-contaminated sites. Forecasting remedial performance (in terms of flux and mass reduction) is a challenge due to uncertainties associated with source properties and the uncertainties associated with contribution and efficiency of concentration reducing mechanisms. In this study, predictive uncertainty analysis of bio-remediation system efficiency is carried out with the null-space Monte Carlo (NSMC) method which combines the calibration solution-space parameters with the ensemble of null-space parameters, creating sets of calibration-constrained parameters for input to follow-on remedial efficiency. The first step in the NSMC methodology for uncertainty analysis is model calibration. The model calibration was conducted by matching simulated BTEX concentration to a total of 48 observations from historical data before implementation of treatment. Two different bio-remediation designs were then implemented in the calibrated model. The first consists in pumping/injection wells and the second in permeable barrier coupled with infiltration across slotted piping. The NSMC method was used to calculate 1000 calibration-constrained parameter sets for the two different models. Several variants of the method were implemented to investigate their effect on the efficiency of the NSMC method. The first variant implementation of the NSMC is based on a single calibrated model. In the second variant, models were calibrated from different initial parameter sets. NSMC calibration-constrained parameter sets were sampled from these different calibrated models. We demonstrate that in context of nonlinear model, second variant avoids to underestimate parameter uncertainty which may lead to a poor quantification of predictive uncertainty. Application of the proposed approach to manage bioremediation of groundwater in a real site shows that it is effective to provide support in management of the in-situ bioremediation systems. Moreover, this study demonstrates that the NSMC method provides a computationally efficient and practical methodology of utilizing model predictive uncertainty methods in environmental management.

Physical resist models and their calibration: their readiness for accurate EUV lithography simulation

NASA Astrophysics Data System (ADS)

Klostermann, U. K.; Mülders, T.; Schmöller, T.; Lorusso, G. F.; Hendrickx, E.

2010-04-01

In this paper, we discuss the performance of EUV resist models in terms of predictive accuracy, and we assess the readiness of the corresponding model calibration methodology. The study is done on an extensive OPC data set collected at IMEC for the ShinEtsu resist SEVR-59 on the ASML EUV Alpha Demo Tool (ADT), with the data set including more than thousand CD values. We address practical aspects such as the speed of calibration and selection of calibration patterns. The model is calibrated on 12 process window data series varying in pattern width (32, 36, 40 nm), orientation (H, V) and pitch (dense, isolated). The minimum measured feature size at nominal process condition is a 32 nm CD at a dense pitch of 64 nm. Mask metrology is applied to verify and eventually correct nominal width of the drawn CD. Cross-sectional SEM information is included in the calibration to tune the simulated resist loss and sidewall angle. The achieved calibration RMS is ~ 1.0 nm. We show what elements are important to obtain a well calibrated model. We discuss the impact of 3D mask effects on the Bossung tilt. We demonstrate that a correct representation of the flare level during the calibration is important to achieve a high predictability at various flare conditions. Although the model calibration is performed on a limited subset of the measurement data (one dimensional structures only), its accuracy is validated based on a large number of OPC patterns (at nominal dose and focus conditions) not included in the calibration; validation RMS results as small as 1 nm can be reached. Furthermore, we study the model's extendibility to two-dimensional end of line (EOL) structures. Finally, we correlate the experimentally observed fingerprint of the CD uniformity to a model, where EUV tool specific signatures are taken into account.

Unifying distance-based goodness-of-fit indicators for hydrologic model assessment

NASA Astrophysics Data System (ADS)

Cheng, Qinbo; Reinhardt-Imjela, Christian; Chen, Xi; Schulte, Achim

2014-05-01

The goodness-of-fit indicator, i.e. efficiency criterion, is very important for model calibration. However, recently the knowledge about the goodness-of-fit indicators is all empirical and lacks a theoretical support. Based on the likelihood theory, a unified distance-based goodness-of-fit indicator termed BC-GED model is proposed, which uses the Box-Cox (BC) transformation to remove the heteroscedasticity of model errors and the generalized error distribution (GED) with zero-mean to fit the distribution of model errors after BC. The BC-GED model can unify all recent distance-based goodness-of-fit indicators, and reveals the mean square error (MSE) and the mean absolute error (MAE) that are widely used goodness-of-fit indicators imply statistic assumptions that the model errors follow the Gaussian distribution and the Laplace distribution with zero-mean, respectively. The empirical knowledge about goodness-of-fit indicators can be also easily interpreted by BC-GED model, e.g. the sensitivity to high flow of the goodness-of-fit indicators with large power of model errors results from the low probability of large model error in the assumed distribution of these indicators. In order to assess the effect of the parameters (i.e. the BC transformation parameter λ and the GED kurtosis coefficient β also termed the power of model errors) of BC-GED model on hydrologic model calibration, six cases of BC-GED model were applied in Baocun watershed (East China) with SWAT-WB-VSA model. Comparison of the inferred model parameters and model simulation results among the six indicators demonstrates these indicators can be clearly separated two classes by the GED kurtosis β: β >1 and β ≤ 1. SWAT-WB-VSA calibrated by the class β >1 of distance-based goodness-of-fit indicators captures high flow very well and mimics the baseflow very badly, but it calibrated by the class β ≤ 1 mimics the baseflow very well, because first the larger value of β, the greater emphasis is put on high flow and second the derivative of GED probability density function at zero is zero as β >1, but discontinuous as β ≤ 1, and even infinity as β < 1 with which the maximum likelihood estimation can guarantee the model errors approach zero as well as possible. The BC-GED that estimates the parameters (i.e. λ and β) of BC-GED model as well as hydrologic model parameters is the best distance-based goodness-of-fit indicator because not only the model validation using groundwater levels is very good, but also the model errors fulfill the statistic assumption best. However, in some cases of model calibration with a few observations e.g. calibration of single-event model, for avoiding estimation of the parameters of BC-GED model the MAE i.e. the boundary indicator (β = 1) of the two classes, can replace the BC-GED, because the model validation of MAE is best.

LiDAR-IMU Time Delay Calibration Based on Iterative Closest Point and Iterated Sigma Point Kalman Filter

PubMed Central

Liu, Wanli

2017-01-01

The time delay calibration between Light Detection and Ranging (LiDAR) and Inertial Measurement Units (IMUs) is an essential prerequisite for its applications. However, the correspondences between LiDAR and IMU measurements are usually unknown, and thus cannot be computed directly for the time delay calibration. In order to solve the problem of LiDAR-IMU time delay calibration, this paper presents a fusion method based on iterative closest point (ICP) and iterated sigma point Kalman filter (ISPKF), which combines the advantages of ICP and ISPKF. The ICP algorithm can precisely determine the unknown transformation between LiDAR-IMU; and the ISPKF algorithm can optimally estimate the time delay calibration parameters. First of all, the coordinate transformation from the LiDAR frame to the IMU frame is realized. Second, the measurement model and time delay error model of LiDAR and IMU are established. Third, the methodology of the ICP and ISPKF procedure is presented for LiDAR-IMU time delay calibration. Experimental results are presented that validate the proposed method and demonstrate the time delay error can be accurately calibrated. PMID:28282897

Multisite Evaluation of APEX for Water Quality: I. Best Professional Judgment Parameterization.

PubMed

Baffaut, Claire; Nelson, Nathan O; Lory, John A; Senaviratne, G M M M Anomaa; Bhandari, Ammar B; Udawatta, Ranjith P; Sweeney, Daniel W; Helmers, Matt J; Van Liew, Mike W; Mallarino, Antonio P; Wortmann, Charles S

2017-11-01

The Agricultural Policy Environmental eXtender (APEX) model is capable of estimating edge-of-field water, nutrient, and sediment transport and is used to assess the environmental impacts of management practices. The current practice is to fully calibrate the model for each site simulation, a task that requires resources and data not always available. The objective of this study was to compare model performance for flow, sediment, and phosphorus transport under two parameterization schemes: a best professional judgment (BPJ) parameterization based on readily available data and a fully calibrated parameterization based on site-specific soil, weather, event flow, and water quality data. The analysis was conducted using 12 datasets at four locations representing poorly drained soils and row-crop production under different tillage systems. Model performance was based on the Nash-Sutcliffe efficiency (NSE), the coefficient of determination () and the regression slope between simulated and measured annualized loads across all site years. Although the BPJ model performance for flow was acceptable (NSE = 0.7) at the annual time step, calibration improved it (NSE = 0.9). Acceptable simulation of sediment and total phosphorus transport (NSE = 0.5 and 0.9, respectively) was obtained only after full calibration at each site. Given the unacceptable performance of the BPJ approach, uncalibrated use of APEX for planning or management purposes may be misleading. Model calibration with water quality data prior to using APEX for simulating sediment and total phosphorus loss is essential. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Mixed Model Association with Family-Biased Case-Control Ascertainment.

PubMed

Hayeck, Tristan J; Loh, Po-Ru; Pollack, Samuela; Gusev, Alexander; Patterson, Nick; Zaitlen, Noah A; Price, Alkes L

2017-01-05

Mixed models have become the tool of choice for genetic association studies; however, standard mixed model methods may be poorly calibrated or underpowered under family sampling bias and/or case-control ascertainment. Previously, we introduced a liability threshold-based mixed model association statistic (LTMLM) to address case-control ascertainment in unrelated samples. Here, we consider family-biased case-control ascertainment, where case and control subjects are ascertained non-randomly with respect to family relatedness. Previous work has shown that this type of ascertainment can severely bias heritability estimates; we show here that it also impacts mixed model association statistics. We introduce a family-based association statistic (LT-Fam) that is robust to this problem. Similar to LTMLM, LT-Fam is computed from posterior mean liabilities (PML) under a liability threshold model; however, LT-Fam uses published narrow-sense heritability estimates to avoid the problem of biased heritability estimation, enabling correct calibration. In simulations with family-biased case-control ascertainment, LT-Fam was correctly calibrated (average χ 2 = 1.00-1.02 for null SNPs), whereas the Armitage trend test (ATT), standard mixed model association (MLM), and case-control retrospective association test (CARAT) were mis-calibrated (e.g., average χ 2 = 0.50-1.22 for MLM, 0.89-2.65 for CARAT). LT-Fam also attained higher power than other methods in some settings. In 1,259 type 2 diabetes-affected case subjects and 5,765 control subjects from the CARe cohort, downsampled to induce family-biased ascertainment, LT-Fam was correctly calibrated whereas ATT, MLM, and CARAT were again mis-calibrated. Our results highlight the importance of modeling family sampling bias in case-control datasets with related samples. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Predicting the performance uncertainty of a 1-MW pilot-scale carbon capture system after hierarchical laboratory-scale calibration and validation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Zhijie; Lai, Canhai; Marcy, Peter William

2017-05-01

A challenging problem in designing pilot-scale carbon capture systems is to predict, with uncertainty, the adsorber performance and capture efficiency under various operating conditions where no direct experimental data exist. Motivated by this challenge, we previously proposed a hierarchical framework in which relevant parameters of physical models were sequentially calibrated from different laboratory-scale carbon capture unit (C2U) experiments. Specifically, three models of increasing complexity were identified based on the fundamental physical and chemical processes of the sorbent-based carbon capture technology. Results from the corresponding laboratory experiments were used to statistically calibrate the physical model parameters while quantifying some of theirmore » inherent uncertainty. The parameter distributions obtained from laboratory-scale C2U calibration runs are used in this study to facilitate prediction at a larger scale where no corresponding experimental results are available. In this paper, we first describe the multiphase reactive flow model for a sorbent-based 1-MW carbon capture system then analyze results from an ensemble of simulations with the upscaled model. The simulation results are used to quantify uncertainty regarding the design’s predicted efficiency in carbon capture. In particular, we determine the minimum gas flow rate necessary to achieve 90% capture efficiency with 95% confidence.« less

Modelling carbon oxidation in pulp mill activated sludge systems: calibration of Activated Sludge Model No 3.

PubMed

Barañao, P A; Hall, E R

2004-01-01

Activated Sludge Model No 3 (ASM3) was chosen to model an activated sludge system treating effluents from a mechanical pulp and paper mill. The high COD concentration and the high content of readily biodegradable substrates of the wastewater make this model appropriate for this system. ASM3 was calibrated based on batch respirometric tests using fresh wastewater and sludge from the treatment plant, and on analytical measurements of COD, TSS and VSS. The model, developed for municipal wastewater, was found suitable for fitting a variety of respirometric batch tests, performed at different temperatures and food to microorganism ratios (F/M). Therefore, a set of calibrated parameters, as well as the wastewater COD fractions, was estimated for this industrial wastewater. The majority of the calibrated parameters were in the range of those found in the literature.

Measurement correction method for force sensor used in dynamic pressure calibration based on artificial neural network optimized by genetic algorithm

NASA Astrophysics Data System (ADS)

Gu, Tingwei; Kong, Deren; Shang, Fei; Chen, Jing

2017-12-01

We present an optimization algorithm to obtain low-uncertainty dynamic pressure measurements from a force-transducer-based device. In this paper, the advantages and disadvantages of the methods that are commonly used to measure the propellant powder gas pressure, the applicable scope of dynamic pressure calibration devices, and the shortcomings of the traditional comparison calibration method based on the drop-weight device are firstly analysed in detail. Then, a dynamic calibration method for measuring pressure using a force sensor based on a drop-weight device is introduced. This method can effectively save time when many pressure sensors are calibrated simultaneously and extend the life of expensive reference sensors. However, the force sensor is installed between the drop-weight and the hammerhead by transition pieces through the connection mode of bolt fastening, which causes adverse effects such as additional pretightening and inertia forces. To solve these effects, the influence mechanisms of the pretightening force, the inertia force and other influence factors on the force measurement are theoretically analysed. Then a measurement correction method for the force measurement is proposed based on an artificial neural network optimized by a genetic algorithm. The training and testing data sets are obtained from calibration tests, and the selection criteria for the key parameters of the correction model is discussed. The evaluation results for the test data show that the correction model can effectively improve the force measurement accuracy of the force sensor. Compared with the traditional high-accuracy comparison calibration method, the percentage difference of the impact-force-based measurement is less than 0.6% and the relative uncertainty of the corrected force value is 1.95%, which can meet the requirements of engineering applications.

Antenna Calibration and Measurement Equipment

NASA Technical Reports Server (NTRS)

Rochblatt, David J.; Cortes, Manuel Vazquez

2012-01-01

A document describes the Antenna Calibration & Measurement Equipment (ACME) system that will provide the Deep Space Network (DSN) with instrumentation enabling a trained RF engineer at each complex to perform antenna calibration measurements and to generate antenna calibration data. This data includes continuous-scan auto-bore-based data acquisition with all-sky data gathering in support of 4th order pointing model generation requirements. Other data includes antenna subreflector focus, system noise temperature and tipping curves, antenna efficiency, reports system linearity, and instrument calibration. The ACME system design is based on the on-the-fly (OTF) mapping technique and architecture. ACME has contributed to the improved RF performance of the DSN by approximately a factor of two. It improved the pointing performances of the DSN antennas and productivity of its personnel and calibration engineers.

Auto-calibration of a one-dimensional hydrodynamic-ecological model using a Monte Carlo approach: simulation of hypoxic events in a polymictic lake

NASA Astrophysics Data System (ADS)

Luo, L.

2011-12-01

Automated calibration of complex deterministic water quality models with a large number of biogeochemical parameters can reduce time-consuming iterative simulations involving empirical judgements of model fit. We undertook auto-calibration of the one-dimensional hydrodynamic-ecological lake model DYRESM-CAEDYM, using a Monte Carlo sampling (MCS) method, in order to test the applicability of this procedure for shallow, polymictic Lake Rotorua (New Zealand). The calibration procedure involved independently minimising the root-mean-square-error (RMSE), maximizing the Pearson correlation coefficient (r) and Nash-Sutcliffe efficient coefficient (Nr) for comparisons of model state variables against measured data. An assigned number of parameter permutations was used for 10,000 simulation iterations. The 'optimal' temperature calibration produced a RMSE of 0.54 °C, Nr-value of 0.99 and r-value of 0.98 through the whole water column based on comparisons with 540 observed water temperatures collected between 13 July 2007 - 13 January 2009. The modeled bottom dissolved oxygen concentration (20.5 m below surface) was compared with 467 available observations. The calculated RMSE of the simulations compared with the measurements was 1.78 mg L-1, the Nr-value was 0.75 and the r-value was 0.87. The autocalibrated model was further tested for an independent data set by simulating bottom-water hypoxia events for the period 15 January 2009 to 8 June 2011 (875 days). This verification produced an accurate simulation of five hypoxic events corresponding to DO < 2 mg L-1 during summer of 2009-2011. The RMSE was 2.07 mg L-1, Nr-value 0.62 and r-value of 0.81, based on the available data set of 738 days. The auto-calibration software of DYRESM-CAEDYM developed here is substantially less time-consuming and more efficient in parameter optimisation than traditional manual calibration which has been the standard tool practiced for similar complex water quality models.

Assessment of measurement errors and dynamic calibration methods for three different tipping bucket rain gauges

NASA Astrophysics Data System (ADS)

Shedekar, Vinayak S.; King, Kevin W.; Fausey, Norman R.; Soboyejo, Alfred B. O.; Harmel, R. Daren; Brown, Larry C.

2016-09-01

Three different models of tipping bucket rain gauges (TBRs), viz. HS-TB3 (Hydrological Services Pty Ltd.), ISCO-674 (Isco, Inc.) and TR-525 (Texas Electronics, Inc.), were calibrated in the lab to quantify measurement errors across a range of rainfall intensities (5 mm·h- 1 to 250 mm·h- 1) and three different volumetric settings. Instantaneous and cumulative values of simulated rainfall were recorded at 1, 2, 5, 10 and 20-min intervals. All three TBR models showed a substantial deviation (α = 0.05) in measurements from actual rainfall depths, with increasing underestimation errors at greater rainfall intensities. Simple linear regression equations were developed for each TBR to correct the TBR readings based on measured intensities (R2 > 0.98). Additionally, two dynamic calibration techniques, viz. quadratic model (R2 > 0.7) and T vs. 1/Q model (R2 = > 0.98), were tested and found to be useful in situations when the volumetric settings of TBRs are unknown. The correction models were successfully applied to correct field-collected rainfall data from respective TBR models. The calibration parameters of correction models were found to be highly sensitive to changes in volumetric calibration of TBRs. Overall, the HS-TB3 model (with a better protected tipping bucket mechanism, and consistent measurement errors across a range of rainfall intensities) was found to be the most reliable and consistent for rainfall measurements, followed by the ISCO-674 (with susceptibility to clogging and relatively smaller measurement errors across a range of rainfall intensities) and the TR-525 (with high susceptibility to clogging and frequent changes in volumetric calibration, and highly intensity-dependent measurement errors). The study demonstrated that corrections based on dynamic and volumetric calibration can only help minimize-but not completely eliminate the measurement errors. The findings from this study will be useful for correcting field data from TBRs; and may have major implications to field- and watershed-scale hydrologic studies.

Calibration of mass transfer-based models to predict reference crop evapotranspiration

NASA Astrophysics Data System (ADS)

Valipour, Mohammad

2017-05-01

The present study aims to compare mass transfer-based models to determine the best model under different weather conditions. The results showed that the Penman model estimates reference crop evapotranspiration better than other models in most provinces of Iran (15 provinces). However, the values of R 2 were less than 0.90 for 24 provinces of Iran. Therefore, the models were calibrated, and precision of estimation was increased (the values of R 2 were less than 0.90 for only ten provinces in the modified models). The mass transfer-based models estimated reference crop evapotranspiration in the northern (near the Caspian Sea) and southern (near the Persian Gulf) Iran (annual relative humidity more than 65 %) better than other provinces. The best values of R 2 were 0.96 and 0.98 for the Trabert and Rohwer models in Ardabil (AR) and Mazandaran (MZ) provinces before and after calibration, respectively. Finally, a list of the best performances of each model was presented to use other regions and next studies according to values of mean, maximum, and minimum temperature, relative humidity, and wind speed. The best weather conditions to use mass transfer-based equations are 8-18 °C (with the exception of Ivanov), <25.5 °C, <15 °C, >55 % for mean, maximum, and minimum temperature, and relative humidity, respectively.

Parameter estimation uncertainty: Comparing apples and apples?

NASA Astrophysics Data System (ADS)

Hart, D.; Yoon, H.; McKenna, S. A.

2012-12-01

Given a highly parameterized ground water model in which the conceptual model of the heterogeneity is stochastic, an ensemble of inverse calibrations from multiple starting points (MSP) provides an ensemble of calibrated parameters and follow-on transport predictions. However, the multiple calibrations are computationally expensive. Parameter estimation uncertainty can also be modeled by decomposing the parameterization into a solution space and a null space. From a single calibration (single starting point) a single set of parameters defining the solution space can be extracted. The solution space is held constant while Monte Carlo sampling of the parameter set covering the null space creates an ensemble of the null space parameter set. A recently developed null-space Monte Carlo (NSMC) method combines the calibration solution space parameters with the ensemble of null space parameters, creating sets of calibration-constrained parameters for input to the follow-on transport predictions. Here, we examine the consistency between probabilistic ensembles of parameter estimates and predictions using the MSP calibration and the NSMC approaches. A highly parameterized model of the Culebra dolomite previously developed for the WIPP project in New Mexico is used as the test case. A total of 100 estimated fields are retained from the MSP approach and the ensemble of results defining the model fit to the data, the reproduction of the variogram model and prediction of an advective travel time are compared to the same results obtained using NSMC. We demonstrate that the NSMC fields based on a single calibration model can be significantly constrained by the calibrated solution space and the resulting distribution of advective travel times is biased toward the travel time from the single calibrated field. To overcome this, newly proposed strategies to employ a multiple calibration-constrained NSMC approach (M-NSMC) are evaluated. Comparison of the M-NSMC and MSP methods suggests that M-NSMC can provide a computationally efficient and practical solution for predictive uncertainty analysis in highly nonlinear and complex subsurface flow and transport models. This material is based upon work supported as part of the Center for Frontiers of Subsurface Energy Security, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Award Number DE-SC0001114. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

Calibration and validation of the SWAT model for a forested watershed in coastal South Carolina

Treesearch

Devendra M. Amatya; Elizabeth B. Haley; Norman S. Levine; Timothy J. Callahan; Artur Radecki-Pawlik; Manoj K. Jha

2008-01-01

Modeling the hydrology of low-gradient coastal watersheds on shallow, poorly drained soils is a challenging task due to the complexities in watershed delineation, runoff generation processes and pathways, flooding, and submergence caused by tropical storms. The objective of the study is to calibrate and validate a GIS-based spatially-distributed hydrologic model, SWAT...

A calibrated, high-resolution goes satellite solar insolation product for a climatology of Florida evapotranspiration

USGS Publications Warehouse

Paech, S.J.; Mecikalski, J.R.; Sumner, D.M.; Pathak, C.S.; Wu, Q.; Islam, S.; Sangoyomi, T.

2009-01-01

Estimates of incoming solar radiation (insolation) from Geostationary Operational Environmental Satellite observations have been produced for the state of Florida over a 10-year period (1995-2004). These insolation estimates were developed into well-calibrated half-hourly and daily integrated solar insolation fields over the state at 2 km resolution, in addition to a 2-week running minimum surface albedo product. Model results of the daily integrated insolation were compared with ground-based pyranometers, and as a result, the entire dataset was calibrated. This calibration was accomplished through a three-step process: (1) comparison with ground-based pyranometer measurements on clear (noncloudy) reference days, (2) correcting for a bias related to cloudiness, and (3) deriving a monthly bias correction factor. Precalibration results indicated good model performance, with a station-averaged model error of 2.2 MJ m-2/day (13%). Calibration reduced errors to 1.7 MJ m -2/day (10%), and also removed temporal-related, seasonal-related, and satellite sensor-related biases. The calibrated insolation dataset will subsequently be used by state of Florida Water Management Districts to produce statewide, 2-km resolution maps of estimated daily reference and potential evapotranspiration for water management-related activities. ?? 2009 American Water Resources Association.

Calculations to support JET neutron yield calibration: Modelling of neutron emission from a compact DT neutron generator

NASA Astrophysics Data System (ADS)

Čufar, Aljaž; Batistoni, Paola; Conroy, Sean; Ghani, Zamir; Lengar, Igor; Milocco, Alberto; Packer, Lee; Pillon, Mario; Popovichev, Sergey; Snoj, Luka; JET Contributors

2017-03-01

At the Joint European Torus (JET) the ex-vessel fission chambers and in-vessel activation detectors are used as the neutron production rate and neutron yield monitors respectively. In order to ensure that these detectors produce accurate measurements they need to be experimentally calibrated. A new calibration of neutron detectors to 14 MeV neutrons, resulting from deuterium-tritium (DT) plasmas, is planned at JET using a compact accelerator based neutron generator (NG) in which a D/T beam impinges on a solid target containing T/D, producing neutrons by DT fusion reactions. This paper presents the analysis that was performed to model the neutron source characteristics in terms of energy spectrum, angle-energy distribution and the effect of the neutron generator geometry. Different codes capable of simulating the accelerator based DT neutron sources are compared and sensitivities to uncertainties in the generator's internal structure analysed. The analysis was performed to support preparation to the experimental measurements performed to characterize the NG as a calibration source. Further extensive neutronics analyses, performed with this model of the NG, will be needed to support the neutron calibration experiments and take into account various differences between the calibration experiment and experiments using the plasma as a source of neutrons.

The creation and evaluation of a model to simulate the probability of conception in seasonal-calving pasture-based dairy heifers.

PubMed

Fenlon, Caroline; O'Grady, Luke; Butler, Stephen; Doherty, Michael L; Dunnion, John

2017-01-01

Herd fertility in pasture-based dairy farms is a key driver of farm economics. Models for predicting nulliparous reproductive outcomes are rare, but age, genetics, weight, and BCS have been identified as factors influencing heifer conception. The aim of this study was to create a simulation model of heifer conception to service with thorough evaluation. Artificial Insemination service records from two research herds and ten commercial herds were provided to build and evaluate the models. All were managed as spring-calving pasture-based systems. The factors studied were related to age, genetics, and time of service. The data were split into training and testing sets and bootstrapping was used to train the models. Logistic regression (with and without random effects) and generalised additive modelling were selected as the model-building techniques. Two types of evaluation were used to test the predictive ability of the models: discrimination and calibration. Discrimination, which includes sensitivity, specificity, accuracy and ROC analysis, measures a model's ability to distinguish between positive and negative outcomes. Calibration measures the accuracy of the predicted probabilities with the Hosmer-Lemeshow goodness-of-fit, calibration plot and calibration error. After data cleaning and the removal of services with missing values, 1396 services remained to train the models and 597 were left for testing. Age, breed, genetic predicted transmitting ability for calving interval, month and year were significant in the multivariate models. The regression models also included an interaction between age and month. Year within herd was a random effect in the mixed regression model. Overall prediction accuracy was between 77.1% and 78.9%. All three models had very high sensitivity, but low specificity. The two regression models were very well-calibrated. The mean absolute calibration errors were all below 4%. Because the models were not adept at identifying unsuccessful services, they are not suggested for use in predicting the outcome of individual heifer services. Instead, they are useful for the comparison of services with different covariate values or as sub-models in whole-farm simulations. The mixed regression model was identified as the best model for prediction, as the random effects can be ignored and the other variables can be easily obtained or simulated.

Absolute radiometric calibration of Landsat using a pseudo invariant calibration site

USGS Publications Warehouse

Helder, D.; Thome, K.J.; Mishra, N.; Chander, G.; Xiong, Xiaoxiong; Angal, A.; Choi, Tae-young

2013-01-01

Pseudo invariant calibration sites (PICS) have been used for on-orbit radiometric trending of optical satellite systems for more than 15 years. This approach to vicarious calibration has demonstrated a high degree of reliability and repeatability at the level of 1-3% depending on the site, spectral channel, and imaging geometries. A variety of sensors have used this approach for trending because it is broadly applicable and easy to implement. Models to describe the surface reflectance properties, as well as the intervening atmosphere have also been developed to improve the precision of the method. However, one limiting factor of using PICS is that an absolute calibration capability has not yet been fully developed. Because of this, PICS are primarily limited to providing only long term trending information for individual sensors or cross-calibration opportunities between two sensors. This paper builds an argument that PICS can be used more extensively for absolute calibration. To illustrate this, a simple empirical model is developed for the well-known Libya 4 PICS based on observations by Terra MODIS and EO-1 Hyperion. The model is validated by comparing model predicted top-of-atmosphere reflectance values to actual measurements made by the Landsat ETM+ sensor reflective bands. Following this, an outline is presented to develop a more comprehensive and accurate PICS absolute calibration model that can be Système international d'unités (SI) traceable. These initial concepts suggest that absolute calibration using PICS is possible on a broad scale and can lead to improved on-orbit calibration capabilities for optical satellite sensors.

Stellar-based calibration in the far infrared with application to IRAS Band 4

NASA Technical Reports Server (NTRS)

Kirby, D. J.; Rieke, G. H.; Lebofsky, L. A.

1994-01-01

Because stars emit very small portions of their outputs in the far infrared, using them as calibrators requires precise measurement and correction for filter leaks at shorter wavelengths. Therefore, it is common to base far infrared calibrations on planetary objects such as asteriods. However, asteroids are complex geological bodies whose thermal properties depend on their evolutionary histories as well as on their gross parameters such as mass and composition, making them difficult to model as calibrators. We propose a new method for measuring filter leaks that can be carried out using the end-to-end detector system and therefore allows reliable use of stellar calibrators. We illustrate this method by showing that the Infrared Astronomy Satellite (IRAS) 100 micrometers (Band 4) filters had a short wavelength leak of 14.3% +/- 3.6% on stars similar to alpha Boo, but that there is no detectable leak in the 60 micrometers (Band 3) filters. We derive a calibration for Band 4 from stellar colors in a way that is closely analogous to the calibrations of Bands 1, 2, and 3. With correction for the leak, the stellar-based calibration is virtually identical to the original calibration based on asteroids; this result requires that the spectra of the asteriods for the original calibration differ from greybody behavior between 60 and 100 micrometers by about 10%.

Ecologically-focused Calibration of Hydrological Models for Environmental Flow Applications

NASA Astrophysics Data System (ADS)

Adams, S. K.; Bledsoe, B. P.

2015-12-01

Hydrologic alteration resulting from watershed urbanization is a common cause of aquatic ecosystem degradation. Developing environmental flow criteria for urbanizing watersheds requires quantitative flow-ecology relationships that describe biological responses to streamflow alteration. Ideally, gaged flow data are used to develop flow-ecology relationships; however, biological monitoring sites are frequently ungaged. For these ungaged locations, hydrologic models must be used to predict streamflow characteristics through calibration and testing at gaged sites, followed by extrapolation to ungaged sites. Physically-based modeling of rainfall-runoff response has frequently utilized "best overall fit" calibration criteria, such as the Nash-Sutcliffe Efficiency (NSE), that do not necessarily focus on specific aspects of the flow regime relevant to biota of interest. This study investigates the utility of employing flow characteristics known a priori to influence regional biological endpoints as "ecologically-focused" calibration criteria compared to traditional, "best overall fit" criteria. For this study, 19 continuous HEC-HMS 4.0 models were created in coastal southern California and calibrated to hourly USGS streamflow gages with nearby biological monitoring sites using one "best overall fit" and three "ecologically-focused" criteria: NSE, Richards-Baker Flashiness Index (RBI), percent of time when the flow is < 1 cfs (%<1), and a Combined Calibration (RBI and %<1). Calibrated models were compared using calibration accuracy, environmental flow metric reproducibility, and the strength of flow-ecology relationships. Results indicate that "ecologically-focused" criteria can be calibrated with high accuracy and may provide stronger flow-ecology relationships than "best overall fit" criteria, especially when multiple "ecologically-focused" criteria are used in concert, despite inabilities to accurately reproduce additional types of ecological flow metrics to which the models are not explicitly calibrated.

Augmenting epidemiological models with point-of-care diagnostics data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pullum, Laura L.; Ramanathan, Arvind; Nutaro, James J.

Although adoption of newer Point-of-Care (POC) diagnostics is increasing, there is a significant challenge using POC diagnostics data to improve epidemiological models. In this work, we propose a method to process zip-code level POC datasets and apply these processed data to calibrate an epidemiological model. We specifically develop a calibration algorithm using simulated annealing and calibrate a parsimonious equation-based model of modified Susceptible-Infected-Recovered (SIR) dynamics. The results show that parsimonious models are remarkably effective in predicting the dynamics observed in the number of infected patients and our calibration algorithm is sufficiently capable of predicting peak loads observed in POC diagnosticsmore » data while staying within reasonable and empirical parameter ranges reported in the literature. Additionally, we explore the future use of the calibrated values by testing the correlation between peak load and population density from Census data. Our results show that linearity assumptions for the relationships among various factors can be misleading, therefore further data sources and analysis are needed to identify relationships between additional parameters and existing calibrated ones. As a result, calibration approaches such as ours can determine the values of newly added parameters along with existing ones and enable policy-makers to make better multi-scale decisions.« less

Augmenting epidemiological models with point-of-care diagnostics data

DOE PAGES

Pullum, Laura L.; Ramanathan, Arvind; Nutaro, James J.; ...

2016-04-20

Although adoption of newer Point-of-Care (POC) diagnostics is increasing, there is a significant challenge using POC diagnostics data to improve epidemiological models. In this work, we propose a method to process zip-code level POC datasets and apply these processed data to calibrate an epidemiological model. We specifically develop a calibration algorithm using simulated annealing and calibrate a parsimonious equation-based model of modified Susceptible-Infected-Recovered (SIR) dynamics. The results show that parsimonious models are remarkably effective in predicting the dynamics observed in the number of infected patients and our calibration algorithm is sufficiently capable of predicting peak loads observed in POC diagnosticsmore » data while staying within reasonable and empirical parameter ranges reported in the literature. Additionally, we explore the future use of the calibrated values by testing the correlation between peak load and population density from Census data. Our results show that linearity assumptions for the relationships among various factors can be misleading, therefore further data sources and analysis are needed to identify relationships between additional parameters and existing calibrated ones. As a result, calibration approaches such as ours can determine the values of newly added parameters along with existing ones and enable policy-makers to make better multi-scale decisions.« less

Mathematical modeling of malaria infection with innate and adaptive immunity in individuals and agent-based communities.

PubMed

Gurarie, David; Karl, Stephan; Zimmerman, Peter A; King, Charles H; St Pierre, Timothy G; Davis, Timothy M E

2012-01-01

Agent-based modeling of Plasmodium falciparum infection offers an attractive alternative to the conventional Ross-Macdonald methodology, as it allows simulation of heterogeneous communities subjected to realistic transmission (inoculation patterns). We developed a new, agent based model that accounts for the essential in-host processes: parasite replication and its regulation by innate and adaptive immunity. The model also incorporates a simplified version of antigenic variation by Plasmodium falciparum. We calibrated the model using data from malaria-therapy (MT) studies, and developed a novel calibration procedure that accounts for a deterministic and a pseudo-random component in the observed parasite density patterns. Using the parasite density patterns of 122 MT patients, we generated a large number of calibrated parameters. The resulting data set served as a basis for constructing and simulating heterogeneous agent-based (AB) communities of MT-like hosts. We conducted several numerical experiments subjecting AB communities to realistic inoculation patterns reported from previous field studies, and compared the model output to the observed malaria prevalence in the field. There was overall consistency, supporting the potential of this agent-based methodology to represent transmission in realistic communities. Our approach represents a novel, convenient and versatile method to model Plasmodium falciparum infection.

Stable Local Volatility Calibration Using Kernel Splines

NASA Astrophysics Data System (ADS)

Coleman, Thomas F.; Li, Yuying; Wang, Cheng

2010-09-01

We propose an optimization formulation using L1 norm to ensure accuracy and stability in calibrating a local volatility function for option pricing. Using a regularization parameter, the proposed objective function balances the calibration accuracy with the model complexity. Motivated by the support vector machine learning, the unknown local volatility function is represented by a kernel function generating splines and the model complexity is controlled by minimizing the 1-norm of the kernel coefficient vector. In the context of the support vector regression for function estimation based on a finite set of observations, this corresponds to minimizing the number of support vectors for predictability. We illustrate the ability of the proposed approach to reconstruct the local volatility function in a synthetic market. In addition, based on S&P 500 market index option data, we demonstrate that the calibrated local volatility surface is simple and resembles the observed implied volatility surface in shape. Stability is illustrated by calibrating local volatility functions using market option data from different dates.

In-flight radiometric calibration of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)

NASA Technical Reports Server (NTRS)

Conel, James E.; Green, Robert O.; Alley, Ronald E.; Bruegge, Carol J.; Carrere, Veronique; Margolis, Jack S.; Vane, Gregg; Chrien, Thomas G.; Slater, Philip N.; Biggard, Stuart F.

1988-01-01

A reflectance-based method was used to provide an analysis of the in-flight radiometric performance of AVIRIS. Field spectral reflectance measurements of the surface and extinction measurements of the atmosphere using solar radiation were used as input to atmospheric radiative transfer calculations. Five separate codes were used in the analysis. Four include multiple scattering, and the computed radiances from these for flight conditions were in good agreement. Code-generated radiances were compared with AVIRIS-predicted radiances based on two laboratory calibrations (pre- and post-season of flight) for a uniform highly reflecting natural dry lake target. For one spectrometer (C), the pre- and post-season calibration factors were found to give identical results, and to be in agreement with the atmospheric models that include multiple scattering. This positive result validates the field and laboratory calibration technique. Results for the other spectrometers (A, B and D) were widely at variance with the models no matter which calibration factors were used. Potential causes of these discrepancies are discussed.

Quantifying the predictive consequences of model error with linear subspace analysis

USGS Publications Warehouse

White, Jeremy T.; Doherty, John E.; Hughes, Joseph D.

2014-01-01

All computer models are simplified and imperfect simulators of complex natural systems. The discrepancy arising from simplification induces bias in model predictions, which may be amplified by the process of model calibration. This paper presents a new method to identify and quantify the predictive consequences of calibrating a simplified computer model. The method is based on linear theory, and it scales efficiently to the large numbers of parameters and observations characteristic of groundwater and petroleum reservoir models. The method is applied to a range of predictions made with a synthetic integrated surface-water/groundwater model with thousands of parameters. Several different observation processing strategies and parameterization/regularization approaches are examined in detail, including use of the Karhunen-Loève parameter transformation. Predictive bias arising from model error is shown to be prediction specific and often invisible to the modeler. The amount of calibration-induced bias is influenced by several factors, including how expert knowledge is applied in the design of parameterization schemes, the number of parameters adjusted during calibration, how observations and model-generated counterparts are processed, and the level of fit with observations achieved through calibration. Failure to properly implement any of these factors in a prediction-specific manner may increase the potential for predictive bias in ways that are not visible to the calibration and uncertainty analysis process.

A Monte-Carlo simulation analysis for evaluating the severity distribution functions (SDFs) calibration methodology and determining the minimum sample-size requirements.

PubMed

Shirazi, Mohammadali; Reddy Geedipally, Srinivas; Lord, Dominique

2017-01-01

Severity distribution functions (SDFs) are used in highway safety to estimate the severity of crashes and conduct different types of safety evaluations and analyses. Developing a new SDF is a difficult task and demands significant time and resources. To simplify the process, the Highway Safety Manual (HSM) has started to document SDF models for different types of facilities. As such, SDF models have recently been introduced for freeway and ramps in HSM addendum. However, since these functions or models are fitted and validated using data from a few selected number of states, they are required to be calibrated to the local conditions when applied to a new jurisdiction. The HSM provides a methodology to calibrate the models through a scalar calibration factor. However, the proposed methodology to calibrate SDFs was never validated through research. Furthermore, there are no concrete guidelines to select a reliable sample size. Using extensive simulation, this paper documents an analysis that examined the bias between the 'true' and 'estimated' calibration factors. It was indicated that as the value of the true calibration factor deviates further away from '1', more bias is observed between the 'true' and 'estimated' calibration factors. In addition, simulation studies were performed to determine the calibration sample size for various conditions. It was found that, as the average of the coefficient of variation (CV) of the 'KAB' and 'C' crashes increases, the analyst needs to collect a larger sample size to calibrate SDF models. Taking this observation into account, sample-size guidelines are proposed based on the average CV of crash severities that are used for the calibration process. Copyright © 2016 Elsevier Ltd. All rights reserved.

Portable Dynamic Pressure Calibrator

NASA Technical Reports Server (NTRS)

Wright, Morgan S.; Maynard, Everett (Technical Monitor)

1996-01-01

A portable, dynamic pressure calibrator was fabricated for use on wind tunnel models at NASA-Ames Research Center. The calibrator generates sine wave pressures at levels up to 1 PSIG P-P(168dB) at frequencies from 10Hz to 6KHz and .5 PSIG P.P (162dB) at frequencies from 6KHz to 20KHz. The calibrator consists of two units connected by a single cable. The handheld unit contains a pressure transducer, speaker, and deadman switch. This unit allows application of dynamic pressure to transducers/ports on installed wind tunnel models. The base unit contains all of power supplies, controls and displays. This unit allows amplitude and frequency to be set and verified at a safe location off of the model.

Landsat-5 TM reflective-band absolute radiometric calibration

USGS Publications Warehouse

Chander, G.; Helder, D.L.; Markham, B.L.; Dewald, J.D.; Kaita, E.; Thome, K.J.; Micijevic, E.; Ruggles, T.A.

2004-01-01

The Landsat-5 Thematic Mapper (TM) sensor provides the longest running continuous dataset of moderate spatial resolution remote sensing imagery, dating back to its launch in March 1984. Historically, the radiometric calibration procedure for this imagery used the instrument's response to the Internal Calibrator (IC) on a scene-by-scene basis to determine the gain and offset of each detector. Due to observed degradations in the IC, a new procedure was implemented for U.S.-processed data in May 2003. This new calibration procedure is based on a lifetime radiometric calibration model for the instrument's reflective bands (1-5 and 7) and is derived, in part, from the IC response without the related degradation effects and is tied to the cross calibration with the Landsat-7 Enhanced Thematic Mapper Plus. Reflective-band absolute radiometric accuracy of the instrument tends to be on the order of 7% to 10%, based on a variety of calibration methods.

Camera calibration method of binocular stereo vision based on OpenCV

NASA Astrophysics Data System (ADS)

Zhong, Wanzhen; Dong, Xiaona

2015-10-01

Camera calibration, an important part of the binocular stereo vision research, is the essential foundation of 3D reconstruction of the spatial object. In this paper, the camera calibration method based on OpenCV (open source computer vision library) is submitted to make the process better as a result of obtaining higher precision and efficiency. First, the camera model in OpenCV and an algorithm of camera calibration are presented, especially considering the influence of camera lens radial distortion and decentering distortion. Then, camera calibration procedure is designed to compute those parameters of camera and calculate calibration errors. High-accurate profile extraction algorithm and a checkboard with 48 corners have also been used in this part. Finally, results of calibration program are presented, demonstrating the high efficiency and accuracy of the proposed approach. The results can reach the requirement of robot binocular stereo vision.

Optical System Error Analysis and Calibration Method of High-Accuracy Star Trackers

PubMed Central

Sun, Ting; Xing, Fei; You, Zheng

2013-01-01

The star tracker is a high-accuracy attitude measurement device widely used in spacecraft. Its performance depends largely on the precision of the optical system parameters. Therefore, the analysis of the optical system parameter errors and a precise calibration model are crucial to the accuracy of the star tracker. Research in this field is relatively lacking a systematic and universal analysis up to now. This paper proposes in detail an approach for the synthetic error analysis of the star tracker, without the complicated theoretical derivation. This approach can determine the error propagation relationship of the star tracker, and can build intuitively and systematically an error model. The analysis results can be used as a foundation and a guide for the optical design, calibration, and compensation of the star tracker. A calibration experiment is designed and conducted. Excellent calibration results are achieved based on the calibration model. To summarize, the error analysis approach and the calibration method are proved to be adequate and precise, and could provide an important guarantee for the design, manufacture, and measurement of high-accuracy star trackers. PMID:23567527

A robust approach to using of the redundant information in the temperature calibration

NASA Astrophysics Data System (ADS)

Strnad, R.; Kňazovická, L.; Šindelář, M.; Kukal, J.

2013-09-01

In the calibration laboratories are used standard procedures for calculating of the calibration model coefficients based on well described standards (EN 60751, ITS-90, EN 60584, etc.). In practice, sensors are mostly calibrated in more points and redundant information is used as a validation of the model. This paper will present the influence of including all measured points with respect to their uncertainties to the measured models using standard weighted least square methods. A special case with regards of the different level of the uncertainty of the measured points in case of the robust approach will be discussed. This will go to the different minimization criteria and different uncertainty propagation methodology. This approach also will eliminate of the influence of the outline measurements in the calibration. In practical part will be three cases of this approach presented, namely industrial calibration according to the standard EN 60751, SPRT according to the ITS-90 and thermocouple according to the standard EN 60584.

Development of theoretical oxygen saturation calibration curve based on optical density ratio and optical simulation approach

NASA Astrophysics Data System (ADS)

Jumadi, Nur Anida; Beng, Gan Kok; Ali, Mohd Alauddin Mohd; Zahedi, Edmond; Morsin, Marlia

2017-09-01

The implementation of surface-based Monte Carlo simulation technique for oxygen saturation (SaO2) calibration curve estimation is demonstrated in this paper. Generally, the calibration curve is estimated either from the empirical study using animals as the subject of experiment or is derived from mathematical equations. However, the determination of calibration curve using animal is time consuming and requires expertise to conduct the experiment. Alternatively, an optical simulation technique has been used widely in the biomedical optics field due to its capability to exhibit the real tissue behavior. The mathematical relationship between optical density (OD) and optical density ratios (ODR) associated with SaO2 during systole and diastole is used as the basis of obtaining the theoretical calibration curve. The optical properties correspond to systolic and diastolic behaviors were applied to the tissue model to mimic the optical properties of the tissues. Based on the absorbed ray flux at detectors, the OD and ODR were successfully calculated. The simulation results of optical density ratio occurred at every 20 % interval of SaO2 is presented with maximum error of 2.17 % when comparing it with previous numerical simulation technique (MC model). The findings reveal the potential of the proposed method to be used for extended calibration curve study using other wavelength pair.

A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area

USGS Publications Warehouse

Clarke, K.C.; Hoppen, S.; Gaydos, L.

1997-01-01

In this paper we describe a cellular automaton (CA) simulation model developed to predict urban growth as part of a project for estimating the regional and broader impact of urbanization on the San Francisco Bay area's climate. The rules of the model are more complex than those of a typical CA and involve the use of multiple data sources, including topography, road networks, and existing settlement distributions, and their modification over time. In addition, the control parameters of the model are allowed to self-modify: that is, the CA adapts itself to the circumstances it generates, in particular, during periods of rapid growth or stagnation. In addition, the model was written to allow the accumulation of probabilistic estimates based on Monte Carlo methods. Calibration of the model has been accomplished by the use of historical maps to compare model predictions of urbanization, based solely upon the distribution in year 1900, with observed data for years 1940, 1954, 1962, 1974, and 1990. The complexity of this model has made calibration a particularly demanding step. Lessons learned about the methods, measures, and strategies developed to calibrate the model may be of use in other environmental modeling contexts. With the calibration complete, the model is being used to generate a set of future scenarios for the San Francisco Bay area along with their probabilities based on the Monte Carlo version of the model. Animated dynamic mapping of the simulations will be used to allow visualization of the impact of future urban growth.

A simple topography-driven, calibration-free runoff generation model

NASA Astrophysics Data System (ADS)

Gao, H.; Birkel, C.; Hrachowitz, M.; Tetzlaff, D.; Soulsby, C.; Savenije, H. H. G.

2017-12-01

Determining the amount of runoff generation from rainfall occupies a central place in rainfall-runoff modelling. Moreover, reading landscapes and developing calibration-free runoff generation models that adequately reflect land surface heterogeneities remains the focus of much hydrological research. In this study, we created a new method to estimate runoff generation - HAND-based Storage Capacity curve (HSC) which uses a topographic index (HAND, Height Above the Nearest Drainage) to identify hydrological similarity and partially the saturated areas of catchments. We then coupled the HSC model with the Mass Curve Technique (MCT) method to estimate root zone storage capacity (SuMax), and obtained the calibration-free runoff generation model HSC-MCT. Both the two models (HSC and HSC-MCT) allow us to estimate runoff generation and simultaneously visualize the spatial dynamic of saturated area. We tested the two models in the data-rich Bruntland Burn (BB) experimental catchment in Scotland with an unusual time series of the field-mapped saturation area extent. The models were subsequently tested in 323 MOPEX (Model Parameter Estimation Experiment) catchments in the United States. HBV and TOPMODEL were used as benchmarks. We found that the HSC performed better in reproducing the spatio-temporal pattern of the observed saturated areas in the BB catchment compared with TOPMODEL which is based on the topographic wetness index (TWI). The HSC also outperformed HBV and TOPMODEL in the MOPEX catchments for both calibration and validation. Despite having no calibrated parameters, the HSC-MCT model also performed comparably well with the calibrated HBV and TOPMODEL, highlighting the robustness of the HSC model to both describe the spatial distribution of the root zone storage capacity and the efficiency of the MCT method to estimate the SuMax. Moreover, the HSC-MCT model facilitated effective visualization of the saturated area, which has the potential to be used for broader geoscience studies beyond hydrology.

Water-Balance Model to Simulate Historical Lake Levels for Lake Merced, California

NASA Astrophysics Data System (ADS)

Maley, M. P.; Onsoy, S.; Debroux, J.; Eagon, B.

2009-12-01

Lake Merced is a freshwater lake located in southwestern San Francisco, California. In the late 1980s and early 1990s, an extended, severe drought impacted the area that resulted in significant declines in Lake Merced lake levels that raised concerns about the long-term health of the lake. In response to these concerns, the Lake Merced Water Level Restoration Project was developed to evaluate an engineered solution to increase and maintain Lake Merced lake levels. The Lake Merced Lake-Level Model was developed to support the conceptual engineering design to restore lake levels. It is a spreadsheet-based water-balance model that performs monthly water-balance calculations based on the hydrological conceptual model. The model independently calculates each water-balance component based on available climate and hydrological data. The model objective was to develop a practical, rule-based approach for the water balance and to calibrate the model results to measured lake levels. The advantage of a rule-based approach is that once the rules are defined, they enhance the ability to then adapt the model for use in future-case simulations. The model was calibrated to historical lake levels over a 70-year period from 1939 to 2009. Calibrating the model over this long historical range tested the model over a variety of hydrological conditions including wet, normal and dry precipitation years, flood events, and periods of high and low lake levels. The historical lake level range was over 16 feet. The model calibration of historical to simulated lake levels had a residual mean of 0.02 feet and an absolute residual mean of 0.42 feet. More importantly, the model demonstrated the ability to simulate both long-term and short-term trends with a strong correlation of the magnitude for both annual and seasonal fluctuations in lake levels. The calibration results demonstrate an improved conceptual understanding of the key hydrological factors that control lake levels, reduce uncertainty in the hydrological conceptual model, and increase confidence in the model’s ability to forecast future lake conditions. The Lake Merced Lake-Level Model will help decision-makers with a straightforward, practical analysis of the major contributions to lake-level declines that can be used to support engineering, environmental and other decisions.

Beyond discrimination: A comparison of calibration methods and clinical usefulness of predictive models of readmission risk.

PubMed

Walsh, Colin G; Sharman, Kavya; Hripcsak, George

2017-12-01

Prior to implementing predictive models in novel settings, analyses of calibration and clinical usefulness remain as important as discrimination, but they are not frequently discussed. Calibration is a model's reflection of actual outcome prevalence in its predictions. Clinical usefulness refers to the utilities, costs, and harms of using a predictive model in practice. A decision analytic approach to calibrating and selecting an optimal intervention threshold may help maximize the impact of readmission risk and other preventive interventions. To select a pragmatic means of calibrating predictive models that requires a minimum amount of validation data and that performs well in practice. To evaluate the impact of miscalibration on utility and cost via clinical usefulness analyses. Observational, retrospective cohort study with electronic health record data from 120,000 inpatient admissions at an urban, academic center in Manhattan. The primary outcome was thirty-day readmission for three causes: all-cause, congestive heart failure, and chronic coronary atherosclerotic disease. Predictive modeling was performed via L1-regularized logistic regression. Calibration methods were compared including Platt Scaling, Logistic Calibration, and Prevalence Adjustment. Performance of predictive modeling and calibration was assessed via discrimination (c-statistic), calibration (Spiegelhalter Z-statistic, Root Mean Square Error [RMSE] of binned predictions, Sanders and Murphy Resolutions of the Brier Score, Calibration Slope and Intercept), and clinical usefulness (utility terms represented as costs). The amount of validation data necessary to apply each calibration algorithm was also assessed. C-statistics by diagnosis ranged from 0.7 for all-cause readmission to 0.86 (0.78-0.93) for congestive heart failure. Logistic Calibration and Platt Scaling performed best and this difference required analyzing multiple metrics of calibration simultaneously, in particular Calibration Slopes and Intercepts. Clinical usefulness analyses provided optimal risk thresholds, which varied by reason for readmission, outcome prevalence, and calibration algorithm. Utility analyses also suggested maximum tolerable intervention costs, e.g., $1720 for all-cause readmissions based on a published cost of readmission of $11,862. Choice of calibration method depends on availability of validation data and on performance. Improperly calibrated models may contribute to higher costs of intervention as measured via clinical usefulness. Decision-makers must understand underlying utilities or costs inherent in the use-case at hand to assess usefulness and will obtain the optimal risk threshold to trigger intervention with intervention cost limits as a result. Copyright © 2017 Elsevier Inc. All rights reserved.

Calibration of Two-dimensional Floodplain Modeling in the Atchafalaya River Basin Using SAR Interferometry

NASA Technical Reports Server (NTRS)

Jung, Hahn Chul; Jasinski, Michael; Kim, Jin-Woo; Shum, C. K.; Bates, Paul; Lee, Hgongki; Neal, Jeffrey; Alsdorf, Doug

2012-01-01

Two-dimensional (2D) satellite imagery has been increasingly employed to improve prediction of floodplain inundation models. However, most focus has been on validation of inundation extent, with little attention on the 2D spatial variations of water elevation and slope. The availability of high resolution Interferometric Synthetic Aperture Radar (InSAR) imagery offers unprecedented opportunity for quantitative validation of surface water heights and slopes derived from 2D hydrodynamic models. In this study, the LISFLOOD-ACC hydrodynamic model is applied to the central Atchafalaya River Basin, Louisiana, during high flows typical of spring floods in the Mississippi Delta region, for the purpose of demonstrating the utility of InSAR in coupled 1D/2D model calibration. Two calibration schemes focusing on Manning s roughness are compared. First, the model is calibrated in terms of water elevations at a single in situ gage during a 62 day simulation period from 1 April 2008 to 1 June 2008. Second, the model is calibrated in terms of water elevation changes calculated from ALOS PALSAR interferometry during 46 days of the image acquisition interval from 16 April 2008 to 1 June 2009. The best-fit models show that the mean absolute errors are 3.8 cm for a single in situ gage calibration and 5.7 cm/46 days for InSAR water level calibration. The optimum values of Manning's roughness coefficients are 0.024/0.10 for the channel/floodplain, respectively, using a single in situ gage, and 0.028/0.10 for channel/floodplain the using SAR. Based on the calibrated water elevation changes, daily storage changes within the size of approx 230 sq km of the model area are also calculated to be of the order of 107 cubic m/day during high water of the modeled period. This study demonstrates the feasibility of SAR interferometry to support 2D hydrodynamic model calibration and as a tool for improved understanding of complex floodplain hydrodynamics

A numerical identifiability test for state-space models--application to optimal experimental design.

PubMed

Hidalgo, M E; Ayesa, E

2001-01-01

This paper describes a mathematical tool for identifiability analysis, easily applicable to high order non-linear systems modelled in state-space and implementable in simulators with a time-discrete approach. This procedure also permits a rigorous analysis of the expected estimation errors (average and maximum) in calibration experiments. The methodology is based on the recursive numerical evaluation of the information matrix during the simulation of a calibration experiment and in the setting-up of a group of information parameters based on geometric interpretations of this matrix. As an example of the utility of the proposed test, the paper presents its application to an optimal experimental design of ASM Model No. 1 calibration, in order to estimate the maximum specific growth rate microH and the concentration of heterotrophic biomass XBH.

Dynamic calibration approach for determining catechins and gallic acid in green tea using LC-ESI/MS.

PubMed

Bedner, Mary; Duewer, David L

2011-08-15

Catechins and gallic acid are antioxidant constituents of Camellia sinensis, or green tea. Liquid chromatography with both ultraviolet (UV) absorbance and electrospray ionization mass spectrometric (ESI/MS) detection was used to determine catechins and gallic acid in three green tea matrix materials that are commonly used as dietary supplements. The results from both detection modes were evaluated with 14 quantitation models, all of which were based on the analyte response relative to an internal standard. Half of the models were static, where quantitation was achieved with calibration factors that were constant over an analysis set. The other half were dynamic, with calibration factors calculated from interpolated response factor data at each time a sample was injected to correct for potential variations in analyte response over time. For all analytes, the relatively nonselective UV responses were found to be very stable over time and independent of the calibrant concentration; comparable results with low variability were obtained regardless of the quantitation model used. Conversely, the highly selective MS responses were found to vary both with time and as a function of the calibrant concentration. A dynamic quantitation model based on polynomial data-fitting was used to reduce the variability in the quantitative results using the MS data.

Information footprint of different ecohydrological data sources: using multi-objective calibration of a physically-based model as hypothesis testing

NASA Astrophysics Data System (ADS)

Kuppel, S.; Soulsby, C.; Maneta, M. P.; Tetzlaff, D.

2017-12-01

The utility of field measurements to help constrain the model solution space and identify feasible model configurations has been an increasingly central issue in hydrological model calibration. Sufficiently informative observations are necessary to ensure that the goodness of model-data fit attained effectively translates into more physically-sound information for the internal model parameters, as a basis for model structure evaluation. Here we assess to which extent the diversity of information content can inform on the suitability of a complex, process-based ecohydrological model to simulate key water flux and storage dynamics at a long-term research catchment in the Scottish Highlands. We use the fully-distributed ecohydrological model EcH2O, calibrated against long-term datasets that encompass hydrologic and energy exchanges and ecological measurements: stream discharge, soil moisture, net radiation above canopy, and pine stand transpiration. Diverse combinations of these constraints were applied using a multi-objective cost function specifically designed to avoid compensatory effects between model-data metrics. Results revealed that calibration against virtually all datasets enabled the model to reproduce streamflow reasonably well. However, parameterizing the model to adequately capture local flux and storage dynamics, such as soil moisture or transpiration, required calibration with specific observations. This indicates that the footprint of the information contained in observations varies for each type of dataset, and that a diverse database informing about the different compartments of the domain, is critical to test hypotheses of catchment function and identify a consistent model parameterization. The results foster confidence in using EcH2O to help understanding current and future ecohydrological couplings in Northern catchments.

Importance of land use update during the calibration period and simulation of water balance response to land use change in the upper Rio das Mortes Catchment (Cerrado Biome, Central-Western Brazil)

NASA Astrophysics Data System (ADS)

Lamparter, Gabriele; Kovacs, Kristof; Nobrega, Rodolfo; Gerold, Gerhard

2015-04-01

Changes in the hydrological balance and following degradation of the water ecosystem services due to large scale land use changes are reported from agricultural frontiers all over the world. Traditionally, hydrological models including vegetation and land use as a part of the hydrological cycle use a fixed distribution of land use for the calibration period. We believe that a meaningful calibration - especially when investigating the effects of land use change on hydrology - demands the inclusion of land use change during the calibration period into the calibration procedure. The SWAT (Soil and Water Assessment Tool) model is a process-based, semi-distributed model calculating the different components of the water balance. The model bases on the definition of hydrological response units (HRUs) which are based on soil, vegetation and slope distribution. It specifically emphasises the role of land use and land management on the water balance. The Central-Western region of Brazil is one of the leading agricultural frontiers, which experienced rapid and radical deforestation and agricultural intensification in the last 40 years (from natural Cerrado savannah to cattle grazing to intensive corn and soya cropland). The land use history of the upper Rio das Mortes catchment (with 17500 km²) is reasonably well documented since the 1970th. At the same time there are almost continuous climate and runoff data available for the period between 1988 and 2011. Therefore, the work presented here shows the model calibration and validation of the SWAT model with the land use update function for three different periods (1988 to 1998, 1998 to 2007 and 2007 to 2011) in comparison with the same calibration periods using a steady state land use distribution. The use of the land use update function allows a clearer identification which changes in the discharge are due to climatic variability and which are due to changes in the vegetation cover. With land use update included into the calibration procedure, the impact of land use change on overall modelled runoff was more pronounced. For example, the accordance of modelled peak discharge improved for the period from 1988 to 1998 (with a decrease of primary Cerrado from 60 to 30 %) with the use of the land use update function compared to the steady state calibration. The effect for the following two periods 1998 to 2007 and 2007 to 2011 (with a decrease of primary Cerrado from 30 to 24 % and 24 to 19 % respectively) show only a small improvement of the model fit.

Calibrating a Rainfall-Runoff and Routing Model for the Continental United States

NASA Astrophysics Data System (ADS)

Jankowfsky, S.; Li, S.; Assteerawatt, A.; Tillmanns, S.; Hilberts, A.

2014-12-01

Catastrophe risk models are widely used in the insurance industry to estimate the cost of risk. The models consist of hazard models linked to vulnerability and financial loss models. In flood risk models, the hazard model generates inundation maps. In order to develop country wide inundation maps for different return periods a rainfall-runoff and routing model is run using stochastic rainfall data. The simulated discharge and runoff is then input to a two dimensional inundation model, which produces the flood maps. In order to get realistic flood maps, the rainfall-runoff and routing models have to be calibrated with observed discharge data. The rainfall-runoff model applied here is a semi-distributed model based on the Topmodel (Beven and Kirkby, 1979) approach which includes additional snowmelt and evapotranspiration models. The routing model is based on the Muskingum-Cunge (Cunge, 1969) approach and includes the simulation of lakes and reservoirs using the linear reservoir approach. Both models were calibrated using the multiobjective NSGA-II (Deb et al., 2002) genetic algorithm with NLDAS forcing data and around 4500 USGS discharge gauges for the period from 1979-2013. Additional gauges having no data after 1979 were calibrated using CPC rainfall data. The model performed well in wetter regions and shows the difficulty of simulating areas with sinks such as karstic areas or dry areas. Beven, K., Kirkby, M., 1979. A physically based, variable contributing area model of basin hydrology. Hydrol. Sci. Bull. 24 (1), 43-69. Cunge, J.A., 1969. On the subject of a flood propagation computation method (Muskingum method), J. Hydr. Research, 7(2), 205-230. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on evolutionary computation, 6(2), 182-197.

KINEROS2-AGWA: Model Use, Calibration, and Validation

NASA Technical Reports Server (NTRS)

Goodrich, D C.; Burns, I. S.; Unkrich, C. L.; Semmens, D. J.; Guertin, D. P.; Hernandez, M.; Yatheendradas, S.; Kennedy, J. R.; Levick, L. R..

2013-01-01

KINEROS (KINematic runoff and EROSion) originated in the 1960s as a distributed event-based model that conceptualizes a watershed as a cascade of overland flow model elements that flow into trapezoidal channel model elements. KINEROS was one of the first widely available watershed models that interactively coupled a finite difference approximation of the kinematic overland flow equations to a physically based infiltration model. Development and improvement of KINEROS continued from the 1960s on a variety of projects for a range of purposes, which has resulted in a suite of KINEROS-based modeling tools. This article focuses on KINEROS2 (K2), a spatially distributed, event-based watershed rainfall-runoff and erosion model, and the companion ArcGIS-based Automated Geospatial Watershed Assessment (AGWA) tool. AGWA automates the time-consuming tasks of watershed delineation into distributed model elements and initial parameterization of these elements using commonly available, national GIS data layers. A variety of approaches have been used to calibrate and validate K2 successfully across a relatively broad range of applications (e.g., urbanization, pre- and post-fire, hillslope erosion, erosion from roads, runoff and recharge, and manure transport). The case studies presented in this article (1) compare lumped to stepwise calibration and validation of runoff and sediment at plot, hillslope, and small watershed scales; and (2) demonstrate an uncalibrated application to address relative change in watershed response to wildfire.

KINEROS2/AGWA: Model use, calibration and validation

USGS Publications Warehouse

Goodrich, D.C.; Burns, I.S.; Unkrich, C.L.; Semmens, Darius J.; Guertin, D.P.; Hernandez, M.; Yatheendradas, S.; Kennedy, Jeffrey R.; Levick, Lainie R.

2012-01-01

KINEROS (KINematic runoff and EROSion) originated in the 1960s as a distributed event-based model that conceptualizes a watershed as a cascade of overland flow model elements that flow into trapezoidal channel model elements. KINEROS was one of the first widely available watershed models that interactively coupled a finite difference approximation of the kinematic overland flow equations to a physically based infiltration model. Development and improvement of KINEROS continued from the 1960s on a variety of projects for a range of purposes, which has resulted in a suite of KINEROS-based modeling tools. This article focuses on KINEROS2 (K2), a spatially distributed, event-based watershed rainfall-runoff and erosion model, and the companion ArcGIS-based Automated Geospatial Watershed Assessment (AGWA) tool. AGWA automates the time-consuming tasks of watershed delineation into distributed model elements and initial parameterization of these elements using commonly available, national GIS data layers. A variety of approaches have been used to calibrate and validate K2 successfully across a relatively broad range of applications (e.g., urbanization, pre- and post-fire, hillslope erosion, erosion from roads, runoff and recharge, and manure transport). The case studies presented in this article (1) compare lumped to stepwise calibration and validation of runoff and sediment at plot, hillslope, and small watershed scales; and (2) demonstrate an uncalibrated application to address relative change in watershed response to wildfire.

New Multi-objective Uncertainty-based Algorithm for Water Resource Models' Calibration

NASA Astrophysics Data System (ADS)

Keshavarz, Kasra; Alizadeh, Hossein

2017-04-01

Water resource models are powerful tools to support water management decision making process and are developed to deal with a broad range of issues including land use and climate change impacts analysis, water allocation, systems design and operation, waste load control and allocation, etc. These models are divided into two categories of simulation and optimization models whose calibration has been addressed in the literature where great relevant efforts in recent decades have led to two main categories of auto-calibration methods of uncertainty-based algorithms such as GLUE, MCMC and PEST and optimization-based algorithms including single-objective optimization such as SCE-UA and multi-objective optimization such as MOCOM-UA and MOSCEM-UA. Although algorithms which benefit from capabilities of both types, such as SUFI-2, were rather developed, this paper proposes a new auto-calibration algorithm which is capable of both finding optimal parameters values regarding multiple objectives like optimization-based algorithms and providing interval estimations of parameters like uncertainty-based algorithms. The algorithm is actually developed to improve quality of SUFI-2 results. Based on a single-objective, e.g. NSE and RMSE, SUFI-2 proposes a routine to find the best point and interval estimation of parameters and corresponding prediction intervals (95 PPU) of time series of interest. To assess the goodness of calibration, final results are presented using two uncertainty measures of p-factor quantifying percentage of observations covered by 95PPU and r-factor quantifying degree of uncertainty, and the analyst has to select the point and interval estimation of parameters which are actually non-dominated regarding both of the uncertainty measures. Based on the described properties of SUFI-2, two important questions are raised, answering of which are our research motivation: Given that in SUFI-2, final selection is based on the two measures or objectives and on the other hand, knowing that there is no multi-objective optimization mechanism in SUFI-2, are the final estimations Pareto-optimal? Can systematic methods be applied to select the final estimations? Dealing with these questions, a new auto-calibration algorithm was proposed where the uncertainty measures were considered as two objectives to find non-dominated interval estimations of parameters by means of coupling Monte Carlo simulation and Multi-Objective Particle Swarm Optimization. Both the proposed algorithm and SUFI-2 were applied to calibrate parameters of water resources planning model of Helleh river basin, Iran. The model is a comprehensive water quantity-quality model developed in the previous researches using WEAP software in order to analyze the impacts of different water resources management strategies including dam construction, increasing cultivation area, utilization of more efficient irrigation technologies, changing crop pattern, etc. Comparing the Pareto frontier resulted from the proposed auto-calibration algorithm with SUFI-2 results, it was revealed that the new algorithm leads to a better and also continuous Pareto frontier, even though it is more computationally expensive. Finally, Nash and Kalai-Smorodinsky bargaining methods were used to choose compromised interval estimation regarding Pareto frontier.

Rainier Mesa CAU Infiltration Model using INFILv3

DOE Office of Scientific and Technical Information (OSTI.GOV)

Levitt, Daniel G.; Kwicklis, Edward M.

The outline of this presentation are: (1) Model Inputs - DEM, Precipitation, Air temp, Soil props, Surface geology, Vegetation; (2) Model Pre-processing - Runoff Routing and sinks, Slope and Azimuth, Soil Ksat reduction with slope (to mitigate bathtub ring), Soil-Bedrock Interface permeabilities; (3) Model Calibration - ET using PEST, Chloride mass balance data, Streamflow using PEST; (4) Model Validation - Streamflow data not used for calibration; (5) Uncertainty Analysis; and (6) Results. Conclusions are: (1) Average annual infiltration rates =11 to 18 mm/year for RM domain; (2) Average annual infiltration rates = 7 to 11 mm/year for SM domain; (3)more » ET = 70% of precipitation for both domains; (4) Runoff = 8-9% for RM; and 22-24% for SM - Apparently high average runoff is caused by the truncation of the lowerelevation portions of watersheds where much of the infiltration of runoff waters would otherwise occur; (5) Model results are calibrated to measured ET, CMB data, and streamflow observations; (6) Model results are validated using streamflow observations discovered after model calibration was complete; (7) Use of soil Ksat reduction with slope to mitigate bathtub ring was successful (based on calibration results); and (8) Soil-bedrock K{_}interface is innovative approach.« less

Geometry calibration for x-ray equipment in radiation treatment devices and estimation of remaining patient alignment errors

NASA Astrophysics Data System (ADS)

Selby, Boris P.; Sakas, Georgios; Walter, Stefan; Stilla, Uwe

2008-03-01

Positioning a patient accurately in treatment devices is crucial for radiological treatment, especially if accuracy vantages of particle beam treatment are exploited. To avoid sub-millimeter misalignments, X-ray images acquired from within the device are compared to a CT to compute respective alignment corrections. Unfortunately, deviations of the underlying geometry model for the imaging system degrade the achievable accuracy. We propose an automatic calibration routine, which bases on the geometry of a phantom and its automatic detection in digital radiographs acquired for various geometric device settings during the calibration. The results from the registration of the phantom's X-ray projections and its known geometry are used to update the model of the respective beamlines, which is used to compute the patient alignment correction. The geometric calibration of a beamline takes all nine relevant degrees of freedom into account, including detector translations in three directions, detector tilt by three axes and three possible translations for the X-ray tube. Introducing a stochastic model for the calibration we are able to predict the patient alignment deviations resulting from inaccuracies inherent to the phantom design and the calibration. Comparisons of the alignment results for a treatment device without calibrated imaging systems and a calibrated device show that an accurate calibration can enhance alignment accuracy.

Improvement of Gaofen-3 Absolute Positioning Accuracy Based on Cross-Calibration

PubMed Central

Deng, Mingjun; Li, Jiansong

2017-01-01

The Chinese Gaofen-3 (GF-3) mission was launched in August 2016, equipped with a full polarimetric synthetic aperture radar (SAR) sensor in the C-band, with a resolution of up to 1 m. The absolute positioning accuracy of GF-3 is of great importance, and in-orbit geometric calibration is a key technology for improving absolute positioning accuracy. Conventional geometric calibration is used to accurately calibrate the geometric calibration parameters of the image (internal delay and azimuth shifts) using high-precision ground control data, which are highly dependent on the control data of the calibration field, but it remains costly and labor-intensive to monitor changes in GF-3’s geometric calibration parameters. Based on the positioning consistency constraint of the conjugate points, this study presents a geometric cross-calibration method for the rapid and accurate calibration of GF-3. The proposed method can accurately calibrate geometric calibration parameters without using corner reflectors and high-precision digital elevation models, thus improving absolute positioning accuracy of the GF-3 image. GF-3 images from multiple regions were collected to verify the absolute positioning accuracy after cross-calibration. The results show that this method can achieve a calibration accuracy as high as that achieved by the conventional field calibration method. PMID:29240675

A Bayesian approach for calibrating probability judgments

NASA Astrophysics Data System (ADS)

Firmino, Paulo Renato A.; Santana, Nielson A.

2012-10-01

Eliciting experts' opinions has been one of the main alternatives for addressing paucity of data. In the vanguard of this area is the development of calibration models (CMs). CMs are models dedicated to overcome miscalibration, i.e. judgment biases reflecting deficient strategies of reasoning adopted by the expert when inferring about an unknown. One of the main challenges of CMs is to determine how and when to intervene against miscalibration, in order to enhance the tradeoff between costs (time spent with calibration processes) and accuracy of the resulting models. The current paper dedicates special attention to this issue by presenting a dynamic Bayesian framework for monitoring, diagnosing, and handling miscalibration patterns. The framework is based on Beta-, Uniform, or Triangular-Bernoulli models and classes of judgmental calibration theories. Issues regarding the usefulness of the proposed framework are discussed and illustrated via simulation studies.

Calibration Transfer Between a Bench Scanning and a Submersible Diode Array Spectrophotometer for In Situ Wastewater Quality Monitoring in Sewer Systems.

PubMed

Brito, Rita S; Pinheiro, Helena M; Ferreira, Filipa; Matos, José S; Pinheiro, Alexandre; Lourenço, Nídia D

2016-03-01

Online monitoring programs based on spectroscopy have a high application potential for the detection of hazardous wastewater discharges in sewer systems. Wastewater hydraulics poses a challenge for in situ spectroscopy, especially when the system includes storm water connections leading to rapid changes in water depth, velocity, and in the water quality matrix. Thus, there is a need to optimize and fix the location of in situ instruments, limiting their availability for calibration. In this context, the development of calibration models on bench spectrophotometers to estimate wastewater quality parameters from spectra acquired with in situ instruments could be very useful. However, spectra contain information not only from the samples, but also from the spectrophotometer generally invalidating this approach. The use of calibration transfer methods is a promising solution to this problem. In this study, calibration models were developed using interval partial least squares (iPLS), for the estimation of total suspended solids (TSS) and chemical oxygen demand (COD) in sewage from Ultraviolet-visible spectra acquired in a bench scanning spectrophotometer. The feasibility of calibration transfer to a submersible, diode array equipment, to be subsequently operated in situ, was assessed using three procedures: slope and bias correction (SBC); single wavelength standardization (SWS) on mean spectra; and local centering (LC). The results showed that SBC was the most adequate for the available data, adding insignificant error to the base model estimates. Single wavelength standardization was a close second best, potentially more robust, and independent of the base iPLS model. Local centering was shown to be inadequate for the samples and instruments used. © The Author(s) 2016.

In-Flight Pitot-Static Calibration

NASA Technical Reports Server (NTRS)

Foster, John V. (Inventor); Cunningham, Kevin (Inventor)

2016-01-01

A GPS-based pitot-static calibration system uses global output-error optimization. High data rate measurements of static and total pressure, ambient air conditions, and GPS-based ground speed measurements are used to compute pitot-static pressure errors over a range of airspeed. System identification methods rapidly compute optimal pressure error models with defined confidence intervals.

Calibrating Parameters of Power System Stability Models using Advanced Ensemble Kalman Filter

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Renke; Diao, Ruisheng; Li, Yuanyuan

With the ever increasing penetration of renewable energy, smart loads, energy storage, and new market behavior, today’s power grid becomes more dynamic and stochastic, which may invalidate traditional study assumptions and pose great operational challenges. Thus, it is of critical importance to maintain good-quality models for secure and economic planning and real-time operation. Following the 1996 Western Systems Coordinating Council (WSCC) system blackout, North American Electric Reliability Corporation (NERC) and Western Electricity Coordinating Council (WECC) in North America enforced a number of policies and standards to guide the power industry to periodically validate power grid models and calibrate poor parametersmore » with the goal of building sufficient confidence in model quality. The PMU-based approach using online measurements without interfering with the operation of generators provides a low-cost alternative to meet NERC standards. This paper presents an innovative procedure and tool suites to validate and calibrate models based on a trajectory sensitivity analysis method and an advanced ensemble Kalman filter algorithm. The developed prototype demonstrates excellent performance in identifying and calibrating bad parameters of a realistic hydro power plant against multiple system events.« less

Identifying influential data points in hydrological model calibration and their impact on streamflow predictions

NASA Astrophysics Data System (ADS)

Wright, David; Thyer, Mark; Westra, Seth

2015-04-01

Highly influential data points are those that have a disproportionately large impact on model performance, parameters and predictions. However, in current hydrological modelling practice the relative influence of individual data points on hydrological model calibration is not commonly evaluated. This presentation illustrates and evaluates several influence diagnostics tools that hydrological modellers can use to assess the relative influence of data. The feasibility and importance of including influence detection diagnostics as a standard tool in hydrological model calibration is discussed. Two classes of influence diagnostics are evaluated: (1) computationally demanding numerical "case deletion" diagnostics; and (2) computationally efficient analytical diagnostics, based on Cook's distance. These diagnostics are compared against hydrologically orientated diagnostics that describe changes in the model parameters (measured through the Mahalanobis distance), performance (objective function displacement) and predictions (mean and maximum streamflow). These influence diagnostics are applied to two case studies: a stage/discharge rating curve model, and a conceptual rainfall-runoff model (GR4J). Removing a single data point from the calibration resulted in differences to mean flow predictions of up to 6% for the rating curve model, and differences to mean and maximum flow predictions of up to 10% and 17%, respectively, for the hydrological model. When using the Nash-Sutcliffe efficiency in calibration, the computationally cheaper Cook's distance metrics produce similar results to the case-deletion metrics at a fraction of the computational cost. However, Cooks distance is adapted from linear regression with inherit assumptions on the data and is therefore less flexible than case deletion. Influential point detection diagnostics show great potential to improve current hydrological modelling practices by identifying highly influential data points. The findings of this study establish the feasibility and importance of including influential point detection diagnostics as a standard tool in hydrological model calibration. They provide the hydrologist with important information on whether model calibration is susceptible to a small number of highly influent data points. This enables the hydrologist to make a more informed decision of whether to (1) remove/retain the calibration data; (2) adjust the calibration strategy and/or hydrological model to reduce the susceptibility of model predictions to a small number of influential observations.

Hierarchical calibration and validation framework of bench-scale computational fluid dynamics simulations for solvent-based carbon capture. Part 2: Chemical absorption across a wetted wall column: Original Research Article: Hierarchical calibration and validation framework of bench-scale computational fluid dynamics simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Chao; Xu, Zhijie; Lai, Kevin

Part 1 of this paper presents a numerical model for non-reactive physical mass transfer across a wetted wall column (WWC). In Part 2, we improved the existing computational fluid dynamics (CFD) model to simulate chemical absorption occurring in a WWC as a bench-scale study of solvent-based carbon dioxide (CO2) capture. To generate data for WWC model validation, CO2 mass transfer across a monoethanolamine (MEA) solvent was first measured on a WWC experimental apparatus. The numerical model developed in this work can account for both chemical absorption and desorption of CO2 in MEA. In addition, the overall mass transfer coefficient predictedmore » using traditional/empirical correlations is conducted and compared with CFD prediction results for both steady and wavy falling films. A Bayesian statistical calibration algorithm is adopted to calibrate the reaction rate constants in chemical absorption/desorption of CO2 across a falling film of MEA. The posterior distributions of the two transport properties, i.e., Henry's constant and gas diffusivity in the non-reacting nitrous oxide (N2O)/MEA system obtained from Part 1 of this study, serves as priors for the calibration of CO2 reaction rate constants after using the N2O/CO2 analogy method. The calibrated model can be used to predict the CO2 mass transfer in a WWC for a wider range of operating conditions.« less

Hierarchical calibration and validation framework of bench-scale computational fluid dynamics simulations for solvent-based carbon capture. Part 2: Chemical absorption across a wetted wall column: Original Research Article: Hierarchical calibration and validation framework of bench-scale computational fluid dynamics simulations

DOE PAGES

Wang, Chao; Xu, Zhijie; Lai, Kevin; ...

2017-10-24

Part 1 of this paper presents a numerical model for non-reactive physical mass transfer across a wetted wall column (WWC). In Part 2, we improved the existing computational fluid dynamics (CFD) model to simulate chemical absorption occurring in a WWC as a bench-scale study of solvent-based carbon dioxide (CO2) capture. To generate data for WWC model validation, CO2 mass transfer across a monoethanolamine (MEA) solvent was first measured on a WWC experimental apparatus. The numerical model developed in this work can account for both chemical absorption and desorption of CO2 in MEA. In addition, the overall mass transfer coefficient predictedmore » using traditional/empirical correlations is conducted and compared with CFD prediction results for both steady and wavy falling films. A Bayesian statistical calibration algorithm is adopted to calibrate the reaction rate constants in chemical absorption/desorption of CO2 across a falling film of MEA. The posterior distributions of the two transport properties, i.e., Henry's constant and gas diffusivity in the non-reacting nitrous oxide (N2O)/MEA system obtained from Part 1 of this study, serves as priors for the calibration of CO2 reaction rate constants after using the N2O/CO2 analogy method. The calibrated model can be used to predict the CO2 mass transfer in a WWC for a wider range of operating conditions.« less

Radiometric calibration status of Landsat-7 and Landsat-5

USGS Publications Warehouse

Barsi, J.A.; Markham, B.L.; Helder, D.L.; Chander, G.

2007-01-01

Launched in April 1999, Landsat-7 ETM+ continues to acquire data globally. The Scan Line Corrector in failure in 2003 has affected ground coverage and the recent switch to Bumper Mode operations in April 2007 has degraded the internal geometric accuracy of the data, but the radiometry has been unaffected. The best of the three on-board calibrators for the reflective bands, the Full Aperture Solar Calibrator, has indicated slow changes in the ETM+, but this is believed to be due to contamination on the panel rather then instrument degradation. The Internal Calibrator lamp 2, though it has not been used regularly throughout the whole mission, indicates smaller changes than the FASC since 2003. The changes indicated by lamp 2 are only statistically significant in band 1, circa 0.3% per year, and may be lamp as opposed to instrument degradations. Regular observations of desert targets in the Saharan and Arabian deserts indicate the no change in the ETM+ reflective band response, though the uncertainty is larger and does not preclude the small changes indicated by lamp 2. The thermal band continues to be stable and well-calibrated since an offset error was corrected in late-2000. Launched in 1984, Landsat-5 TM also continues to acquire global data; though without the benefit of an on-board recorder, data can only be acquired where a ground station is within range. Historically, the calibration of the TM reflective bands has used an onboard calibration system with multiple lamps. The calibration procedure for the TM reflective bands was updated in 2003 based on the best estimate at the time, using only one of the three lamps and a cross-calibration with Landsat-7 ETM+. Since then, the Saharan desert sites have been used to validate this calibration model. Problems were found with the lamp based model of up to 13% in band 1. Using the Saharan data, a new model was developed and implemented in the US processing system in April 2007. The TM thermal band was found to have a calibration offset error of 0.092 W/m 2 sr ??m (0.68K at 300K) based on vicarious calibration data between 1999 and 2006. The offset error was corrected in the US processing system on April 2007 for all data acquired since April 1999.

Real-time monitoring of high-gravity corn mash fermentation using in situ raman spectroscopy.

PubMed

Gray, Steven R; Peretti, Steven W; Lamb, H Henry

2013-06-01

In situ Raman spectroscopy was employed for real-time monitoring of simultaneous saccharification and fermentation (SSF) of corn mash by an industrial strain of Saccharomyces cerevisiae. An accurate univariate calibration model for ethanol was developed based on the very strong 883 cm(-1) C-C stretching band. Multivariate partial least squares (PLS) calibration models for total starch, dextrins, maltotriose, maltose, glucose, and ethanol were developed using data from eight batch fermentations and validated using predictions for a separate batch. The starch, ethanol, and dextrins models showed significant prediction improvement when the calibration data were divided into separate high- and low-concentration sets. Collinearity between the ethanol and starch models was avoided by excluding regions containing strong ethanol peaks from the starch model and, conversely, excluding regions containing strong saccharide peaks from the ethanol model. The two-set calibration models for starch (R(2)  = 0.998, percent error = 2.5%) and ethanol (R(2)  = 0.999, percent error = 2.1%) provide more accurate predictions than any previously published spectroscopic models. Glucose, maltose, and maltotriose are modeled to accuracy comparable to previous work on less complex fermentation processes. Our results demonstrate that Raman spectroscopy is capable of real time in situ monitoring of a complex industrial biomass fermentation. To our knowledge, this is the first PLS-based chemometric modeling of corn mash fermentation under typical industrial conditions, and the first Raman-based monitoring of a fermentation process with glucose, oligosaccharides and polysaccharides present. Copyright © 2013 Wiley Periodicals, Inc.

A calculation and uncertainty evaluation method for the effective area of a piston rod used in quasi-static pressure calibration

NASA Astrophysics Data System (ADS)

Gu, Tingwei; Kong, Deren; Shang, Fei; Chen, Jing

2018-04-01

This paper describes the merits and demerits of different sensors for measuring propellant gas pressure, the applicable range of the frequently used dynamic pressure calibration methods, and the working principle of absolute quasi-static pressure calibration based on the drop-weight device. The main factors affecting the accuracy of pressure calibration are analyzed from two aspects of the force sensor and the piston area. To calculate the effective area of the piston rod and evaluate the uncertainty between the force sensor and the corresponding peak pressure in the absolute quasi-static pressure calibration process, a method for solving these problems based on the least squares principle is proposed. According to the relevant quasi-static pressure calibration experimental data, the least squares fitting model between the peak force and the peak pressure, and the effective area of the piston rod and its measurement uncertainty, are obtained. The fitting model is tested by an additional group of experiments, and the peak pressure obtained by the existing high-precision comparison calibration method is taken as the reference value. The test results show that the peak pressure obtained by the least squares fitting model is closer to the reference value than the one directly calculated by the cross-sectional area of the piston rod. When the peak pressure is higher than 150 MPa, the percentage difference is less than 0.71%, which can meet the requirements of practical application.

Computer simulation of storm runoff for three watersheds in Albuquerque, New Mexico

USGS Publications Warehouse

Knutilla, R.L.; Veenhuis, J.E.

1994-01-01

Rainfall-runoff data from three watersheds were selected for calibration and verification of the U.S. Geological Survey's Distributed Routing Rainfall-Runoff Model. The watersheds chosen are residentially developed. The conceptually based model uses an optimization process that adjusts selected parameters to achieve the best fit between measured and simulated runoff volumes and peak discharges. Three of these optimization parameters represent soil-moisture conditions, three represent infiltration, and one accounts for effective impervious area. Each watershed modeled was divided into overland-flow segments and channel segments. The overland-flow segments were further subdivided to reflect pervious and impervious areas. Each overland-flow and channel segment was assigned representative values of area, slope, percentage of imperviousness, and roughness coefficients. Rainfall-runoff data for each watershed were separated into two sets for use in calibration and verification. For model calibration, seven input parameters were optimized to attain a best fit of the data. For model verification, parameter values were set using values from model calibration. The standard error of estimate for calibration of runoff volumes ranged from 19 to 34 percent, and for peak discharge calibration ranged from 27 to 44 percent. The standard error of estimate for verification of runoff volumes ranged from 26 to 31 percent, and for peak discharge verification ranged from 31 to 43 percent.

Evaluating the role of evapotranspiration remote sensing data in improving hydrological modeling predictability

NASA Astrophysics Data System (ADS)

Herman, Matthew R.; Nejadhashemi, A. Pouyan; Abouali, Mohammad; Hernandez-Suarez, Juan Sebastian; Daneshvar, Fariborz; Zhang, Zhen; Anderson, Martha C.; Sadeghi, Ali M.; Hain, Christopher R.; Sharifi, Amirreza

2018-01-01

As the global demands for the use of freshwater resources continues to rise, it has become increasingly important to insure the sustainability of this resources. This is accomplished through the use of management strategies that often utilize monitoring and the use of hydrological models. However, monitoring at large scales is not feasible and therefore model applications are becoming challenging, especially when spatially distributed datasets, such as evapotranspiration, are needed to understand the model performances. Due to these limitations, most of the hydrological models are only calibrated for data obtained from site/point observations, such as streamflow. Therefore, the main focus of this paper is to examine whether the incorporation of remotely sensed and spatially distributed datasets can improve the overall performance of the model. In this study, actual evapotranspiration (ETa) data was obtained from the two different sets of satellite based remote sensing data. One dataset estimates ETa based on the Simplified Surface Energy Balance (SSEBop) model while the other one estimates ETa based on the Atmosphere-Land Exchange Inverse (ALEXI) model. The hydrological model used in this study is the Soil and Water Assessment Tool (SWAT), which was calibrated against spatially distributed ETa and single point streamflow records for the Honeyoey Creek-Pine Creek Watershed, located in Michigan, USA. Two different techniques, multi-variable and genetic algorithm, were used to calibrate the SWAT model. Using the aforementioned datasets, the performance of the hydrological model in estimating ETa was improved using both calibration techniques by achieving Nash-Sutcliffe efficiency (NSE) values >0.5 (0.73-0.85), percent bias (PBIAS) values within ±25% (±21.73%), and root mean squared error - observations standard deviation ratio (RSR) values <0.7 (0.39-0.52). However, the genetic algorithm technique was more effective with the ETa calibration while significantly reducing the model performance for estimating the streamflow (NSE: 0.32-0.52, PBIAS: ±32.73%, and RSR: 0.63-0.82). Meanwhile, using the multi-variable technique, the model performance for estimating the streamflow was maintained with a high level of accuracy (NSE: 0.59-0.61, PBIAS: ±13.70%, and RSR: 0.63-0.64) while the evapotranspiration estimations were improved. Results from this assessment shows that incorporation of remotely sensed and spatially distributed data can improve the hydrological model performance if it is coupled with a right calibration technique.

Joint Calibration of 3d Laser Scanner and Digital Camera Based on Dlt Algorithm

NASA Astrophysics Data System (ADS)

Gao, X.; Li, M.; Xing, L.; Liu, Y.

2018-04-01

Design a calibration target that can be scanned by 3D laser scanner while shot by digital camera, achieving point cloud and photos of a same target. A method to joint calibrate 3D laser scanner and digital camera based on Direct Linear Transformation algorithm was proposed. This method adds a distortion model of digital camera to traditional DLT algorithm, after repeating iteration, it can solve the inner and external position element of the camera as well as the joint calibration of 3D laser scanner and digital camera. It comes to prove that this method is reliable.

The hydrological calibration and validation of a complexly-linked watershed reservoir model for the Occoquan watershed, Virginia

NASA Astrophysics Data System (ADS)

Xu, Zhongyan; Godrej, Adil N.; Grizzard, Thomas J.

2007-10-01

SummaryRunoff models such as HSPF and reservoir models such as CE-QUAL-W2 are used to model water quality in watersheds. Most often, the models are independently calibrated to observed data. While this approach can achieve good calibration, it does not replicate the physically-linked nature of the system. When models are linked by using the model output from an upstream model as input to a downstream model, the physical reality of a continuous watershed, where the overland and waterbody portions are parts of the whole, is better represented. There are some additional challenges in the calibration of such linked models, because the aim is to simulate the entire system as a whole, rather than piecemeal. When public entities are charged with model development, one of the driving forces is to use public-domain models. This paper describes the use of two such models, HSPF and CE-QUAL-W2, in the linked modeling of the Occoquan watershed located in northern Virginia, USA. The description of the process is provided, and results from the hydrological calibration and validation are shown. The Occoquan model consists of six HSPF and two CE-QUAL-W2 models, linked in a complex way, to simulate two major reservoirs and the associated drainage areas. The overall linked model was calibrated for a three-year period and validated for a two-year period. The results show that a successful calibration can be achieved using the linked approach, with moderate additional effort. Overall flow balances based on the three-year calibration period at four stream stations showed agreement ranging from -3.95% to +3.21%. Flow balances for the two reservoirs, compared via the daily water surface elevations, also showed good agreement ( R2 values of 0.937 for Lake Manassas and 0.926 for Occoquan Reservoir), when missing (un-monitored) flows were included. Validation of the models ranged from poor to fair for the watershed models and excellent for the waterbody models, thus indicating that the current model can be used to explore waterbody issues, but should be used with appropriate care for watershed issues. The study objective of being able to use the Occoquan model to study the impact of land use changes on hydrodynamics and water quality in the waterbodies, particularly the Occoquan Reservoir, can be met with the current model. However, appropriate judgment should be exercised when using the model for the prediction of watershed runoff. One of the advantages of using the linked approach is to develop a direct linkage between upstream land use changes and downstream water quality. This makes it easier for decision-makers to evaluate alternative watershed management plans and for the public to understand the decision-making process. The successful calibration of hydrology provides a solid base for further model development and application.

Calibration of hydrological models using flow-duration curves

NASA Astrophysics Data System (ADS)

Westerberg, I. K.; Guerrero, J.-L.; Younger, P. M.; Beven, K. J.; Seibert, J.; Halldin, S.; Freer, J. E.; Xu, C.-Y.

2011-07-01

The degree of belief we have in predictions from hydrologic models will normally depend on how well they can reproduce observations. Calibrations with traditional performance measures, such as the Nash-Sutcliffe model efficiency, are challenged by problems including: (1) uncertain discharge data, (2) variable sensitivity of different performance measures to different flow magnitudes, (3) influence of unknown input/output errors and (4) inability to evaluate model performance when observation time periods for discharge and model input data do not overlap. This paper explores a calibration method using flow-duration curves (FDCs) to address these problems. The method focuses on reproducing the observed discharge frequency distribution rather than the exact hydrograph. It consists of applying limits of acceptability for selected evaluation points (EPs) on the observed uncertain FDC in the extended GLUE approach. Two ways of selecting the EPs were tested - based on equal intervals of discharge and of volume of water. The method was tested and compared to a calibration using the traditional model efficiency for the daily four-parameter WASMOD model in the Paso La Ceiba catchment in Honduras and for Dynamic TOPMODEL evaluated at an hourly time scale for the Brue catchment in Great Britain. The volume method of selecting EPs gave the best results in both catchments with better calibrated slow flow, recession and evaporation than the other criteria. Observed and simulated time series of uncertain discharges agreed better for this method both in calibration and prediction in both catchments. An advantage with the method is that the rejection criterion is based on an estimation of the uncertainty in discharge data and that the EPs of the FDC can be chosen to reflect the aims of the modelling application, e.g. using more/less EPs at high/low flows. While the method appears less sensitive to epistemic input/output errors than previous use of limits of acceptability applied directly to the time series of discharge, it still requires a reasonable representation of the distribution of inputs. Additional constraints might therefore be required in catchments subject to snow and where peak-flow timing at sub-daily time scales is of high importance. The results suggest that the calibration method can be useful when observation time periods for discharge and model input data do not overlap. The method could also be suitable for calibration to regional FDCs while taking uncertainties in the hydrological model and data into account.

Calibration of hydrological models using flow-duration curves

NASA Astrophysics Data System (ADS)

Westerberg, I. K.; Guerrero, J.-L.; Younger, P. M.; Beven, K. J.; Seibert, J.; Halldin, S.; Freer, J. E.; Xu, C.-Y.

2010-12-01

The degree of belief we have in predictions from hydrologic models depends on how well they can reproduce observations. Calibrations with traditional performance measures such as the Nash-Sutcliffe model efficiency are challenged by problems including: (1) uncertain discharge data, (2) variable importance of the performance with flow magnitudes, (3) influence of unknown input/output errors and (4) inability to evaluate model performance when observation time periods for discharge and model input data do not overlap. A new calibration method using flow-duration curves (FDCs) was developed which addresses these problems. The method focuses on reproducing the observed discharge frequency distribution rather than the exact hydrograph. It consists of applying limits of acceptability for selected evaluation points (EPs) of the observed uncertain FDC in the extended GLUE approach. Two ways of selecting the EPs were tested - based on equal intervals of discharge and of volume of water. The method was tested and compared to a calibration using the traditional model efficiency for the daily four-parameter WASMOD model in the Paso La Ceiba catchment in Honduras and for Dynamic TOPMODEL evaluated at an hourly time scale for the Brue catchment in Great Britain. The volume method of selecting EPs gave the best results in both catchments with better calibrated slow flow, recession and evaporation than the other criteria. Observed and simulated time series of uncertain discharges agreed better for this method both in calibration and prediction in both catchments without resulting in overpredicted simulated uncertainty. An advantage with the method is that the rejection criterion is based on an estimation of the uncertainty in discharge data and that the EPs of the FDC can be chosen to reflect the aims of the modelling application e.g. using more/less EPs at high/low flows. While the new method is less sensitive to epistemic input/output errors than the normal use of limits of acceptability applied directly to the time series of discharge, it still requires a reasonable representation of the distribution of inputs. Additional constraints might therefore be required in catchments subject to snow. The results suggest that the new calibration method can be useful when observation time periods for discharge and model input data do not overlap. The new method could also be suitable for calibration to regional FDCs while taking uncertainties in the hydrological model and data into account.

Multi-gauge Calibration for modeling the Semi-Arid Santa Cruz Watershed in Arizona-Mexico Border Area Using SWAT

USGS Publications Warehouse

Niraula, Rewati; Norman, Laura A.; Meixner, Thomas; Callegary, James B.

2012-01-01

In most watershed-modeling studies, flow is calibrated at one monitoring site, usually at the watershed outlet. Like many arid and semi-arid watersheds, the main reach of the Santa Cruz watershed, located on the Arizona-Mexico border, is discontinuous for most of the year except during large flood events, and therefore the flow characteristics at the outlet do not represent the entire watershed. Calibration is required at multiple locations along the Santa Cruz River to improve model reliability. The objective of this study was to best portray surface water flow in this semiarid watershed and evaluate the effect of multi-gage calibration on flow predictions. In this study, the Soil and Water Assessment Tool (SWAT) was calibrated at seven monitoring stations, which improved model performance and increased the reliability of flow, in the Santa Cruz watershed. The most sensitive parameters to affect flow were found to be curve number (CN2), soil evaporation and compensation coefficient (ESCO), threshold water depth in shallow aquifer for return flow to occur (GWQMN), base flow alpha factor (Alpha_Bf), and effective hydraulic conductivity of the soil layer (Ch_K2). In comparison, when the model was established with a single calibration at the watershed outlet, flow predictions at other monitoring gages were inaccurate. This study emphasizes the importance of multi-gage calibration to develop a reliable watershed model in arid and semiarid environments. The developed model, with further calibration of water quality parameters will be an integral part of the Santa Cruz Watershed Ecosystem Portfolio Model (SCWEPM), an online decision support tool, to assess the impacts of climate change and urban growth in the Santa Cruz watershed.

Parameter estimation for groundwater models under uncertain irrigation data

USGS Publications Warehouse

Demissie, Yonas; Valocchi, Albert J.; Cai, Ximing; Brozovic, Nicholas; Senay, Gabriel; Gebremichael, Mekonnen

2015-01-01

The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression-based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least-squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least-squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least-squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p < 0.05) bias in estimated parameters and model predictions that persist despite calibrating the models to different calibration data and sample sizes. However, by directly accounting for the irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes.

Simultaneous estimation of diet composition and calibration coefficients with fatty acid signature data

USGS Publications Warehouse

Bromaghin, Jeffrey F.; Budge, Suzanne M.; Thiemann, Gregory W.; Rode, Karyn D.

2017-01-01

Knowledge of animal diets provides essential insights into their life history and ecology, although diet estimation is challenging and remains an active area of research. Quantitative fatty acid signature analysis (QFASA) has become a popular method of estimating diet composition, especially for marine species. A primary assumption of QFASA is that constants called calibration coefficients, which account for the differential metabolism of individual fatty acids, are known. In practice, however, calibration coefficients are not known, but rather have been estimated in feeding trials with captive animals of a limited number of model species. The impossibility of verifying the accuracy of feeding trial derived calibration coefficients to estimate the diets of wild animals is a foundational problem with QFASA that has generated considerable criticism. We present a new model that allows simultaneous estimation of diet composition and calibration coefficients based only on fatty acid signature samples from wild predators and potential prey. Our model performed almost flawlessly in four tests with constructed examples, estimating both diet proportions and calibration coefficients with essentially no error. We also applied the model to data from Chukchi Sea polar bears, obtaining diet estimates that were more diverse than estimates conditioned on feeding trial calibration coefficients. Our model avoids bias in diet estimates caused by conditioning on inaccurate calibration coefficients, invalidates the primary criticism of QFASA, eliminates the need to conduct feeding trials solely for diet estimation, and consequently expands the utility of fatty acid data to investigate aspects of ecology linked to animal diets.

Natural streamflow simulation for two largest river basins in Poland: a baseline for identification of flow alterations

NASA Astrophysics Data System (ADS)

Piniewski, Mikołaj

2016-05-01

The objective of this study was to apply a previously developed large-scale and high-resolution SWAT model of the Vistula and the Odra basins, calibrated with the focus of natural flow simulation, in order to assess the impact of three different dam reservoirs on streamflow using the Indicators of Hydrologic Alteration (IHA). A tailored spatial calibration approach was designed, in which calibration was focused on a large set of relatively small non-nested sub-catchments with semi-natural flow regime. These were classified into calibration clusters based on the flow statistics similarity. After performing calibration and validation that gave overall positive results, the calibrated parameter values were transferred to the remaining part of the basins using an approach based on hydrological similarity of donor and target catchments. The calibrated model was applied in three case studies with the purpose of assessing the effect of dam reservoirs (Włocławek, Siemianówka and Czorsztyn Reservoirs) on streamflow alteration. Both the assessment based on gauged streamflow (Before-After design) and the one based on simulated natural streamflow showed large alterations in selected flow statistics related to magnitude, duration, high and low flow pulses and rate of change. Some benefits of using a large-scale and high-resolution hydrological model for the assessment of streamflow alteration include: (1) providing an alternative or complementary approach to the classical Before-After designs, (2) isolating the climate variability effect from the dam (or any other source of alteration) effect, (3) providing a practical tool that can be applied at a range of spatial scales over large area such as a country, in a uniform way. Thus, presented approach can be applied for designing more natural flow regimes, which is crucial for river and floodplain ecosystem restoration in the context of the European Union's policy on environmental flows.

Sediment transport in forested head water catchments - Calibration and validation of a soil erosion and landscape evolution model

NASA Astrophysics Data System (ADS)

Hancock, G. R.; Webb, A. A.; Turner, L.

2017-11-01

Sediment transport and soil erosion can be determined by a variety of field and modelling approaches. Computer based soil erosion and landscape evolution models (LEMs) offer the potential to be reliable assessment and prediction tools. An advantage of such models is that they provide both erosion and deposition patterns as well as total catchment sediment output. However, before use, like all models they require calibration and validation. In recent years LEMs have been used for a variety of both natural and disturbed landscape assessment. However, these models have not been evaluated for their reliability in steep forested catchments. Here, the SIBERIA LEM is calibrated and evaluated for its reliability for two steep forested catchments in south-eastern Australia. The model is independently calibrated using two methods. Firstly, hydrology and sediment transport parameters are inferred from catchment geomorphology and soil properties and secondly from catchment sediment transport and discharge data. The results demonstrate that both calibration methods provide similar parameters and reliable modelled sediment transport output. A sensitivity study of the input parameters demonstrates the model's sensitivity to correct parameterisation and also how the model could be used to assess potential timber harvesting as well as the removal of vegetation by fire.

Simulation of daily streamflows at gaged and ungaged locations within the Cedar River Basin, Iowa, using a Precipitation-Runoff Modeling System model

USGS Publications Warehouse

Christiansen, Daniel E.

2012-01-01

The U.S. Geological Survey, in cooperation with the Iowa Department of Natural Resources, conducted a study to examine techniques for estimation of daily streamflows using hydrological models and statistical methods. This report focuses on the use of a hydrologic model, the U.S. Geological Survey's Precipitation-Runoff Modeling System, to estimate daily streamflows at gaged and ungaged locations. The Precipitation-Runoff Modeling System is a modular, physically based, distributed-parameter modeling system developed to evaluate the impacts of various combinations of precipitation, climate, and land use on surface-water runoff and general basin hydrology. The Cedar River Basin was selected to construct a Precipitation-Runoff Modeling System model that simulates the period from January 1, 2000, to December 31, 2010. The calibration period was from January 1, 2000, to December 31, 2004, and the validation periods were from January 1, 2005, to December 31, 2010 and January 1, 2000 to December 31, 2010. A Geographic Information System tool was used to delineate the Cedar River Basin and subbasins for the Precipitation-Runoff Modeling System model and to derive parameters based on the physical geographical features. Calibration of the Precipitation-Runoff Modeling System model was completed using a U.S. Geological Survey calibration software tool. The main objective of the calibration was to match the daily streamflow simulated by the Precipitation-Runoff Modeling System model with streamflow measured at U.S. Geological Survey streamflow gages. The Cedar River Basin daily streamflow model performed with a Nash-Sutcliffe efficiency ranged from 0.82 to 0.33 during the calibration period, and a Nash-Sutcliffe efficiency ranged from 0.77 to -0.04 during the validation period. The Cedar River Basin model is meeting the criteria of greater than 0.50 Nash-Sutcliffe and is a good fit for streamflow conditions for the calibration period at all but one location, Austin, Minnesota. The Precipitation-Runoff Modeling System model accurately simulated streamflow at four of six uncalibrated sites within the basin. Overall, there was good agreement between simulated and measured seasonal and annual volumes throughout the basin for calibration and validation sites. The calibration period ranged from 0.2 to 20.8 percent difference, and the validation period ranged from 0.0 to 19.5 percent difference across all seasons and total annual runoff. The Precipitation-Runoff Modeling System model tended to underestimate lower streamflows compared to the observed streamflow values. This is an indication that the Precipitation-Runoff Modeling model needs more detailed groundwater and storage information to properly model the low-flow conditions in the Cedar River Basin.

Parameter Set Cloning Based on Catchment Similarity for Large-scale Hydrologic Modeling

NASA Astrophysics Data System (ADS)

Liu, Z.; Kaheil, Y.; McCollum, J.

2016-12-01

Parameter calibration is a crucial step to ensure the accuracy of hydrological models. However, streamflow gauges are not available everywhere for calibrating a large-scale hydrologic model globally. Thus, assigning parameters appropriately for regions where the calibration cannot be performed directly has been a challenge for large-scale hydrologic modeling. Here we propose a method to estimate the model parameters in ungauged regions based on the values obtained through calibration in areas where gauge observations are available. This parameter set cloning is performed according to a catchment similarity index, a weighted sum index based on four catchment characteristic attributes. These attributes are IPCC Climate Zone, Soil Texture, Land Cover, and Topographic Index. The catchments with calibrated parameter values are donors, while the uncalibrated catchments are candidates. Catchment characteristic analyses are first conducted for both donors and candidates. For each attribute, we compute a characteristic distance between donors and candidates. Next, for each candidate, weights are assigned to the four attributes such that higher weights are given to properties that are more directly linked to the hydrologic dominant processes. This will ensure that the parameter set cloning emphasizes the dominant hydrologic process in the region where the candidate is located. The catchment similarity index for each donor - candidate couple is then created as the sum of the weighted distance of the four properties. Finally, parameters are assigned to each candidate from the donor that is "most similar" (i.e. with the shortest weighted distance sum). For validation, we applied the proposed method to catchments where gauge observations are available, and compared simulated streamflows using the parameters cloned by other catchments to the results obtained by calibrating the hydrologic model directly using gauge data. The comparison shows good agreement between the two models for different river basins as we show here. This method has been applied globally to the Hillslope River Routing (HRR) model using gauge observations obtained from the Global Runoff Data Center (GRDC). As next step, more catchment properties can be taken into account to further improve the representation of catchment similarity.

A new methodology based on sensitivity analysis to simplify the recalibration of functional-structural plant models in new conditions.

PubMed

Mathieu, Amélie; Vidal, Tiphaine; Jullien, Alexandra; Wu, QiongLi; Chambon, Camille; Bayol, Benoit; Cournède, Paul-Henry

2018-06-19

Functional-structural plant models (FSPMs) describe explicitly the interactions between plants and their environment at organ to plant scale. However, the high level of description of the structure or model mechanisms makes this type of model very complex and hard to calibrate. A two-step methodology to facilitate the calibration process is proposed here. First, a global sensitivity analysis method was applied to the calibration loss function. It provided first-order and total-order sensitivity indexes that allow parameters to be ranked by importance in order to select the most influential ones. Second, the Akaike information criterion (AIC) was used to quantify the model's quality of fit after calibration with different combinations of selected parameters. The model with the lowest AIC gives the best combination of parameters to select. This methodology was validated by calibrating the model on an independent data set (same cultivar, another year) with the parameters selected in the second step. All the parameters were set to their nominal value; only the most influential ones were re-estimated. Sensitivity analysis applied to the calibration loss function is a relevant method to underline the most significant parameters in the estimation process. For the studied winter oilseed rape model, 11 out of 26 estimated parameters were selected. Then, the model could be recalibrated for a different data set by re-estimating only three parameters selected with the model selection method. Fitting only a small number of parameters dramatically increases the efficiency of recalibration, increases the robustness of the model and helps identify the principal sources of variation in varying environmental conditions. This innovative method still needs to be more widely validated but already gives interesting avenues to improve the calibration of FSPMs.

A Vision-Based Self-Calibration Method for Robotic Visual Inspection Systems

PubMed Central

Yin, Shibin; Ren, Yongjie; Zhu, Jigui; Yang, Shourui; Ye, Shenghua

2013-01-01

A vision-based robot self-calibration method is proposed in this paper to evaluate the kinematic parameter errors of a robot using a visual sensor mounted on its end-effector. This approach could be performed in the industrial field without external, expensive apparatus or an elaborate setup. A robot Tool Center Point (TCP) is defined in the structural model of a line-structured laser sensor, and aligned to a reference point fixed in the robot workspace. A mathematical model is established to formulate the misalignment errors with kinematic parameter errors and TCP position errors. Based on the fixed point constraints, the kinematic parameter errors and TCP position errors are identified with an iterative algorithm. Compared to the conventional methods, this proposed method eliminates the need for a robot-based-frame and hand-to-eye calibrations, shortens the error propagation chain, and makes the calibration process more accurate and convenient. A validation experiment is performed on an ABB IRB2400 robot. An optimal configuration on the number and distribution of fixed points in the robot workspace is obtained based on the experimental results. Comparative experiments reveal that there is a significant improvement of the measuring accuracy of the robotic visual inspection system. PMID:24300597

Development of an innovative validation strategy of gas-surface interaction modelling for re-entry applications

NASA Astrophysics Data System (ADS)

Joiner, N.; Esser, B.; Fertig, M.; Gülhan, A.; Herdrich, G.; Massuti-Ballester, B.

2016-12-01

This paper summarises the final synthesis of an ESA technology research programme entitled "Development of an Innovative Validation Strategy of Gas Surface Interaction Modelling for Re-entry Applications". The focus of the project was to demonstrate the correct pressure dependency of catalytic surface recombination, with an emphasis on Low Earth Orbit (LEO) re-entry conditions and thermal protection system materials. A physics-based model describing the prevalent recombination mechanisms was proposed for implementation into two CFD codes, TINA and TAU. A dedicated experimental campaign was performed to calibrate and validate the CFD model on TPS materials pertinent to the EXPERT space vehicle at a wide range of temperatures and pressures relevant to LEO. A new set of catalytic recombination data was produced that was able to improve the chosen model calibration for CVD-SiC and provide the first model calibration for the Nickel-Chromium super-alloy PM1000. The experimentally observed pressure dependency of catalytic recombination can only be reproduced by the Langmuir-Hinshelwood recombination mechanism. Due to decreasing degrees of (enthalpy and hence) dissociation with facility stagnation pressure, it was not possible to obtain catalytic recombination coefficients from the measurements at high experimental stagnation pressures. Therefore, the CFD model calibration has been improved by this activity based on the low pressure results. The results of the model calibration were applied to the existing EXPERT mission profile to examine the impact of the experimentally calibrated model at flight relevant conditions. The heat flux overshoot at the CVD-SiC/PM1000 junction on EXPERT is confirmed to produce radiative equilibrium temperatures in close proximity to the PM1000 melt temperature.This was anticipated within the margins of the vehicle design; however, due to the measurements made here for the first time at relevant temperatures for the junction, an increased confidence in this finding is placed on the computations.

Understanding controls of hydrologic processes across two headwater monolithological catchments using model-data synthesis

NASA Astrophysics Data System (ADS)

Xiao, D.; Shi, Y.; Hoagland, B.; Del Vecchio, J.; Russo, T. A.; DiBiase, R. A.; Li, L.

2017-12-01

How do watershed hydrologic processes differ in catchments derived from different lithology? This study compares two first order, deciduous forest watersheds in Pennsylvania, a sandstone watershed, Garner Run (GR, 1.34 km2), and a shale-derived watershed, Shale Hills (SH, 0.08 km2). Both watersheds are simulated using a combination of national datasets and field measurements, and a physics-based land surface hydrologic model, Flux-PIHM. We aim to evaluate the effects of lithology on watershed hydrology and assess if we can simulate a new watershed without intensive measurements, i.e., directly use calibration information from one watershed (SH) to reproduce hydrologic dynamics of another watershed (GR). Without any calibration, the model at GR based on national datasets and calibration inforamtion from SH cannot capture some discharge peaks or the baseflow during dry periods. The model prediction agrees well with the GR field discharge and soil moisture after calibrating the soil hydraulic parameters using the uncertainty based Hornberger-Spear-Young algorithm and the Latin Hypercube Sampling method. Agreeing with the field observation and national datasets, the difference in parameter values shows that the sandstone watershed has a larger averaged soil pore diameter, greater water storage created by porosity, lower water retention ability, and greater preferential flow. The water budget calculation shows that the riparian zone and the colluvial valley serves as buffer zones that stores water at GR. Using the same procedure, we compared Flux-PIHM simulations with and without a field measured surface boulder map at GR. When the boulder map is used, the prediction of areal averaged soil moisture is improved, without performing extra calibration. When calibrated separately, the cases with or without boulder map yield different calibration values, but their hydrologic predictions are similar, showing equifinality. The calibrated soil hydraulic parameter values in the with boulder map case is more physically plausible than the without boulder map case. We switched the topography and soil properties between GR and SH, and results indicate that the hydrologic processes are more sensitive to changes in domain topography than to changes in the soil properties.

Nonsequential modeling of laser diode stacks using Zemax: simulation, optimization, and experimental validation.

PubMed

Coluccelli, Nicola

2010-08-01

Modeling a real laser diode stack based on Zemax ray tracing software that operates in a nonsequential mode is reported. The implementation of the model is presented together with the geometric and optical parameters to be adjusted to calibrate the model and to match the simulated intensity irradiance profiles with the experimental profiles. The calibration of the model is based on a near-field and a far-field measurement. The validation of the model has been accomplished by comparing the simulated and experimental transverse irradiance profiles at different positions along the caustic formed by a lens. Spot sizes and waist location are predicted with a maximum error below 6%.

A multi-objective approach to improve SWAT model calibration in alpine catchments

NASA Astrophysics Data System (ADS)

Tuo, Ye; Marcolini, Giorgia; Disse, Markus; Chiogna, Gabriele

2018-04-01

Multi-objective hydrological model calibration can represent a valuable solution to reduce model equifinality and parameter uncertainty. The Soil and Water Assessment Tool (SWAT) model is widely applied to investigate water quality and water management issues in alpine catchments. However, the model calibration is generally based on discharge records only, and most of the previous studies have defined a unique set of snow parameters for an entire basin. Only a few studies have considered snow observations to validate model results or have taken into account the possible variability of snow parameters for different subbasins. This work presents and compares three possible calibration approaches. The first two procedures are single-objective calibration procedures, for which all parameters of the SWAT model were calibrated according to river discharge alone. Procedures I and II differ from each other by the assumption used to define snow parameters: The first approach assigned a unique set of snow parameters to the entire basin, whereas the second approach assigned different subbasin-specific sets of snow parameters to each subbasin. The third procedure is a multi-objective calibration, in which we considered snow water equivalent (SWE) information at two different spatial scales (i.e. subbasin and elevation band), in addition to discharge measurements. We tested these approaches in the Upper Adige river basin where a dense network of snow depth measurement stations is available. Only the set of parameters obtained with this multi-objective procedure provided an acceptable prediction of both river discharge and SWE. These findings offer the large community of SWAT users a strategy to improve SWAT modeling in alpine catchments.

The Iterative Research Cycle: Process-Based Model Evaluation

NASA Astrophysics Data System (ADS)

Vrugt, J. A.

2014-12-01

The ever increasing pace of computational power, along with continued advances in measurement technologies and improvements in process understanding has stimulated the development of increasingly complex physics based models that simulate a myriad of processes at different spatial and temporal scales. Reconciling these high-order system models with perpetually larger volumes of field data is becoming more and more difficult, particularly because classical likelihood-based fitting methods lack the power to detect and pinpoint deficiencies in the model structure. In this talk I will give an overview of our latest research on process-based model calibration and evaluation. This approach, rooted in Bayesian theory, uses summary metrics of the calibration data rather than the data itself to help detect which component(s) of the model is (are) malfunctioning and in need of improvement. A few case studies involving hydrologic and geophysical models will be used to demonstrate the proposed methodology.

Improving regression-model-based streamwater constituent load estimates derived from serially correlated data

USGS Publications Warehouse

Aulenbach, Brent T.

2013-01-01

A regression-model based approach is a commonly used, efficient method for estimating streamwater constituent load when there is a relationship between streamwater constituent concentration and continuous variables such as streamwater discharge, season and time. A subsetting experiment using a 30-year dataset of daily suspended sediment observations from the Mississippi River at Thebes, Illinois, was performed to determine optimal sampling frequency, model calibration period length, and regression model methodology, as well as to determine the effect of serial correlation of model residuals on load estimate precision. Two regression-based methods were used to estimate streamwater loads, the Adjusted Maximum Likelihood Estimator (AMLE), and the composite method, a hybrid load estimation approach. While both methods accurately and precisely estimated loads at the model’s calibration period time scale, precisions were progressively worse at shorter reporting periods, from annually to monthly. Serial correlation in model residuals resulted in observed AMLE precision to be significantly worse than the model calculated standard errors of prediction. The composite method effectively improved upon AMLE loads for shorter reporting periods, but required a sampling interval of at least 15-days or shorter, when the serial correlations in the observed load residuals were greater than 0.15. AMLE precision was better at shorter sampling intervals and when using the shortest model calibration periods, such that the regression models better fit the temporal changes in the concentration–discharge relationship. The models with the largest errors typically had poor high flow sampling coverage resulting in unrepresentative models. Increasing sampling frequency and/or targeted high flow sampling are more efficient approaches to ensure sufficient sampling and to avoid poorly performing models, than increasing calibration period length.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, R; Jee, K; Sharp, G

Purpose: Proton radiography, which images the patients with the same type of particles that they are to be treated with, is a promising approach for image guidance and range uncertainties reduction. This study aimed to realize quality proton radiography by measuring dose rate functions (DRF) in time domain using a single flat panel and retrieve water equivalent path length (WEPL) from them. Methods: An amorphous silicon flat panel (PaxScan™ 4030CB, Varian Medical Systems, Inc., Palo Alto, CA) was placed behind phantoms to measure DRFs from a proton beam modulated by the modulator wheel. To retrieve WEPL and RSP, calibration modelsmore » based on the intensity of DRFs only, root mean square (RMS) of DRFs only and the intensity weighted RMS were tested. The quality of obtained WEPL images (in terms of spatial resolution and level of details) and the accuracy of WEPL were compared. Results: RSPs for most of the Gammex phantom inserts were retrieved within ± 1% errors by calibration models based on the RMS and intensity weighted RMS. The mean percentage error for all inserts was reduced from 1.08% to 0.75% by matching intensity in the calibration model. In specific cases such as the insert with a titanium rod, the calibration model based on RMS only fails while the that based on intensity weighted RMS is still valid. The quality of retrieved WEPL images were significantly improved for calibration models including intensity matching. Conclusion: For the first time, a flat panel, which is readily available in the beamline for image guidance, was tested to acquire quality proton radiography with WEPL accurately retrieved from it. This technique is promising to be applied for image-guided proton therapy as well as patient specific RSP determination to reduce uncertainties of beam ranges.« less

Probabilistic methods for sensitivity analysis and calibration in the NASA challenge problem

DOE PAGES

Safta, Cosmin; Sargsyan, Khachik; Najm, Habib N.; ...

2015-01-01

In this study, a series of algorithms are proposed to address the problems in the NASA Langley Research Center Multidisciplinary Uncertainty Quantification Challenge. A Bayesian approach is employed to characterize and calibrate the epistemic parameters based on the available data, whereas a variance-based global sensitivity analysis is used to rank the epistemic and aleatory model parameters. A nested sampling of the aleatory–epistemic space is proposed to propagate uncertainties from model parameters to output quantities of interest.

Probabilistic methods for sensitivity analysis and calibration in the NASA challenge problem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Safta, Cosmin; Sargsyan, Khachik; Najm, Habib N.

In this study, a series of algorithms are proposed to address the problems in the NASA Langley Research Center Multidisciplinary Uncertainty Quantification Challenge. A Bayesian approach is employed to characterize and calibrate the epistemic parameters based on the available data, whereas a variance-based global sensitivity analysis is used to rank the epistemic and aleatory model parameters. A nested sampling of the aleatory–epistemic space is proposed to propagate uncertainties from model parameters to output quantities of interest.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Menikoff, Ralph

Previously the SURFplus reactive burn model was calibrated for the TATB based explosive PBX 9502. The calibration was based on fitting Pop plot data, the failure diameter and the limiting detonation speed, and curvature effect data for small curvature. The model failure diameter is determined utilizing 2-D simulations of an unconfined rate stick to find the minimum diameter for which a detonation wave propagates. Here we examine the effect of mesh resolution on an unconfined rate stick with a diameter (10mm) slightly greater than the measured failure diameter (8 to 9 mm).

Automatic Calibration of a Distributed Rainfall-Runoff Model, Using the Degree-Day Formulation for Snow Melting, Within DMIP2 Project

NASA Astrophysics Data System (ADS)

Frances, F.; Orozco, I.

2010-12-01

This work presents the assessment of the TETIS distributed hydrological model in mountain basins of the American and Carson rivers in Sierra Nevada (USA) at hourly time discretization, as part of the DMIP2 Project. In TETIS each cell of the spatial grid conceptualizes the water cycle using six tanks connected among them. The relationship between tanks depends on the case, although at the end in most situations, simple linear reservoirs and flow thresholds schemes are used with exceptional results (Vélez et al., 1999; Francés et al., 2002). In particular, within the snow tank, snow melting is based in this work on the simple degree-day method with spatial constant parameters. The TETIS model includes an automatic calibration module, based on the SCE-UA algorithm (Duan et al., 1992; Duan et al., 1994) and the model effective parameters are organized following a split structure, as presented by Francés and Benito (1995) and Francés et al. (2007). In this way, the calibration involves in TETIS up to 9 correction factors (CFs), which correct globally the different parameter maps instead of each parameter cell value, thus reducing drastically the number of variables to be calibrated. This strategy allows for a fast and agile modification in different hydrological processes preserving the spatial structure of each parameter map. With the snowmelt submodel, automatic model calibration was carried out in three steps, separating the calibration of rainfall-runoff and snowmelt parameters. In the first step, the automatic calibration of the CFs during the period 05/20/1990 to 07/31/1990 in the American River (without snow influence), gave a Nash-Sutcliffe Efficiency (NSE) index of 0.92. The calibration of the three degree-day parameters was done using all the SNOTEL stations in the American and Carson rivers. Finally, using previous calibrations as initial values, the complete calibration done in the Carson River for the period 10/01/1992 to 07/31/1993 gave a NSE index of 0.86. The temporal and spatial validation using five periods must be considered in both rivers excellent for discharges (NSEs higher than 0.76) and good for snow distribution (daily spatial coverage errors ranging from -10 to 27%). In conclusion, this work demonstrates: 1.- The viability of automatic calibration of distributed models, with the corresponding personal time saving and maximum exploitation of the available information. 2.- The good performance of the degree-day snowmelt formulation even at hourly time discretization, in spite of its simplicity.

Parameter estimation and sensitivity analysis in an agent-based model of Leishmania major infection

PubMed Central

Jones, Douglas E.; Dorman, Karin S.

2009-01-01

Computer models of disease take a systems biology approach toward understanding host-pathogen interactions. In particular, data driven computer model calibration is the basis for inference of immunological and pathogen parameters, assessment of model validity, and comparison between alternative models of immune or pathogen behavior. In this paper we describe the calibration and analysis of an agent-based model of Leishmania major infection. A model of macrophage loss following uptake of necrotic tissue is proposed to explain macrophage depletion following peak infection. Using Gaussian processes to approximate the computer code, we perform a sensitivity analysis to identify important parameters and to characterize their influence on the simulated infection. The analysis indicates that increasing growth rate can favor or suppress pathogen loads, depending on the infection stage and the pathogen’s ability to avoid detection. Subsequent calibration of the model against previously published biological observations suggests that L. major has a relatively slow growth rate and can replicate for an extended period of time before damaging the host cell. PMID:19837088

Geomorphically based predictive mapping of soil thickness in upland watersheds

NASA Astrophysics Data System (ADS)

Pelletier, Jon D.; Rasmussen, Craig

2009-09-01

The hydrologic response of upland watersheds is strongly controlled by soil (regolith) thickness. Despite the need to quantify soil thickness for input into hydrologic models, there is currently no widely used, geomorphically based method for doing so. In this paper we describe and illustrate a new method for predictive mapping of soil thicknesses using high-resolution topographic data, numerical modeling, and field-based calibration. The model framework works directly with input digital elevation model data to predict soil thicknesses assuming a long-term balance between soil production and erosion. Erosion rates in the model are quantified using one of three geomorphically based sediment transport models: nonlinear slope-dependent transport, nonlinear area- and slope-dependent transport, and nonlinear depth- and slope-dependent transport. The model balances soil production and erosion locally to predict a family of solutions corresponding to a range of values of two unconstrained model parameters. A small number of field-based soil thickness measurements can then be used to calibrate the local value of those unconstrained parameters, thereby constraining which solution is applicable at a particular study site. As an illustration, the model is used to predictively map soil thicknesses in two small, ˜0.1 km2, drainage basins in the Marshall Gulch watershed, a semiarid drainage basin in the Santa Catalina Mountains of Pima County, Arizona. Field observations and calibration data indicate that the nonlinear depth- and slope-dependent sediment transport model is the most appropriate transport model for this site. The resulting framework provides a generally applicable, geomorphically based tool for predictive mapping of soil thickness using high-resolution topographic data sets.

Modeling marine oily wastewater treatment by a probabilistic agent-based approach.

PubMed

Jing, Liang; Chen, Bing; Zhang, Baiyu; Ye, Xudong

2018-02-01

This study developed a novel probabilistic agent-based approach for modeling of marine oily wastewater treatment processes. It begins first by constructing a probability-based agent simulation model, followed by a global sensitivity analysis and a genetic algorithm-based calibration. The proposed modeling approach was tested through a case study of the removal of naphthalene from marine oily wastewater using UV irradiation. The removal of naphthalene was described by an agent-based simulation model using 8 types of agents and 11 reactions. Each reaction was governed by a probability parameter to determine its occurrence. The modeling results showed that the root mean square errors between modeled and observed removal rates were 8.73 and 11.03% for calibration and validation runs, respectively. Reaction competition was analyzed by comparing agent-based reaction probabilities, while agents' heterogeneity was visualized by plotting their real-time spatial distribution, showing a strong potential for reactor design and process optimization. Copyright © 2017 Elsevier Ltd. All rights reserved.

Modeling study of a proposed field calibration source using K-40 and high-Z targets for sodium iodide detectors

DOE PAGES

Rogers, Jeremy; Marianno, Craig; Kallenbach, Gene; ...

2016-06-01

Calibration sources based on the primordial isotope potassium-40 ( 40K) have reduced controls on the source’s activity due to its terrestrial ubiquity and very low specific activity. Potassium–40’s beta emissions and 1,460.8 keV gamma ray can be used to induce K-shell fluorescence x rays in high-Z metals between 60 and 80 keV. A gamma ray calibration source that uses potassium chloride salt and a high-Z metal to create a two-point calibration for a sodium iodide field gamma spectroscopy instrument is thus proposed. The calibration source was designed in collaboration with the Sandia National Laboratory using the Monte Carlo N-Particle eXtendedmore » (MCNPX) transport code. Two methods of x-ray production were explored. First, a thin high-Z layer (HZL) was interposed between the detector and the potassium chloride-urethane source matrix. Second, bismuth metal powder was homogeneously mixed with a urethane binding agent to form a potassium chloride-bismuth matrix (KBM). The bismuth-based source was selected as the development model because it is inexpensive, nontoxic, and outperforms the high-Z layer method in simulation. As a result, based on the MCNPX studies, sealing a mixture of bismuth powder and potassium chloride into a thin plastic case could provide a light, inexpensive field calibration source.« less

On the importance of geological data for hydraulic tomography analysis: Laboratory sandbox study

NASA Astrophysics Data System (ADS)

Zhao, Zhanfeng; Illman, Walter A.; Berg, Steven J.

2016-11-01

This paper investigates the importance of geological data in Hydraulic Tomography (HT) through sandbox experiments. In particular, four groundwater models with homogeneous geological units constructed with borehole data of varying accuracy are jointly calibrated with multiple pumping test data of two different pumping and observation densities. The results are compared to those from a geostatistical inverse model. Model calibration and validation performances are quantitatively assessed using drawdown scatterplots. We find that accurate and inaccurate geological models can be well calibrated, despite the estimated K values for the poor geological models being quite different from the actual values. Model validation results reveal that inaccurate geological models yield poor drawdown predictions, but using more calibration data improves its predictive capability. Moreover, model comparisons among a highly parameterized geostatistical and layer-based geological models show that, (1) as the number of pumping tests and monitoring locations are reduced, the performance gap between the approaches decreases, and (2) a simplified geological model with a fewer number of layers is more reliable than the one based on the wrong description of stratigraphy. Finally, using a geological model as prior information in geostatistical inverse models results in the preservation of geological features, especially in areas where drawdown data are not available. Overall, our sandbox results emphasize the importance of incorporating geological data in HT surveys when data from pumping tests is sparse. These findings have important implications for field applications of HT where well distances are large.

An efficient surrogate-based simulation-optimization method for calibrating a regional MODFLOW model

NASA Astrophysics Data System (ADS)

Chen, Mingjie; Izady, Azizallah; Abdalla, Osman A.

2017-01-01

Simulation-optimization method entails a large number of model simulations, which is computationally intensive or even prohibitive if the model simulation is extremely time-consuming. Statistical models have been examined as a surrogate of the high-fidelity physical model during simulation-optimization process to tackle this problem. Among them, Multivariate Adaptive Regression Splines (MARS), a non-parametric adaptive regression method, is superior in overcoming problems of high-dimensions and discontinuities of the data. Furthermore, the stability and accuracy of MARS model can be improved by bootstrap aggregating methods, namely, bagging. In this paper, Bagging MARS (BMARS) method is integrated to a surrogate-based simulation-optimization framework to calibrate a three-dimensional MODFLOW model, which is developed to simulate the groundwater flow in an arid hardrock-alluvium region in northwestern Oman. The physical MODFLOW model is surrogated by the statistical model developed using BMARS algorithm. The surrogate model, which is fitted and validated using training dataset generated by the physical model, can approximate solutions rapidly. An efficient Sobol' method is employed to calculate global sensitivities of head outputs to input parameters, which are used to analyze their importance for the model outputs spatiotemporally. Only sensitive parameters are included in the calibration process to further improve the computational efficiency. Normalized root mean square error (NRMSE) between measured and simulated heads at observation wells is used as the objective function to be minimized during optimization. The reasonable history match between the simulated and observed heads demonstrated feasibility of this high-efficient calibration framework.

Radiometric modeling and calibration of the Geostationary Imaging Fourier Transform Spectrometer (GIFTS) ground based measurement experiment

NASA Astrophysics Data System (ADS)

Tian, Jialin; Smith, William L.; Gazarik, Michael J.

2008-12-01

The ultimate remote sensing benefits of the high resolution Infrared radiance spectrometers will be realized with their geostationary satellite implementation in the form of imaging spectrometers. This will enable dynamic features of the atmosphere's thermodynamic fields and pollutant and greenhouse gas constituents to be observed for revolutionary improvements in weather forecasts and more accurate air quality and climate predictions. As an important step toward realizing this application objective, the Geostationary Imaging Fourier Transform Spectrometer (GIFTS) Engineering Demonstration Unit (EDU) was successfully developed under the NASA New Millennium Program, 2000-2006. The GIFTS-EDU instrument employs three focal plane arrays (FPAs), which gather measurements across the long-wave IR (LWIR), short/mid-wave IR (SMWIR), and visible spectral bands. The GIFTS calibration is achieved using internal blackbody calibration references at ambient (260 K) and hot (286 K) temperatures. In this paper, we introduce a refined calibration technique that utilizes Principle Component (PC) analysis to compensate for instrument distortions and artifacts, therefore, enhancing the absolute calibration accuracy. This method is applied to data collected during the GIFTS Ground Based Measurement (GBM) experiment, together with simultaneous observations by the accurately calibrated AERI (Atmospheric Emitted Radiance Interferometer), both simultaneously zenith viewing the sky through the same external scene mirror at ten-minute intervals throughout a cloudless day at Logan Utah on September 13, 2006. The accurately calibrated GIFTS radiances are produced using the first four PC scores in the GIFTS-AERI regression model. Temperature and moisture profiles retrieved from the PC-calibrated GIFTS radiances are verified against radiosonde measurements collected throughout the GIFTS sky measurement period. Using the GIFTS GBM calibration model, we compute the calibrated radiances from data collected during the moon tracking and viewing experiment events. From which, we derive the lunar surface temperature and emissivity associated with the moon viewing measurements.

Radiometric Modeling and Calibration of the Geostationary Imaging Fourier Transform Spectrometer (GIFTS)Ground Based Measurement Experiment

NASA Technical Reports Server (NTRS)

Tian, Jialin; Smith, William L.; Gazarik, Michael J.

2008-01-01

The ultimate remote sensing benefits of the high resolution Infrared radiance spectrometers will be realized with their geostationary satellite implementation in the form of imaging spectrometers. This will enable dynamic features of the atmosphere s thermodynamic fields and pollutant and greenhouse gas constituents to be observed for revolutionary improvements in weather forecasts and more accurate air quality and climate predictions. As an important step toward realizing this application objective, the Geostationary Imaging Fourier Transform Spectrometer (GIFTS) Engineering Demonstration Unit (EDU) was successfully developed under the NASA New Millennium Program, 2000-2006. The GIFTS-EDU instrument employs three focal plane arrays (FPAs), which gather measurements across the long-wave IR (LWIR), short/mid-wave IR (SMWIR), and visible spectral bands. The GIFTS calibration is achieved using internal blackbody calibration references at ambient (260 K) and hot (286 K) temperatures. In this paper, we introduce a refined calibration technique that utilizes Principle Component (PC) analysis to compensate for instrument distortions and artifacts, therefore, enhancing the absolute calibration accuracy. This method is applied to data collected during the GIFTS Ground Based Measurement (GBM) experiment, together with simultaneous observations by the accurately calibrated AERI (Atmospheric Emitted Radiance Interferometer), both simultaneously zenith viewing the sky through the same external scene mirror at ten-minute intervals throughout a cloudless day at Logan Utah on September 13, 2006. The accurately calibrated GIFTS radiances are produced using the first four PC scores in the GIFTS-AERI regression model. Temperature and moisture profiles retrieved from the PC-calibrated GIFTS radiances are verified against radiosonde measurements collected throughout the GIFTS sky measurement period. Using the GIFTS GBM calibration model, we compute the calibrated radiances from data collected during the moon tracking and viewing experiment events. From which, we derive the lunar surface temperature and emissivity associated with the moon viewing measurements.

Nondestructive prediction of the drug content of an aspirin suppository by near-infrared spectroscopy.

PubMed

Otsuka, Eri; Abe, Hiroyuki; Aburada, Masaki; Otsuka, Makoto

2010-07-01

A suppository dosage form has a rapid effect on therapeutics, because it dissolves in the rectum, is absorbed in the bloodstream, and passes the hepatic metabolism. However, the dosage form is unstable, because a suppository is made in a semisolid form, and so it is not easy to mix the bulk drug powder in the base. This article describes a nondestructive method of determining the drug content of suppositories using near-infrared spectrometry (NIR) combined with chemometrics. Suppositories (aspirin content: 1.8, 2.7, 4.5, 7.3, and 9.1%, w/w) were produced by mixing an aspirin bulk powder with hard fat at 50 degrees C and pouring the melt mixture into a plastic mold (2.25 mL). NIR spectra of 12 calibration and 12 validation sample sets were recorded 5 times. A total of 60 spectral data were used as a calibration set to establish a calibration model to predict drug content with a partial least-squares (PLS) regression analysis. NIR data of the suppository samples were divided into two wave number ranges, 4000-12500 cm(-1) (LR), and 5900-6300 cm(-1) (SR). Calibration models for the aspirin content of the suppositories were calculated based on LR and SR ranges of second-derivative NIR spectra using PLS. The models for LR and SR consisted of five and one principal components (PC), respectively. The plots of predicted values against actual values gave a straight line with regression coefficient constants of 0.9531 and 0.9749, respectively. The mean bias and mean accuracy of the calibration models were calculated based on the SR of variation data sets, and were lower than those of LR, respectively. Limiting the wave number of spectral data sets is useful to help understand the calibration model because of noise cancellation and to measure objective functions.

The Wally plot approach to assess the calibration of clinical prediction models.

PubMed

Blanche, Paul; Gerds, Thomas A; Ekstrøm, Claus T

2017-12-06

A prediction model is calibrated if, roughly, for any percentage x we can expect that x subjects out of 100 experience the event among all subjects that have a predicted risk of x%. Typically, the calibration assumption is assessed graphically but in practice it is often challenging to judge whether a "disappointing" calibration plot is the consequence of a departure from the calibration assumption, or alternatively just "bad luck" due to sampling variability. We propose a graphical approach which enables the visualization of how much a calibration plot agrees with the calibration assumption to address this issue. The approach is mainly based on the idea of generating new plots which mimic the available data under the calibration assumption. The method handles the common non-trivial situations in which the data contain censored observations and occurrences of competing events. This is done by building on ideas from constrained non-parametric maximum likelihood estimation methods. Two examples from large cohort data illustrate our proposal. The 'wally' R package is provided to make the methodology easily usable.

Linear regression analysis and its application to multivariate chromatographic calibration for the quantitative analysis of two-component mixtures.

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

Multivariate chromatographic calibration technique was developed for the quantitative analysis of binary mixtures enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets in the presence of losartan potassium (LST). The mathematical algorithm of multivariate chromatographic calibration technique is based on the use of the linear regression equations constructed using relationship between concentration and peak area at the five-wavelength set. The algorithm of this mathematical calibration model having a simple mathematical content was briefly described. This approach is a powerful mathematical tool for an optimum chromatographic multivariate calibration and elimination of fluctuations coming from instrumental and experimental conditions. This multivariate chromatographic calibration contains reduction of multivariate linear regression functions to univariate data set. The validation of model was carried out by analyzing various synthetic binary mixtures and using the standard addition technique. Developed calibration technique was applied to the analysis of the real pharmaceutical tablets containing EA and HCT. The obtained results were compared with those obtained by classical HPLC method. It was observed that the proposed multivariate chromatographic calibration gives better results than classical HPLC.

Accuracy evaluation of optical distortion calibration by digital image correlation

NASA Astrophysics Data System (ADS)

Gao, Zeren; Zhang, Qingchuan; Su, Yong; Wu, Shangquan

2017-11-01

Due to its convenience of operation, the camera calibration algorithm, which is based on the plane template, is widely used in image measurement, computer vision and other fields. How to select a suitable distortion model is always a problem to be solved. Therefore, there is an urgent need for an experimental evaluation of the accuracy of camera distortion calibrations. This paper presents an experimental method for evaluating camera distortion calibration accuracy, which is easy to implement, has high precision, and is suitable for a variety of commonly used lens. First, we use the digital image correlation method to calculate the in-plane rigid body displacement field of an image displayed on a liquid crystal display before and after translation, as captured with a camera. Next, we use a calibration board to calibrate the camera to obtain calibration parameters which are used to correct calculation points of the image before and after deformation. The displacement field before and after correction is compared to analyze the distortion calibration results. Experiments were carried out to evaluate the performance of two commonly used industrial camera lenses for four commonly used distortion models.

Semi-automated in vivo solid-phase microextraction sampling and the diffusion-based interface calibration model to determine the pharmacokinetics of methoxyfenoterol and fenoterol in rats.

PubMed

Yeung, Joanne Chung Yan; de Lannoy, Inés; Gien, Brad; Vuckovic, Dajana; Yang, Yingbo; Bojko, Barbara; Pawliszyn, Janusz

2012-09-12

In vivo solid-phase microextraction (SPME) can be used to sample the circulating blood of animals without the need to withdraw a representative blood sample. In this study, in vivo SPME in combination with liquid-chromatography tandem mass spectrometry (LC-MS/MS) was used to determine the pharmacokinetics of two drug analytes, R,R-fenoterol and R,R-methoxyfenoterol, administered as 5 mg kg(-1) i.v. bolus doses to groups of 5 rats. This research illustrates, for the first time, the feasibility of the diffusion-based calibration interface model for in vivo SPME studies. To provide a constant sampling rate as required for the diffusion-based interface model, partial automation of the SPME sampling of the analytes from the circulating blood was accomplished using an automated blood sampling system. The use of the blood sampling system allowed automation of all SPME sampling steps in vivo, except for the insertion and removal of the SPME probe from the sampling interface. The results from in vivo SPME were compared to the conventional method based on blood withdrawal and sample clean up by plasma protein precipitation. Both whole blood and plasma concentrations were determined by the conventional method. The concentrations of methoxyfenoterol and fenoterol obtained by SPME generally concur with the whole blood concentrations determined by the conventional method indicating the utility of the proposed method. The proposed diffusion-based interface model has several advantages over other kinetic calibration models for in vivo SPME sampling including (i) it does not require the addition of a standard into the sample matrix during in vivo studies, (ii) it is simple and rapid and eliminates the need to pre-load appropriate standard onto the SPME extraction phase and (iii) the calibration constant for SPME can be calculated based on the diffusion coefficient, extraction time, fiber length and radius, and size of the boundary layer. In the current study, the experimental calibration constants of 338.9±30 mm(-3) and 298.5±25 mm(-3) are in excellent agreement with the theoretical calibration constants of 307.9 mm(-3) and 316.0 mm(-3) for fenoterol and methoxyfenoterol respectively. Copyright © 2012 Elsevier B.V. All rights reserved.

Dynamic calibration of agent-based models using data assimilation.

PubMed

Ward, Jonathan A; Evans, Andrew J; Malleson, Nicolas S

2016-04-01

A widespread approach to investigating the dynamical behaviour of complex social systems is via agent-based models (ABMs). In this paper, we describe how such models can be dynamically calibrated using the ensemble Kalman filter (EnKF), a standard method of data assimilation. Our goal is twofold. First, we want to present the EnKF in a simple setting for the benefit of ABM practitioners who are unfamiliar with it. Second, we want to illustrate to data assimilation experts the value of using such methods in the context of ABMs of complex social systems and the new challenges these types of model present. We work towards these goals within the context of a simple question of practical value: how many people are there in Leeds (or any other major city) right now? We build a hierarchy of exemplar models that we use to demonstrate how to apply the EnKF and calibrate these using open data of footfall counts in Leeds.

Spectral Irradiance Calibration in the Infrared. 7. 5-14 microns Spectroscopy of the Asteroids Ceres, Vesta, and Pallas

NASA Technical Reports Server (NTRS)

Cohen, Martin; Witteborn, Fred C.; Roush, Ted; Bregman, Jesse; Wooden, Diane

1996-01-01

We describe our efforts to seek "closure" in our infrared absolute calibration scheme by comparing spectra of asteroids, absolutely calibrated through reference stars, with "Standard Thermal Models" and "Thermophysical Models" for these bodies. Our use of continuous 5-14 microns airborne spectra provides complete sampling of the rise to, and peak, of the infrared spectral energy distribution and constrains these models. Such models currently support the absolute calibration of ISO-PHOT at far-infrared wave- lengths (as far as 300 microns), and contribute to that of the Mid-Infrared Spectrometer on the "Infrared Telescope in Space" in the 6-12 microns region. The best match to our observed spectra of Ceres and Vesta is a, standard thermal model using a beaming factor of unity. We also report the presence of three emissivity features in Ceres which may complicate the traditional model extrapolation to the far-infrared from contemporaneous ground-based N-band photometry that is used to support calibration of, for example, ISO-PHOT. While identification of specific materials that cause these features is not made, we discuss families of minerals that may be responsible.

Photometric Data from Non-Resolved Objects for Space Object Characterization and Improved Atmospheric Modeling

NASA Astrophysics Data System (ADS)

Linares, R.; Palmer, D.; Thompson, D.; Koller, J.

2013-09-01

Recent events in space, including the collision of Russia's Cosmos 2251 satellite with Iridium 33 and China's Feng Yun 1C anti-satellite demonstration, have stressed the capabilities of Space Surveillance Network (SSN) and its ability to provide accurate and actionable impact probability estimates. The SSN network has the unique challenge of tracking more than 18,000 resident space objects (RSOs) and providing critical collision avoidance warnings to military, NASA, and commercial systems. However, due to the large number of RSOs and the limited number of sensors available to track them, it is impossible to maintain persistent surveillance. Observation gaps result in large propagation intervals between measurements and close approaches. Coupled with nonlinear RSO dynamics this results in difficulty in modeling the probability distribution functions (pdfs) of the RSO. In particular low-Earth orbiting (LEO) satellites are heavily influenced by atmospheric drag, which is very difficult to model accurately. A number of atmospheric models exist which can be classified as either empirical or physics-based models. The current Air Force standard is the High Accuracy Satellite Drag Model (HASDM), which is an empirical model based on observation of calibration satellites. These satellite observations are used to determine model parameters based on their orbit determination solutions. Atmospheric orbits are perturbed by a number of factors including drag coefficient, attitude, and shape of the space object. The satellites used for the HASDM model calibration process are chosen because of their relatively simple shapes, to minimize errors introduced due to shape miss-modeling. Under this requirement the number of calibration satellites that can be used for calibrating the atmospheric models is limited. Los Alamos National Laboratory (LANL) has established a research effort, called IMPACT (Integrated Modeling of Perturbations in Atmospheres for Conjunction Tracking), to improve impact assessment via improved physics-based modeling. As part of this effort calibration satellite observations are used to dynamically calibrate the physics-based model and to improve its forecasting capability. The observations are collected from a variety of sources, including from LANL's own Raven-class optical telescope. This system collects both astrometric and photometric data on space objects. The photometric data will be used to estimate the space objects' attitude and shape. Non-resolved photometric data have been studied by many as a mechanism for space object characterization. Photometry is the measurement of an object's flux or apparent brightness measured over a wavelength band. The temporal variation of photometric measurements is referred to as photometric signature. The photometric optical signature of an object contains information about shape, attitude, size and material composition. This work focuses on the processing of the data collected with LANL's telescope in an effort to use photometric data to expand the number of space objects that can be used as calibration satellites. An Unscented Kalman filter is used to estimate the attitude and angular velocity of the space object; both real data and simulated data scenarios are shown. A number of inactive space objects are used for the real data examples and good estimation results are shown.

Automatic calibration system for analog instruments based on DSP and CCD sensor

NASA Astrophysics Data System (ADS)

Lan, Jinhui; Wei, Xiangqin; Bai, Zhenlong

2008-12-01

Currently, the calibration work of analog measurement instruments is mainly completed by manual and there are many problems waiting for being solved. In this paper, an automatic calibration system (ACS) based on Digital Signal Processor (DSP) and Charge Coupled Device (CCD) sensor is developed and a real-time calibration algorithm is presented. In the ACS, TI DM643 DSP processes the data received by CCD sensor and the outcome is displayed on Liquid Crystal Display (LCD) screen. For the algorithm, pointer region is firstly extracted for improving calibration speed. And then a math model of the pointer is built to thin the pointer and determine the instrument's reading. Through numbers of experiments, the time of once reading is no more than 20 milliseconds while it needs several seconds if it is done manually. At the same time, the error of the instrument's reading satisfies the request of the instruments. It is proven that the automatic calibration system can effectively accomplish the calibration work of the analog measurement instruments.

Numerical simulation of groundwater flow in Dar es Salaam Coastal Plain (Tanzania)

NASA Astrophysics Data System (ADS)

Luciani, Giulia; Sappa, Giuseppe; Cella, Antonella

2016-04-01

They are presented the results of a groundwater modeling study on the Coastal Aquifer of Dar es Salaam (Tanzania). Dar es Salaam is one of the fastest-growing coastal cities in Sub-Saharan Africa, with with more than 4 million of inhabitants and a population growth rate of about 8 per cent per year. The city faces periodic water shortages, due to the lack of an adequate water supply network. These two factors have determined, in the last ten years, an increasing demand of groundwater exploitation, carried on by quite a number of private wells, which have been drilled to satisfy human demand. A steady-state three dimensional groundwater model has been set up by the MODFLOW code, and calibrated with the UCODE code for inverse modeling. The aim of the model was to carry out a characterization of groundwater flow system in the Dar es Salaam Coastal Plain. The inputs applied to the model included net recharge rate, calculated from time series of precipitation data (1961-2012), estimations of average groundwater extraction, and estimations of groundwater recharge, coming from zones, outside the area under study. Parametrization of the hydraulic conductivities was realized referring to the main geological features of the study area, based on available literature data and information. Boundary conditions were assigned based on hydrogeological boundaries. The conceptual model was defined in subsequent steps, which added some hydrogeological features and excluded other ones. Calibration was performed with UCODE 2014, using 76 measures of hydraulic head, taken in 2012 referred to the same season. Data were weighted on the basis of the expected errors. Sensitivity analysis of data was performed during calibration, and permitted to identify which parameters were possible to be estimated, and which data could support parameters estimation. Calibration was evaluated based on statistical index, maps of error distribution and test of independence of residuals. Further model analysis was performed after calibration, to test model performance under a range of variations of input variables.

Addressing subjective decision-making inherent in GLUE-based multi-criteria rainfall-runoff model calibration

NASA Astrophysics Data System (ADS)

Shafii, Mahyar; Tolson, Bryan; Shawn Matott, L.

2015-04-01

GLUE is one of the most commonly used informal methodologies for uncertainty estimation in hydrological modelling. Despite the ease-of-use of GLUE, it involves a number of subjective decisions such as the strategy for identifying the behavioural solutions. This study evaluates the impact of behavioural solution identification strategies in GLUE on the quality of model output uncertainty. Moreover, two new strategies are developed to objectively identify behavioural solutions. The first strategy considers Pareto-based ranking of parameter sets, while the second one is based on ranking the parameter sets based on an aggregated criterion. The proposed strategies, as well as the traditional strategies in the literature, are evaluated with respect to reliability (coverage of observations by the envelope of model outcomes) and sharpness (width of the envelope of model outcomes) in different numerical experiments. These experiments include multi-criteria calibration and uncertainty estimation of three rainfall-runoff models with different number of parameters. To demonstrate the importance of behavioural solution identification strategy more appropriately, GLUE is also compared with two other informal multi-criteria calibration and uncertainty estimation methods (Pareto optimization and DDS-AU). The results show that the model output uncertainty varies with the behavioural solution identification strategy, and furthermore, a robust GLUE implementation would require considering multiple behavioural solution identification strategies and choosing the one that generates the desired balance between sharpness and reliability. The proposed objective strategies prove to be the best options in most of the case studies investigated in this research. Implementing such an approach for a high-dimensional calibration problem enables GLUE to generate robust results in comparison with Pareto optimization and DDS-AU.

A parallel calibration utility for WRF-Hydro on high performance computers

NASA Astrophysics Data System (ADS)

Wang, J.; Wang, C.; Kotamarthi, V. R.

2017-12-01

A successful modeling of complex hydrological processes comprises establishing an integrated hydrological model which simulates the hydrological processes in each water regime, calibrates and validates the model performance based on observation data, and estimates the uncertainties from different sources especially those associated with parameters. Such a model system requires large computing resources and often have to be run on High Performance Computers (HPC). The recently developed WRF-Hydro modeling system provides a significant advancement in the capability to simulate regional water cycles more completely. The WRF-Hydro model has a large range of parameters such as those in the input table files — GENPARM.TBL, SOILPARM.TBL and CHANPARM.TBL — and several distributed scaling factors such as OVROUGHRTFAC. These parameters affect the behavior and outputs of the model and thus may need to be calibrated against the observations in order to obtain a good modeling performance. Having a parameter calibration tool specifically for automate calibration and uncertainty estimates of WRF-Hydro model can provide significant convenience for the modeling community. In this study, we developed a customized tool using the parallel version of the model-independent parameter estimation and uncertainty analysis tool, PEST, to enabled it to run on HPC with PBS and SLURM workload manager and job scheduler. We also developed a series of PEST input file templates that are specifically for WRF-Hydro model calibration and uncertainty analysis. Here we will present a flood case study occurred in April 2013 over Midwest. The sensitivity and uncertainties are analyzed using the customized PEST tool we developed.

Strategy for design NIR calibration sets based on process spectrum and model space: An innovative approach for process analytical technology.

PubMed

Cárdenas, V; Cordobés, M; Blanco, M; Alcalà, M

2015-10-10

The pharmaceutical industry is under stringent regulations on quality control of their products because is critical for both, productive process and consumer safety. According to the framework of "process analytical technology" (PAT), a complete understanding of the process and a stepwise monitoring of manufacturing are required. Near infrared spectroscopy (NIRS) combined with chemometrics have lately performed efficient, useful and robust for pharmaceutical analysis. One crucial step in developing effective NIRS-based methodologies is selecting an appropriate calibration set to construct models affording accurate predictions. In this work, we developed calibration models for a pharmaceutical formulation during its three manufacturing stages: blending, compaction and coating. A novel methodology is proposed for selecting the calibration set -"process spectrum"-, into which physical changes in the samples at each stage are algebraically incorporated. Also, we established a "model space" defined by Hotelling's T(2) and Q-residuals statistics for outlier identification - inside/outside the defined space - in order to select objectively the factors to be used in calibration set construction. The results obtained confirm the efficacy of the proposed methodology for stepwise pharmaceutical quality control, and the relevance of the study as a guideline for the implementation of this easy and fast methodology in the pharma industry. Copyright © 2015 Elsevier B.V. All rights reserved.

Study on rapid valid acidity evaluation of apple by fiber optic diffuse reflectance technique

NASA Astrophysics Data System (ADS)

Liu, Yande; Ying, Yibin; Fu, Xiaping; Jiang, Xuesong

2004-03-01

Some issues related to nondestructive evaluation of valid acidity in intact apples by means of Fourier transform near infrared (FTNIR) (800-2631nm) method were addressed. A relationship was established between the diffuse reflectance spectra recorded with a bifurcated optic fiber and the valid acidity. The data were analyzed by multivariate calibration analysis such as partial least squares (PLS) analysis and principal component regression (PCR) technique. A total of 120 Fuji apples were tested and 80 of them were used to form a calibration data set. The influence of data preprocessing and different spectra treatments were also investigated. Models based on smoothing spectra were slightly worse than models based on derivative spectra and the best result was obtained when the segment length was 5 and the gap size was 10. Depending on data preprocessing and multivariate calibration technique, the best prediction model had a correlation efficient (0.871), a low RMSEP (0.0677), a low RMSEC (0.056) and a small difference between RMSEP and RMSEC by PLS analysis. The results point out the feasibility of FTNIR spectral analysis to predict the fruit valid acidity non-destructively. The ratio of data standard deviation to the root mean square error of prediction (SDR) is better to be less than 3 in calibration models, however, the results cannot meet the demand of actual application. Therefore, further study is required for better calibration and prediction.

New PARSEC data base of α-enhanced stellar evolutionary tracks and isochrones - I. Calibration with 47 Tuc (NGC 104) and the improvement on RGB bump

NASA Astrophysics Data System (ADS)

Fu, Xiaoting; Bressan, Alessandro; Marigo, Paola; Girardi, Léo; Montalbán, Josefina; Chen, Yang; Nanni, Ambra

2018-05-01

Precise studies on the Galactic bulge, globular cluster, Galactic halo, and Galactic thick disc require stellar models with α enhancement and various values of helium content. These models are also important for extra-Galactic population synthesis studies. For this purpose, we complement the existing PARSEC models, which are based on the solar partition of heavy elements, with α-enhanced partitions. We collect detailed measurements on the metal mixture and helium abundance for the two populations of 47 Tuc (NGC 104) from the literature, and calculate stellar tracks and isochrones with these α-enhanced compositions. By fitting the precise colour-magnitude diagram with HST ACS/WFC data, from low main sequence till horizontal branch (HB), we calibrate some free parameters that are important for the evolution of low mass stars like the mixing at the bottom of the convective envelope. This new calibration significantly improves the prediction of the red giant branch bump (RGBB) brightness. Comparison with the observed RGB and HB luminosity functions also shows that the evolutionary lifetimes are correctly predicted. As a further result of this calibration process, we derive the age, distance modulus, reddening, and the RGB mass-loss for 47 Tuc. We apply the new calibration and α-enhanced mixtures of the two 47 Tuc populations ([α/Fe] ˜ 0.4 and 0.2) to other metallicities. The new models reproduce the RGB bump observations much better than previous models. This new PARSEC data base, with the newly updated α-enhanced stellar evolutionary tracks and isochrones, will also be a part of the new stellar products for Gaia.

Studies in Software Cost Model Behavior: Do We Really Understand Cost Model Performance?

NASA Technical Reports Server (NTRS)

Lum, Karen; Hihn, Jairus; Menzies, Tim

2006-01-01

While there exists extensive literature on software cost estimation techniques, industry practice continues to rely upon standard regression-based algorithms. These software effort models are typically calibrated or tuned to local conditions using local data. This paper cautions that current approaches to model calibration often produce sub-optimal models because of the large variance problem inherent in cost data and by including far more effort multipliers than the data supports. Building optimal models requires that a wider range of models be considered while correctly calibrating these models requires rejection rules that prune variables and records and use multiple criteria for evaluating model performance. The main contribution of this paper is to document a standard method that integrates formal model identification, estimation, and validation. It also documents what we call the large variance problem that is a leading cause of cost model brittleness or instability.

Prospects of second generation artificial intelligence tools in calibration of chemical sensors.

PubMed

Braibanti, Antonio; Rao, Rupenaguntla Sambasiva; Ramam, Veluri Anantha; Rao, Gollapalli Nageswara; Rao, Vaddadi Venkata Panakala

2005-05-01

Multivariate data driven calibration models with neural networks (NNs) are developed for binary (Cu++ and Ca++) and quaternary (K+, Ca++, NO3- and Cl-) ion-selective electrode (ISE) data. The response profiles of ISEs with concentrations are non-linear and sub-Nernstian. This task represents function approximation of multi-variate, multi-response, correlated, non-linear data with unknown noise structure i.e. multi-component calibration/prediction in chemometric parlance. Radial distribution function (RBF) and Fuzzy-ARTMAP-NN models implemented in the software packages, TRAJAN and Professional II, are employed for the calibration. The optimum NN models reported are based on residuals in concentration space. Being a data driven information technology, NN does not require a model, prior- or posterior- distribution of data or noise structure. Missing information, spikes or newer trends in different concentration ranges can be modeled through novelty detection. Two simulated data sets generated from mathematical functions are modeled as a function of number of data points and network parameters like number of neurons and nearest neighbors. The success of RBF and Fuzzy-ARTMAP-NNs to develop adequate calibration models for experimental data and function approximation models for more complex simulated data sets ensures AI2 (artificial intelligence, 2nd generation) as a promising technology in quantitation.

Improving near-infrared prediction model robustness with support vector machine regression: a pharmaceutical tablet assay example.

PubMed

Igne, Benoît; Drennen, James K; Anderson, Carl A

2014-01-01

Changes in raw materials and process wear and tear can have significant effects on the prediction error of near-infrared calibration models. When the variability that is present during routine manufacturing is not included in the calibration, test, and validation sets, the long-term performance and robustness of the model will be limited. Nonlinearity is a major source of interference. In near-infrared spectroscopy, nonlinearity can arise from light path-length differences that can come from differences in particle size or density. The usefulness of support vector machine (SVM) regression to handle nonlinearity and improve the robustness of calibration models in scenarios where the calibration set did not include all the variability present in test was evaluated. Compared to partial least squares (PLS) regression, SVM regression was less affected by physical (particle size) and chemical (moisture) differences. The linearity of the SVM predicted values was also improved. Nevertheless, although visualization and interpretation tools have been developed to enhance the usability of SVM-based methods, work is yet to be done to provide chemometricians in the pharmaceutical industry with a regression method that can supplement PLS-based methods.

Consideration of VT5 etch-based OPC modeling

NASA Astrophysics Data System (ADS)

Lim, ChinTeong; Temchenko, Vlad; Kaiser, Dieter; Meusel, Ingo; Schmidt, Sebastian; Schneider, Jens; Niehoff, Martin

2008-03-01

Including etch-based empirical data during OPC model calibration is a desired yet controversial decision for OPC modeling, especially for process with a large litho to etch biasing. While many OPC software tools are capable of providing this functionality nowadays; yet few were implemented in manufacturing due to various risks considerations such as compromises in resist and optical effects prediction, etch model accuracy or even runtime concern. Conventional method of applying rule-based alongside resist model is popular but requires a lot of lengthy code generation to provide a leaner OPC input. This work discusses risk factors and their considerations, together with introduction of techniques used within Mentor Calibre VT5 etch-based modeling at sub 90nm technology node. Various strategies are discussed with the aim of better handling of large etch bias offset without adding complexity into final OPC package. Finally, results were presented to assess the advantages and limitations of the final method chosen.

Calibration of sea ice dynamic parameters in an ocean-sea ice model using an ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Massonnet, F.; Goosse, H.; Fichefet, T.; Counillon, F.

2014-07-01

The choice of parameter values is crucial in the course of sea ice model development, since parameters largely affect the modeled mean sea ice state. Manual tuning of parameters will soon become impractical, as sea ice models will likely include more parameters to calibrate, leading to an exponential increase of the number of possible combinations to test. Objective and automatic methods for parameter calibration are thus progressively called on to replace the traditional heuristic, "trial-and-error" recipes. Here a method for calibration of parameters based on the ensemble Kalman filter is implemented, tested and validated in the ocean-sea ice model NEMO-LIM3. Three dynamic parameters are calibrated: the ice strength parameter P*, the ocean-sea ice drag parameter Cw, and the atmosphere-sea ice drag parameter Ca. In twin, perfect-model experiments, the default parameter values are retrieved within 1 year of simulation. Using 2007-2012 real sea ice drift data, the calibration of the ice strength parameter P* and the oceanic drag parameter Cw improves clearly the Arctic sea ice drift properties. It is found that the estimation of the atmospheric drag Ca is not necessary if P* and Cw are already estimated. The large reduction in the sea ice speed bias with calibrated parameters comes with a slight overestimation of the winter sea ice areal export through Fram Strait and a slight improvement in the sea ice thickness distribution. Overall, the estimation of parameters with the ensemble Kalman filter represents an encouraging alternative to manual tuning for ocean-sea ice models.

Impact of the calibration period on the conceptual rainfall-runoff model parameter estimates

NASA Astrophysics Data System (ADS)

Todorovic, Andrijana; Plavsic, Jasna

2015-04-01

A conceptual rainfall-runoff model is defined by its structure and parameters, which are commonly inferred through model calibration. Parameter estimates depend on objective function(s), optimisation method, and calibration period. Model calibration over different periods may result in dissimilar parameter estimates, while model efficiency decreases outside calibration period. Problem of model (parameter) transferability, which conditions reliability of hydrologic simulations, has been investigated for decades. In this paper, dependence of the parameter estimates and model performance on calibration period is analysed. The main question that is addressed is: are there any changes in optimised parameters and model efficiency that can be linked to the changes in hydrologic or meteorological variables (flow, precipitation and temperature)? Conceptual, semi-distributed HBV-light model is calibrated over five-year periods shifted by a year (sliding time windows). Length of the calibration periods is selected to enable identification of all parameters. One water year of model warm-up precedes every simulation, which starts with the beginning of a water year. The model is calibrated using the built-in GAP optimisation algorithm. The objective function used for calibration is composed of Nash-Sutcliffe coefficient for flows and logarithms of flows, and volumetric error, all of which participate in the composite objective function with approximately equal weights. Same prior parameter ranges are used in all simulations. The model is calibrated against flows observed at the Slovac stream gauge on the Kolubara River in Serbia (records from 1954 to 2013). There are no trends in precipitation nor in flows, however, there is a statistically significant increasing trend in temperatures at this catchment. Parameter variability across the calibration periods is quantified in terms of standard deviations of normalised parameters, enabling detection of the most variable parameters. Correlation coefficients among optimised model parameters and total precipitation P, mean temperature T and mean flow Q are calculated to give an insight into parameter dependence on the hydrometeorological drivers. The results reveal high sensitivity of almost all model parameters towards calibration period. The highest variability is displayed by the refreezing coefficient, water holding capacity, and temperature gradient. The only statistically significant (decreasing) trend is detected in the evapotranspiration reduction threshold. Statistically significant correlation is detected between the precipitation gradient and precipitation depth, and between the time-area histogram base and flows. All other correlations are not statistically significant, implying that changes in optimised parameters cannot generally be linked to the changes in P, T or Q. As for the model performance, the model reproduces the observed runoff satisfactorily, though the runoff is slightly overestimated in wet periods. The Nash-Sutcliffe efficiency coefficient (NSE) ranges from 0.44 to 0.79. Higher NSE values are obtained over wetter periods, what is supported by statistically significant correlation between NSE and flows. Overall, no systematic variations in parameters or in model performance are detected. Parameter variability may therefore rather be attributed to errors in data or inadequacies in the model structure. Further research is required to examine the impact of the calibration strategy or model structure on the variability in optimised parameters in time.

Absolute Calibration of Optical Satellite Sensors Using Libya 4 Pseudo Invariant Calibration Site

NASA Technical Reports Server (NTRS)

Mishra, Nischal; Helder, Dennis; Angal, Amit; Choi, Jason; Xiong, Xiaoxiong

2014-01-01

The objective of this paper is to report the improvements in an empirical absolute calibration model developed at South Dakota State University using Libya 4 (+28.55 deg, +23.39 deg) pseudo invariant calibration site (PICS). The approach was based on use of the Terra MODIS as the radiometer to develop an absolute calibration model for the spectral channels covered by this instrument from visible to shortwave infrared. Earth Observing One (EO-1) Hyperion, with a spectral resolution of 10 nm, was used to extend the model to cover visible and near-infrared regions. A simple Bidirectional Reflectance Distribution function (BRDF) model was generated using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) observations over Libya 4 and the resulting model was validated with nadir data acquired from satellite sensors such as Aqua MODIS and Landsat 7 (L7) Enhanced Thematic Mapper (ETM+). The improvements in the absolute calibration model to account for the BRDF due to off-nadir measurements and annual variations in the atmosphere are summarized. BRDF models due to off-nadir viewing angles have been derived using the measurements from EO-1 Hyperion. In addition to L7 ETM+, measurements from other sensors such as Aqua MODIS, UK-2 Disaster Monitoring Constellation (DMC), ENVISAT Medium Resolution Imaging Spectrometer (MERIS) and Operational Land Imager (OLI) onboard Landsat 8 (L8), which was launched in February 2013, were employed to validate the model. These satellite sensors differ in terms of the width of their spectral bandpasses, overpass time, off-nadir-viewing capabilities, spatial resolution and temporal revisit time, etc. The results demonstrate that the proposed empirical calibration model has accuracy of the order of 3% with an uncertainty of about 2% for the sensors used in the study.

Relationship of physiography and snow area to stream discharge. [Kings River Watershed, California

NASA Technical Reports Server (NTRS)

Mccuen, R. H. (Principal Investigator)

1979-01-01

The author has identified the following significant results. A comparison of snowmelt runoff models shows that the accuracy of the Tangborn model and regression models is greater if the test data falls within the range of calibration than if the test data lies outside the range of calibration data. The regression models are significantly more accurate for forecasts of 60 days or more than for shorter prediction periods. The Tangborn model is more accurate for forecasts of 90 days or more than for shorter prediction periods. The Martinec model is more accurate for forecasts of one or two days than for periods of 3,5,10, or 15 days. Accuracy of the long-term models seems to be independent of forecast data. The sufficiency of the calibration data base is a function not only of the number of years of record but also of the accuracy with which the calibration years represent the total population of data years. Twelve years appears to be a sufficient length of record for each of the models considered, as long as the twelve years are representative of the population.

Hierarchical calibration and validation framework of bench-scale computational fluid dynamics simulations for solvent-based carbon capture. Part 2: Chemical absorption across a wetted wall column: Original Research Article: Hierarchical calibration and validation framework of bench-scale computational fluid dynamics simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Chao; Xu, Zhijie; Lai, Kevin

The first part of this paper (Part 1) presents a numerical model for non-reactive physical mass transfer across a wetted wall column (WWC). In Part 2, we improved the existing computational fluid dynamics (CFD) model to simulate chemical absorption occurring in a WWC as a bench-scale study of solvent-based carbon dioxide (CO2) capture. To generate data for WWC model validation, CO2 mass transfer across a monoethanolamine (MEA) solvent was first measured on a WWC experimental apparatus. The numerical model developed in this work has the ability to account for both chemical absorption and desorption of CO2 in MEA. In addition,more » the overall mass transfer coefficient predicted using traditional/empirical correlations is conducted and compared with CFD prediction results for both steady and wavy falling films. A Bayesian statistical calibration algorithm is adopted to calibrate the reaction rate constants in chemical absorption/desorption of CO2 across a falling film of MEA. The posterior distributions of the two transport properties, i.e., Henry’s constant and gas diffusivity in the non-reacting nitrous oxide (N2O)/MEA system obtained from Part 1 of this study, serves as priors for the calibration of CO2 reaction rate constants after using the N2O/CO2 analogy method. The calibrated model can be used to predict the CO2 mass transfer in a WWC for a wider range of operating conditions.« less

Hierarchical calibration and validation framework of bench-scale computational fluid dynamics simulations for solvent-based carbon capture. Part 2: Chemical absorption across a wetted wall column: Original Research Article: Hierarchical calibration and validation framework of bench-scale computational fluid dynamics simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Chao; Xu, Zhijie; Lai, Kevin

Part 1 of this paper presents a numerical model for non-reactive physical mass transfer across a wetted wall column (WWC). In Part 2, we improved the existing computational fluid dynamics (CFD) model to simulate chemical absorption occurring in a WWC as a bench-scale study of solvent-based carbon dioxide (CO 2) capture. In this study, to generate data for WWC model validation, CO 2 mass transfer across a monoethanolamine (MEA) solvent was first measured on a WWC experimental apparatus. The numerical model developed in this work can account for both chemical absorption and desorption of CO 2 in MEA. In addition,more » the overall mass transfer coefficient predicted using traditional/empirical correlations is conducted and compared with CFD prediction results for both steady and wavy falling films. A Bayesian statistical calibration algorithm is adopted to calibrate the reaction rate constants in chemical absorption/desorption of CO 2 across a falling film of MEA. The posterior distributions of the two transport properties, i.e., Henry's constant and gas diffusivity in the non-reacting nitrous oxide (N 2O)/MEA system obtained from Part 1 of this study, serves as priors for the calibration of CO 2 reaction rate constants after using the N 2O/CO 2 analogy method. Finally, the calibrated model can be used to predict the CO 2 mass transfer in a WWC for a wider range of operating conditions.« less

Hierarchical calibration and validation framework of bench-scale computational fluid dynamics simulations for solvent-based carbon capture. Part 2: Chemical absorption across a wetted wall column: Original Research Article: Hierarchical calibration and validation framework of bench-scale computational fluid dynamics simulations

DOE PAGES

Wang, Chao; Xu, Zhijie; Lai, Kevin; ...

2017-10-24

Part 1 of this paper presents a numerical model for non-reactive physical mass transfer across a wetted wall column (WWC). In Part 2, we improved the existing computational fluid dynamics (CFD) model to simulate chemical absorption occurring in a WWC as a bench-scale study of solvent-based carbon dioxide (CO 2) capture. In this study, to generate data for WWC model validation, CO 2 mass transfer across a monoethanolamine (MEA) solvent was first measured on a WWC experimental apparatus. The numerical model developed in this work can account for both chemical absorption and desorption of CO 2 in MEA. In addition,more » the overall mass transfer coefficient predicted using traditional/empirical correlations is conducted and compared with CFD prediction results for both steady and wavy falling films. A Bayesian statistical calibration algorithm is adopted to calibrate the reaction rate constants in chemical absorption/desorption of CO 2 across a falling film of MEA. The posterior distributions of the two transport properties, i.e., Henry's constant and gas diffusivity in the non-reacting nitrous oxide (N 2O)/MEA system obtained from Part 1 of this study, serves as priors for the calibration of CO 2 reaction rate constants after using the N 2O/CO 2 analogy method. Finally, the calibrated model can be used to predict the CO 2 mass transfer in a WWC for a wider range of operating conditions.« less

BRDF Calibration of Sintered PTFE in the SWIR

NASA Technical Reports Server (NTRS)

Georgiev, Georgi T.; Butler, James J.

2009-01-01

Satellite instruments operating in the reflective solar wavelength region often require accurate and precise determination of the Bidirectional Reflectance Distribution Function (BRDF) of laboratory-based diffusers used in their pre-flight calibrations and ground-based support of on-orbit remote sensing instruments. The Diffuser Calibration Facility at NASA's Goddard Space Flight Center is a secondary diffuser calibration standard after NEST for over two decades, providing numerous NASA projects with BRDF data in the UV, Visible and the NIR spectral regions. Currently the Diffuser Calibration Facility extended the covered spectral range from 900 nm up to 1.7 microns. The measurements were made using the existing scatterometer by replacing the Si photodiode based receiver with an InGaAs-based one. The BRDF data was recorded at normal incidence and scatter zenith angles from 10 to 60 deg. Tunable coherent light source was setup. Broadband light source application is under development. Gray-scale sintered PTFE samples were used at these first trials, illuminated with P and S polarized incident light. The results are discussed and compared to empirically generated BRDF data from simple model based on 8 deg directional/hemispherical measurements.

Characterization of Inactive Rocket Bodies Via Non-Resolved Photometric Data

NASA Astrophysics Data System (ADS)

Linares, R.; Palmer, D.; Thompson, D.; Klimenko, A.

2014-09-01

Recent events in space, including the collision of Russias Cosmos 2251 satellite with Iridium 33 and Chinas Feng Yun 1C anti-satellite demonstration, have stressed the capabilities of Space Surveillance Network (SSN) and its ability to provide accurate and actionable impact probability estimates. The SSN network has the unique challenge of tracking more than 18,000 resident space objects (RSOs) and providing critical collision avoidance warnings to military, NASA, and commercial systems. However, due to the large number of RSOs and the limited number of sensors available to track them, it is impossible to maintain persistent surveillance. Observation gaps result in large propagation intervals between measurements and close approaches. Coupled with nonlinear RSO dynamics this results in difficulty in modeling the probability distribution functions (pdfs) of the RSO. In particular low-Earth orbiting (LEO) satellites are heavily influenced by atmospheric drag, which is very difficult to model accurately. A number of atmospheric models exist which can be classified as either empirical or physics-based models. The current Air Force standard is the High Accuracy Satellite Drag Model (HASDM), which is an empirical model based on observation of calibration satellites. These satellite observations are used to determine model parameters based on their orbit determination solutions. Atmospheric orbits are perturbed by a number of factors including drag coefficient, attitude, and shape of the space object. The satellites used for the HASDM model calibration process are chosen because of their relatively simple shapes, to minimize errors introduced due to shape miss-modeling. Under this requirement the number of calibration satellites that can be used for calibrating the atmospheric models is limited. Los Alamos National Laboratory (LANL) has established a research effort, called IMPACT (Integrated Modeling of Perturbations in Atmospheres for Conjunction Tracking), to improve impact assessment via improved physics-based modeling. As part of this effort calibration satellite observations are used to dynamically calibrate the physics-based model and to improve its forecasting capability. The observations are collected from a variety of sources, including from LANLs own Raven-class optical telescope. This system collects both astrometric and photometric data on space objects. The photometric data will be used to estimate the space objects attitude and shape. Non-resolved photometric data have been studied by many as a mechanism for space object characterization. Photometry is the measurement of an objects flux or apparent brightness measured over a wavelength band. The temporal variation of photometric measurements is referred to as photometric signature. The photometric optical signature of an object contains information about shape, attitude, size and material composition. This work focuses on the processing of the data collected with LANLs telescope in an effort to use photometric data to expand the number of space objects that can be used as calibration satellites. A nonlinear least squares is used to estimate the attitude and angular velocity of the space object; a number of real data examples are shown. Inactive space objects are used for the real data examples and good estimation results are shown.

A Full-Envelope Air Data Calibration and Three-Dimensional Wind Estimation Method Using Global Output-Error Optimization and Flight-Test Techniques

NASA Technical Reports Server (NTRS)

Taylor, Brian R.

2012-01-01

A novel, efficient air data calibration method is proposed for aircraft with limited envelopes. This method uses output-error optimization on three-dimensional inertial velocities to estimate calibration and wind parameters. Calibration parameters are based on assumed calibration models for static pressure, angle of attack, and flank angle. Estimated wind parameters are the north, east, and down components. The only assumptions needed for this method are that the inertial velocities and Euler angles are accurate, the calibration models are correct, and that the steady-state component of wind is constant throughout the maneuver. A two-minute maneuver was designed to excite the aircraft over the range of air data calibration parameters and de-correlate the angle-of-attack bias from the vertical component of wind. Simulation of the X-48B (The Boeing Company, Chicago, Illinois) aircraft was used to validate the method, ultimately using data derived from wind-tunnel testing to simulate the un-calibrated air data measurements. Results from the simulation were accurate and robust to turbulence levels comparable to those observed in flight. Future experiments are planned to evaluate the proposed air data calibration in a flight environment.

Adaptive Prior Variance Calibration in the Bayesian Continual Reassessment Method

PubMed Central

Zhang, Jin; Braun, Thomas M.; Taylor, Jeremy M.G.

2012-01-01

Use of the Continual Reassessment Method (CRM) and other model-based approaches to design in Phase I clinical trials has increased due to the ability of the CRM to identify the maximum tolerated dose (MTD) better than the 3+3 method. However, the CRM can be sensitive to the variance selected for the prior distribution of the model parameter, especially when a small number of patients are enrolled. While methods have emerged to adaptively select skeletons and to calibrate the prior variance only at the beginning of a trial, there has not been any approach developed to adaptively calibrate the prior variance throughout a trial. We propose three systematic approaches to adaptively calibrate the prior variance during a trial and compare them via simulation to methods proposed to calibrate the variance at the beginning of a trial. PMID:22987660

Calibrating the orientation between a microlens array and a sensor based on projective geometry

NASA Astrophysics Data System (ADS)

Su, Lijuan; Yan, Qiangqiang; Cao, Jun; Yuan, Yan

2016-07-01

We demonstrate a method for calibrating a microlens array (MLA) with a sensor component by building a plenoptic camera with a conventional prime lens. This calibration method includes a geometric model, a setup to adjust the distance (L) between the prime lens and the MLA, a calibration procedure for determining the subimage centers, and an optimization algorithm. The geometric model introduces nine unknown parameters regarding the centers of the microlenses and their images, whereas the distance adjustment setup provides an initial guess for the distance L. The simulation results verify the effectiveness and accuracy of the proposed method. The experimental results demonstrate the calibration process can be performed with a commercial prime lens and the proposed method can be used to quantitatively evaluate whether a MLA and a sensor is assembled properly for plenoptic systems.

A Synthesis of Star Calibration Techniques for Ground-Based Narrowband Electron-Multiplying Charge-Coupled Device Imagers Used in Auroral Photometry

NASA Technical Reports Server (NTRS)

Grubbs, Guy II; Michell, Robert; Samara, Marilia; Hampton, Don; Jahn, Jorg-Micha

2016-01-01

A technique is presented for the periodic and systematic calibration of ground-based optical imagers. It is important to have a common system of units (Rayleighs or photon flux) for cross comparison as well as self-comparison over time. With the advancement in technology, the sensitivity of these imagers has improved so that stars can be used for more precise calibration. Background subtraction, flat fielding, star mapping, and other common techniques are combined in deriving a calibration technique appropriate for a variety of ground-based imager installations. Spectral (4278, 5577, and 8446 A ) ground-based imager data with multiple fields of view (19, 47, and 180 deg) are processed and calibrated using the techniques developed. The calibration techniques applied result in intensity measurements in agreement between different imagers using identical spectral filtering, and the intensity at each wavelength observed is within the expected range of auroral measurements. The application of these star calibration techniques, which convert raw imager counts into units of photon flux, makes it possible to do quantitative photometry. The computed photon fluxes, in units of Rayleighs, can be used for the absolute photometry between instruments or as input parameters for auroral electron transport models.

Global Parameter Optimization of CLM4.5 Using Sparse-Grid Based Surrogates

NASA Astrophysics Data System (ADS)

Lu, D.; Ricciuto, D. M.; Gu, L.

2016-12-01

Calibration of the Community Land Model (CLM) is challenging because of its model complexity, large parameter sets, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time. The goal of this study is to calibrate some of the CLM parameters in order to improve model projection of carbon fluxes. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first use advanced sparse grid (SG) interpolation to construct a surrogate system of the actual CLM model, and then we calibrate the surrogate model in the optimization process. As the surrogate model is a polynomial whose evaluation is fast, it can be efficiently evaluated with sufficiently large number of times in the optimization, which facilitates the global search. We calibrate five parameters against 12 months of GPP, NEP, and TLAI data from the U.S. Missouri Ozark (US-MOz) tower. The results indicate that an accurate surrogate model can be created for the CLM4.5 with a relatively small number of SG points (i.e., CLM4.5 simulations), and the application of the optimized parameters leads to a higher predictive capacity than the default parameter values in the CLM4.5 for the US-MOz site.

ZY3-02 Laser Altimeter Footprint Geolocation Prediction

PubMed Central

Xie, Junfeng; Tang, Xinming; Mo, Fan; Li, Guoyuan; Zhu, Guangbin; Wang, Zhenming; Fu, Xingke; Gao, Xiaoming; Dou, Xianhui

2017-01-01

Successfully launched on 30 May 2016, ZY3-02 is the first Chinese surveying and mapping satellite equipped with a lightweight laser altimeter. Calibration is necessary before the laser altimeter becomes operational. Laser footprint location prediction is the first step in calibration that is based on ground infrared detectors, and it is difficult because the sample frequency of the ZY3-02 laser altimeter is 2 Hz, and the distance between two adjacent laser footprints is about 3.5 km. In this paper, we build an on-orbit rigorous geometric prediction model referenced to the rigorous geometric model of optical remote sensing satellites. The model includes three kinds of data that must be predicted: pointing angle, orbit parameters, and attitude angles. The proposed method is verified by a ZY3-02 laser altimeter on-orbit geometric calibration test. Five laser footprint prediction experiments are conducted based on the model, and the laser footprint prediction accuracy is better than 150 m on the ground. The effectiveness and accuracy of the on-orbit rigorous geometric prediction model are confirmed by the test results. The geolocation is predicted precisely by the proposed method, and this will give a reference to the geolocation prediction of future land laser detectors in other laser altimeter calibration test. PMID:28934160

ZY3-02 Laser Altimeter Footprint Geolocation Prediction.

PubMed

Xie, Junfeng; Tang, Xinming; Mo, Fan; Li, Guoyuan; Zhu, Guangbin; Wang, Zhenming; Fu, Xingke; Gao, Xiaoming; Dou, Xianhui

2017-09-21

Successfully launched on 30 May 2016, ZY3-02 is the first Chinese surveying and mapping satellite equipped with a lightweight laser altimeter. Calibration is necessary before the laser altimeter becomes operational. Laser footprint location prediction is the first step in calibration that is based on ground infrared detectors, and it is difficult because the sample frequency of the ZY3-02 laser altimeter is 2 Hz, and the distance between two adjacent laser footprints is about 3.5 km. In this paper, we build an on-orbit rigorous geometric prediction model referenced to the rigorous geometric model of optical remote sensing satellites. The model includes three kinds of data that must be predicted: pointing angle, orbit parameters, and attitude angles. The proposed method is verified by a ZY3-02 laser altimeter on-orbit geometric calibration test. Five laser footprint prediction experiments are conducted based on the model, and the laser footprint prediction accuracy is better than 150 m on the ground. The effectiveness and accuracy of the on-orbit rigorous geometric prediction model are confirmed by the test results. The geolocation is predicted precisely by the proposed method, and this will give a reference to the geolocation prediction of future land laser detectors in other laser altimeter calibration test.

Absolute Spectrophotometric Calibration to 1% from the FUV through the near-IR

NASA Astrophysics Data System (ADS)

Finley, David

2006-07-01

We are requesting additional support to complete the work now being carried out under the Cycle 14 archive program, HST-AR-10654. The most critical component of that effort is an accurate determination of the STIS spectrometer LSF, so that we may correctly model the infill of the Balmer line cores by light redistributed from the wings and adjacent continuum. That is the essential input for obtaining accurate and unbiased effective temperatures and gravities, and hence calibrated fluxes, via line profile fitting of the WD calibration standards. To evaluate the published STIS LSF, we investigated the spectral images of the calibration targets, yielding several significant results: a} the STIS LSF varies significantly; b} existing observation-based spectroscopic LSFs or imaging PSFs are inadequate for deriving suitable spectroscopic LSFs; c} accounting for the PSF/LSF variability will improve spectrophotometric accuracy; d} the LSFs used for model fits must be consistent with the extraction process details; and, e} TinyTim-generated PSFs, with some modifications, provide the most suitable basis for producing the required LSFs that are tailored to each individual spectral observation. Based on our current {greatly improved} state of knowlege of the instrumental effects, we are now requesting additional support to complete the work needed to generate correct LSFs, and then carry out the analyses that were the subject of the original proposal.Our goal is the same: to produce a significant improvement to the existing HST calibration. The current calibration is based on three primary DA white dwarf standards, GD 71, GD 153,and G 191-B2B. The standard fluxes are calculated using NLTE models, with effective temperatures and gravities that were derived from Balmer line fits using LTE models. We propose to improve the accuracy and internal consistency of the calibration by deriving corrected effective temperatures and gravities based on fitting the observed line profiles with updated NLTE models, and including the fit results from multiple STIS spectra, rather than the {usually} 1 or 2 ground-based spectra used previously. We will also determine the fluxes for 5 new, fainter primary or secondary standards, extending the standard V magnitude lower limit from 13.4 to 16.5, and extending the wavelength coverage from 0.1 to 2.5 micron. The goal is to achieve an overall flux accuracy of 1%, which will be needed, for example, for the upcoming supernova survey missions to measure the equation of state of the dark energy that is accelerating the expansion of the universe.

Digital filtering and model updating methods for improving the robustness of near-infrared multivariate calibrations.

PubMed

Kramer, Kirsten E; Small, Gary W

2009-02-01

Fourier transform near-infrared (NIR) transmission spectra are used for quantitative analysis of glucose for 17 sets of prediction data sampled as much as six months outside the timeframe of the corresponding calibration data. Aqueous samples containing physiological levels of glucose in a matrix of bovine serum albumin and triacetin are used to simulate clinical samples such as blood plasma. Background spectra of a single analyte-free matrix sample acquired during the instrumental warm-up period on the prediction day are used for calibration updating and for determining the optimal frequency response of a preprocessing infinite impulse response time-domain digital filter. By tuning the filter and the calibration model to the specific instrumental response associated with the prediction day, the calibration model is given enhanced ability to operate over time. This methodology is demonstrated in conjunction with partial least squares calibration models built with a spectral range of 4700-4300 cm(-1). By using a subset of the background spectra to evaluate the prediction performance of the updated model, projections can be made regarding the success of subsequent glucose predictions. If a threshold standard error of prediction (SEP) of 1.5 mM is used to establish successful model performance with the glucose samples, the corresponding threshold for the SEP of the background spectra is found to be 1.3 mM. For calibration updating in conjunction with digital filtering, SEP values of all 17 prediction sets collected over 3-178 days displaced from the calibration data are below 1.5 mM. In addition, the diagnostic based on the background spectra correctly assesses the prediction performance in 16 of the 17 cases.

Statistical analysis of target acquisition sensor modeling experiments

NASA Astrophysics Data System (ADS)

Deaver, Dawne M.; Moyer, Steve

2015-05-01

The U.S. Army RDECOM CERDEC NVESD Modeling and Simulation Division is charged with the development and advancement of military target acquisition models to estimate expected soldier performance when using all types of imaging sensors. Two elements of sensor modeling are (1) laboratory-based psychophysical experiments used to measure task performance and calibrate the various models and (2) field-based experiments used to verify the model estimates for specific sensors. In both types of experiments, it is common practice to control or measure environmental, sensor, and target physical parameters in order to minimize uncertainty of the physics based modeling. Predicting the minimum number of test subjects required to calibrate or validate the model should be, but is not always, done during test planning. The objective of this analysis is to develop guidelines for test planners which recommend the number and types of test samples required to yield a statistically significant result.

Calibration of a polarimetric imaging SAR

NASA Technical Reports Server (NTRS)

Sarabandi, K.; Pierce, L. E.; Ulaby, F. T.

1991-01-01

Calibration of polarimetric imaging Synthetic Aperture Radars (SAR's) using point calibration targets is discussed. The four-port network calibration technique is used to describe the radar error model. The polarimetric ambiguity function of the SAR is then found using a single point target, namely a trihedral corner reflector. Based on this, an estimate for the backscattering coefficient of the terrain is found by a deconvolution process. A radar image taken by the JPL Airborne SAR (AIRSAR) is used for verification of the deconvolution calibration method. The calibrated responses of point targets in the image are compared both with theory and the POLCAL technique. Also, response of a distributed target are compared using the deconvolution and POLCAL techniques.

PowderSim: Lagrangian Discrete and Mesh-Free Continuum Simulation Code for Cohesive Soils

NASA Technical Reports Server (NTRS)

Johnson, Scott; Walton, Otis; Settgast, Randolph

2013-01-01

PowderSim is a calculation tool that combines a discrete-element method (DEM) module, including calibrated interparticle-interaction relationships, with a mesh-free, continuum, SPH (smoothed-particle hydrodynamics) based module that utilizes enhanced, calibrated, constitutive models capable of mimicking both large deformations and the flow behavior of regolith simulants and lunar regolith under conditions anticipated during in situ resource utilization (ISRU) operations. The major innovation introduced in PowderSim is to use a mesh-free method (SPH-based) with a calibrated and slightly modified critical-state soil mechanics constitutive model to extend the ability of the simulation tool to also address full-scale engineering systems in the continuum sense. The PowderSim software maintains the ability to address particle-scale problems, like size segregation, in selected regions with a traditional DEM module, which has improved contact physics and electrostatic interaction models.

Advancing computational methods for calibration of the Soil and Water Assessment Tool (SWAT): Application for modeling climate change impacts on water resources in the Upper Neuse Watershed of North Carolina

NASA Astrophysics Data System (ADS)

Ercan, Mehmet Bulent

Watershed-scale hydrologic models are used for a variety of applications from flood prediction, to drought analysis, to water quality assessments. A particular challenge in applying these models is calibration of the model parameters, many of which are difficult to measure at the watershed-scale. A primary goal of this dissertation is to contribute new computational methods and tools for calibration of watershed-scale hydrologic models and the Soil and Water Assessment Tool (SWAT) model, in particular. SWAT is a physically-based, watershed-scale hydrologic model developed to predict the impact of land management practices on water quality and quantity. The dissertation follows a manuscript format meaning it is comprised of three separate but interrelated research studies. The first two research studies focus on SWAT model calibration, and the third research study presents an application of the new calibration methods and tools to study climate change impacts on water resources in the Upper Neuse Watershed of North Carolina using SWAT. The objective of the first two studies is to overcome computational challenges associated with calibration of SWAT models. The first study evaluates a parallel SWAT calibration tool built using the Windows Azure cloud environment and a parallel version of the Dynamically Dimensioned Search (DDS) calibration method modified to run in Azure. The calibration tool was tested for six model scenarios constructed using three watersheds of increasing size (the Eno, Upper Neuse, and Neuse) for both a 2 year and 10 year simulation duration. Leveraging the cloud as an on demand computing resource allowed for a significantly reduced calibration time such that calibration of the Neuse watershed went from taking 207 hours on a personal computer to only 3.4 hours using 256 cores in the Azure cloud. The second study aims at increasing SWAT model calibration efficiency by creating an open source, multi-objective calibration tool using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). This tool was demonstrated through an application for the Upper Neuse Watershed in North Carolina, USA. The objective functions used for the calibration were Nash-Sutcliffe (E) and Percent Bias (PB), and the objective sites were the Flat, Little, and Eno watershed outlets. The results show that the use of multi-objective calibration algorithms for SWAT calibration improved model performance especially in terms of minimizing PB compared to the single objective model calibration. The third study builds upon the first two studies by leveraging the new calibration methods and tools to study future climate impacts on the Upper Neuse watershed. Statistically downscaled outputs from eight Global Circulation Models (GCMs) were used for both low and high emission scenarios to drive a well calibrated SWAT model of the Upper Neuse watershed. The objective of the study was to understand the potential hydrologic response of the watershed, which serves as a public water supply for the growing Research Triangle Park region of North Carolina, under projected climate change scenarios. The future climate change scenarios, in general, indicate an increase in precipitation and temperature for the watershed in coming decades. The SWAT simulations using the future climate scenarios, in general, suggest an increase in soil water and water yield, and a decrease in evapotranspiration within the Upper Neuse watershed. In summary, this dissertation advances the field of watershed-scale hydrologic modeling by (i) providing some of the first work to apply cloud computing for the computationally-demanding task of model calibration; (ii) providing a new, open source library that can be used by SWAT modelers to perform multi-objective calibration of their models; and (iii) advancing understanding of climate change impacts on water resources for an important watershed in the Research Triangle Park region of North Carolina. The third study leveraged the methodological advances presented in the first two studies. Therefore, the dissertation contains three independent by interrelated studies that collectively advance the field of watershed-scale hydrologic modeling and analysis.

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study

NASA Astrophysics Data System (ADS)

Attia, Khalid A. M.; Nassar, Mohammed W. I.; El-Zeiny, Mohamed B.; Serag, Ahmed

2017-01-01

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration.

Calibration of HEC-Ras hydrodynamic model using gauged discharge data and flood inundation maps

NASA Astrophysics Data System (ADS)

Tong, Rui; Komma, Jürgen

2017-04-01

The estimation of flood is essential for disaster alleviation. Hydrodynamic models are implemented to predict the occurrence and variance of flood in different scales. In practice, the calibration of hydrodynamic models aims to search the best possible parameters for the representation the natural flow resistance. Recent years have seen the calibration of hydrodynamic models being more actual and faster following the advance of earth observation products and computer based optimization techniques. In this study, the Hydrologic Engineering River Analysis System (HEC-Ras) model was set up with high-resolution digital elevation model from Laser scanner for the river Inn in Tyrol, Austria. 10 largest flood events from 19 hourly discharge gauges and flood inundation maps were selected to calibrate the HEC-Ras model. Manning roughness values and lateral inflow factors as parameters were automatically optimized with the Shuffled complex with Principal component analysis (SP-UCI) algorithm developed from the Shuffled Complex Evolution (SCE-UA). Different objective functions (Nash-Sutcliffe model efficiency coefficient, the timing of peak, peak value and Root-mean-square deviation) were used in single or multiple way. It was found that the lateral inflow factor was the most sensitive parameter. SP-UCI algorithm could avoid the local optimal and achieve efficient and effective parameters in the calibration of HEC-Ras model using flood extension images. As results showed, calibration by means of gauged discharge data and flood inundation maps, together with objective function of Nash-Sutcliffe model efficiency coefficient, was very robust to obtain more reliable flood simulation, and also to catch up with the peak value and the timing of peak.

A monolithic 3D-0D coupled closed-loop model of the heart and the vascular system: Experiment-based parameter estimation for patient-specific cardiac mechanics.

PubMed

Hirschvogel, Marc; Bassilious, Marina; Jagschies, Lasse; Wildhirt, Stephen M; Gee, Michael W

2016-10-15

A model for patient-specific cardiac mechanics simulation is introduced, incorporating a 3-dimensional finite element model of the ventricular part of the heart, which is coupled to a reduced-order 0-dimensional closed-loop vascular system, heart valve, and atrial chamber model. The ventricles are modeled by a nonlinear orthotropic passive material law. The electrical activation is mimicked by a prescribed parameterized active stress acting along a generic muscle fiber orientation. Our activation function is constructed such that the start of ventricular contraction and relaxation as well as the active stress curve's slope are parameterized. The imaging-based patient-specific ventricular model is prestressed to low end-diastolic pressure to account for the imaged, stressed configuration. Visco-elastic Robin boundary conditions are applied to the heart base and the epicardium to account for the embedding surrounding. We treat the 3D solid-0D fluid interaction as a strongly coupled monolithic problem, which is consistently linearized with respect to 3D solid and 0D fluid model variables to allow for a Newton-type solution procedure. The resulting coupled linear system of equations is solved iteratively in every Newton step using 2  ×  2 physics-based block preconditioning. Furthermore, we present novel efficient strategies for calibrating active contractile and vascular resistance parameters to experimental left ventricular pressure and stroke volume data gained in porcine experiments. Two exemplary states of cardiovascular condition are considered, namely, after application of vasodilatory beta blockers (BETA) and after injection of vasoconstrictive phenylephrine (PHEN). The parameter calibration to the specific individual and cardiovascular state at hand is performed using a 2-stage nonlinear multilevel method that uses a low-fidelity heart model to compute a parameter correction for the high-fidelity model optimization problem. We discuss 2 different low-fidelity model choices with respect to their ability to augment the parameter optimization. Because the periodic state conditions on the model (active stress, vascular pressures, and fluxes) are a priori unknown and also dependent on the parameters to be calibrated (and vice versa), we perform parameter calibration and periodic state condition estimation simultaneously. After a couple of heart beats, the calibration algorithm converges to a settled, periodic state because of conservation of blood volume within the closed-loop circulatory system. The proposed model and multilevel calibration method are cost-efficient and allow for an efficient determination of a patient-specific in silico heart model that reproduces physiological observations very well. Such an individual and state accurate model is an important predictive tool in intervention planning, assist device engineering and other medical applications. Copyright © 2016 John Wiley & Sons, Ltd.

FATE 5: A natural attenuation calibration tool for groundwater fate and transport modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nevin, J.P.; Connor, J.A.; Newell, C.J.

1997-12-31

A new groundwater attenuation modeling tool (FATE 5) has been developed to assist users with determining site-specific natural attenuation rates for organic constituents dissolved in groundwater. FATE 5 is based on and represents an enhancement to the Domenico analytical groundwater transport model. These enhancements include use of an optimization routine to match results from the Domenico model to actual measured site concentrations, an extensive database of chemical property data, and calculation of an estimate of the length of time needed for a plume to reach steady state conditions. FATE 5 was developed in Microsoft{reg_sign} Excel and is controlled by meansmore » of a simple, user-friendly graphical interface. Using the Solver routine built into Excel, FATE 5 is able to calibrate the attenuation rate used by the Domenico model to match site-specific data. By calibrating the decay rate to site-specific measurements, FATE 5 can yield accurate predictions of long-term natural attenuation processes within a groundwater within a groundwater plume. In addition, FATE 5 includes a formulation of the transient Domenico solution used to help the user determine if the steady-state assumptions employed by the model are appropriate. The calibrated groundwater flow model can then be used either to (i) predict upper-bound constituent concentrations in groundwater, based on an observed source zone concentration, or (ii) back-calculate a lower-bound SSTL value, based on a user-specified exposure point concentration at the groundwater point of exposure (POE). This paper reviews the major elements of the FATE 5 model - and gives results for real-world applications. Key modeling assumptions and summary guidelines regarding calculation procedures and input parameter selection are also addressed.« less

Stochastic isotropic hyperelastic materials: constitutive calibration and model selection

NASA Astrophysics Data System (ADS)

Mihai, L. Angela; Woolley, Thomas E.; Goriely, Alain

2018-03-01

Biological and synthetic materials often exhibit intrinsic variability in their elastic responses under large strains, owing to microstructural inhomogeneity or when elastic data are extracted from viscoelastic mechanical tests. For these materials, although hyperelastic models calibrated to mean data are useful, stochastic representations accounting also for data dispersion carry extra information about the variability of material properties found in practical applications. We combine finite elasticity and information theories to construct homogeneous isotropic hyperelastic models with random field parameters calibrated to discrete mean values and standard deviations of either the stress-strain function or the nonlinear shear modulus, which is a function of the deformation, estimated from experimental tests. These quantities can take on different values, corresponding to possible outcomes of the experiments. As multiple models can be derived that adequately represent the observed phenomena, we apply Occam's razor by providing an explicit criterion for model selection based on Bayesian statistics. We then employ this criterion to select a model among competing models calibrated to experimental data for rubber and brain tissue under single or multiaxial loads.

Estimation of adsorption isotherm and mass transfer parameters in protein chromatography using artificial neural networks.

PubMed

Wang, Gang; Briskot, Till; Hahn, Tobias; Baumann, Pascal; Hubbuch, Jürgen

2017-03-03

Mechanistic modeling has been repeatedly successfully applied in process development and control of protein chromatography. For each combination of adsorbate and adsorbent, the mechanistic models have to be calibrated. Some of the model parameters, such as system characteristics, can be determined reliably by applying well-established experimental methods, whereas others cannot be measured directly. In common practice of protein chromatography modeling, these parameters are identified by applying time-consuming methods such as frontal analysis combined with gradient experiments, curve-fitting, or combined Yamamoto approach. For new components in the chromatographic system, these traditional calibration approaches require to be conducted repeatedly. In the presented work, a novel method for the calibration of mechanistic models based on artificial neural network (ANN) modeling was applied. An in silico screening of possible model parameter combinations was performed to generate learning material for the ANN model. Once the ANN model was trained to recognize chromatograms and to respond with the corresponding model parameter set, it was used to calibrate the mechanistic model from measured chromatograms. The ANN model's capability of parameter estimation was tested by predicting gradient elution chromatograms. The time-consuming model parameter estimation process itself could be reduced down to milliseconds. The functionality of the method was successfully demonstrated in a study with the calibration of the transport-dispersive model (TDM) and the stoichiometric displacement model (SDM) for a protein mixture. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

Coupling machine learning with mechanistic models to study runoff production and river flow at the hillslope scale

NASA Astrophysics Data System (ADS)

Marçais, J.; Gupta, H. V.; De Dreuzy, J. R.; Troch, P. A. A.

2016-12-01

Geomorphological structure and geological heterogeneity of hillslopes are major controls on runoff responses. The diversity of hillslopes (morphological shapes and geological structures) on one hand, and the highly non linear runoff mechanism response on the other hand, make it difficult to transpose what has been learnt at one specific hillslope to another. Therefore, making reliable predictions on runoff appearance or river flow for a given hillslope is a challenge. Applying a classic model calibration (based on inverse problems technique) requires doing it for each specific hillslope and having some data available for calibration. When applied to thousands of cases it cannot always be promoted. Here we propose a novel modeling framework based on coupling process based models with data based approach. First we develop a mechanistic model, based on hillslope storage Boussinesq equations (Troch et al. 2003), able to model non linear runoff responses to rainfall at the hillslope scale. Second we set up a model database, representing thousands of non calibrated simulations. These simulations investigate different hillslope shapes (real ones obtained by analyzing 5m digital elevation model of Brittany and synthetic ones), different hillslope geological structures (i.e. different parametrizations) and different hydrologic forcing terms (i.e. different infiltration chronicles). Then, we use this model library to train a machine learning model on this physically based database. Machine learning model performance is then assessed by a classic validating phase (testing it on new hillslopes and comparing machine learning with mechanistic outputs). Finally we use this machine learning model to learn what are the hillslope properties controlling runoffs. This methodology will be further tested combining synthetic datasets with real ones.

Kinetics of autothermal thermophilic aerobic digestion - application and extension of Activated Sludge Model No 1 at thermophilic temperatures.

PubMed

Kovács, R; Miháltz, P; Csikor, Zs

2007-01-01

The application of an ASM1-based mathematical model for the modeling of autothermal thermophilic aerobic digestion is demonstrated. Based on former experimental results the original ASM1 was extended by the activation of facultative thermophiles from the feed sludge and a new component, the thermophilic biomass was introduced. The resulting model was calibrated in the temperature range of 20-60 degrees C. The temperature dependence of the growth and decay rates in the model is given in terms of the slightly modified Arrhenius and Topiwala-Sinclair equations. The capabilities of the calibrated model in realistic ATAD scenarios are demonstrated with a focus on autothermal properties of ATAD systems at different conditions.

A Semi-Automatic Image-Based Close Range 3D Modeling Pipeline Using a Multi-Camera Configuration

PubMed Central

Rau, Jiann-Yeou; Yeh, Po-Chia

2012-01-01

The generation of photo-realistic 3D models is an important task for digital recording of cultural heritage objects. This study proposes an image-based 3D modeling pipeline which takes advantage of a multi-camera configuration and multi-image matching technique that does not require any markers on or around the object. Multiple digital single lens reflex (DSLR) cameras are adopted and fixed with invariant relative orientations. Instead of photo-triangulation after image acquisition, calibration is performed to estimate the exterior orientation parameters of the multi-camera configuration which can be processed fully automatically using coded targets. The calibrated orientation parameters of all cameras are applied to images taken using the same camera configuration. This means that when performing multi-image matching for surface point cloud generation, the orientation parameters will remain the same as the calibrated results, even when the target has changed. Base on this invariant character, the whole 3D modeling pipeline can be performed completely automatically, once the whole system has been calibrated and the software was seamlessly integrated. Several experiments were conducted to prove the feasibility of the proposed system. Images observed include that of a human being, eight Buddhist statues, and a stone sculpture. The results for the stone sculpture, obtained with several multi-camera configurations were compared with a reference model acquired by an ATOS-I 2M active scanner. The best result has an absolute accuracy of 0.26 mm and a relative accuracy of 1:17,333. It demonstrates the feasibility of the proposed low-cost image-based 3D modeling pipeline and its applicability to a large quantity of antiques stored in a museum. PMID:23112656

A semi-automatic image-based close range 3D modeling pipeline using a multi-camera configuration.

PubMed

Rau, Jiann-Yeou; Yeh, Po-Chia

2012-01-01

The generation of photo-realistic 3D models is an important task for digital recording of cultural heritage objects. This study proposes an image-based 3D modeling pipeline which takes advantage of a multi-camera configuration and multi-image matching technique that does not require any markers on or around the object. Multiple digital single lens reflex (DSLR) cameras are adopted and fixed with invariant relative orientations. Instead of photo-triangulation after image acquisition, calibration is performed to estimate the exterior orientation parameters of the multi-camera configuration which can be processed fully automatically using coded targets. The calibrated orientation parameters of all cameras are applied to images taken using the same camera configuration. This means that when performing multi-image matching for surface point cloud generation, the orientation parameters will remain the same as the calibrated results, even when the target has changed. Base on this invariant character, the whole 3D modeling pipeline can be performed completely automatically, once the whole system has been calibrated and the software was seamlessly integrated. Several experiments were conducted to prove the feasibility of the proposed system. Images observed include that of a human being, eight Buddhist statues, and a stone sculpture. The results for the stone sculpture, obtained with several multi-camera configurations were compared with a reference model acquired by an ATOS-I 2M active scanner. The best result has an absolute accuracy of 0.26 mm and a relative accuracy of 1:17,333. It demonstrates the feasibility of the proposed low-cost image-based 3D modeling pipeline and its applicability to a large quantity of antiques stored in a museum.

Spectral irradiance calibration in the infrared. I - Ground-based and IRAS broadband calibrations

NASA Technical Reports Server (NTRS)

Cohen, Martin; Walker, Russell G.; Barlow, Michael J.; Deacon, John R.

1992-01-01

Absolutely calibrated versions of realistic model atmosphere calculations for Sirius and Vega by Kurucz (1991) are presented and used as a basis to offer a new absolute calibration of infrared broad and narrow filters. In-band fluxes for Vega are obtained and defined to be zero magnitude at all wavelengths shortward of 20 microns. Existing infrared photometry is used differentially to establish an absolute scale of the new Sirius model, yielding an angular diameter within 1 sigma of the mean determined interferometrically by Hanbury Brown et al. (1974). The use of Sirius as a primary infrared stellar standard beyond the 20 micron region is suggested. Isophotal wavelengths and monochromatic flux densities for both Vega and Sirius are tabulated.

Whole body counter calibration using Monte Carlo modeling with an array of phantom sizes based on national anthropometric reference data

NASA Astrophysics Data System (ADS)

Shypailo, R. J.; Ellis, K. J.

2011-05-01

During construction of the whole body counter (WBC) at the Children's Nutrition Research Center (CNRC), efficiency calibration was needed to translate acquired counts of 40K to actual grams of potassium for measurement of total body potassium (TBK) in a diverse subject population. The MCNP Monte Carlo n-particle simulation program was used to describe the WBC (54 detectors plus shielding), test individual detector counting response, and create a series of virtual anthropomorphic phantoms based on national reference anthropometric data. Each phantom included an outer layer of adipose tissue and an inner core of lean tissue. Phantoms were designed for both genders representing ages 3.5 to 18.5 years with body sizes from the 5th to the 95th percentile based on body weight. In addition, a spherical surface source surrounding the WBC was modeled in order to measure the effects of subject mass on room background interference. Individual detector measurements showed good agreement with the MCNP model. The background source model came close to agreement with empirical measurements, but showed a trend deviating from unity with increasing subject size. Results from the MCNP simulation of the CNRC WBC agreed well with empirical measurements using BOMAB phantoms. Individual detector efficiency corrections were used to improve the accuracy of the model. Nonlinear multiple regression efficiency calibration equations were derived for each gender. Room background correction is critical in improving the accuracy of the WBC calibration.

Man vs. Machine: An interactive poll to evaluate hydrological model performance of a manual and an automatic calibration

NASA Astrophysics Data System (ADS)

Wesemann, Johannes; Burgholzer, Reinhard; Herrnegger, Mathew; Schulz, Karsten

2017-04-01

In recent years, a lot of research in hydrological modelling has been invested to improve the automatic calibration of rainfall-runoff models. This includes for example (1) the implementation of new optimisation methods, (2) the incorporation of new and different objective criteria and signatures in the optimisation and (3) the usage of auxiliary data sets apart from runoff. Nevertheless, in many applications manual calibration is still justifiable and frequently applied. The hydrologist performing the manual calibration, with his expert knowledge, is able to judge the hydrographs simultaneously concerning details but also in a holistic view. This integrated eye-ball verification procedure available to man can be difficult to formulate in objective criteria, even when using a multi-criteria approach. Comparing the results of automatic and manual calibration is not straightforward. Automatic calibration often solely involves objective criteria such as Nash-Sutcliffe Efficiency Coefficient or the Kling-Gupta-Efficiency as a benchmark during the calibration. Consequently, a comparison based on such measures is intrinsically biased towards automatic calibration. Additionally, objective criteria do not cover all aspects of a hydrograph leaving questions concerning the quality of a simulation open. This contribution therefore seeks to examine the quality of manually and automatically calibrated hydrographs by interactively involving expert knowledge in the evaluation. Simulations have been performed for the Mur catchment in Austria with the rainfall-runoff model COSERO using two parameter sets evolved from a manual and an automatic calibration. A subset of resulting hydrographs for observation and simulation, representing the typical flow conditions and events, will be evaluated in this study. In an interactive crowdsourcing approach experts attending the session can vote for their preferred simulated hydrograph without having information on the calibration method that produced the respective hydrograph. Therefore, the result of the poll can be seen as an additional quality criterion for the comparison of the two different approaches and help in the evaluation of the automatic calibration method.

Model-based monitoring of stormwater runoff quality.

PubMed

Birch, Heidi; Vezzaro, Luca; Mikkelsen, Peter Steen

2013-01-01

Monitoring of micropollutants (MP) in stormwater is essential to evaluate the impacts of stormwater on the receiving aquatic environment. The aim of this study was to investigate how different strategies for monitoring of stormwater quality (combining a model with field sampling) affect the information obtained about MP discharged from the monitored system. A dynamic stormwater quality model was calibrated using MP data collected by automatic volume-proportional sampling and passive sampling in a storm drainage system on the outskirts of Copenhagen (Denmark) and a 10-year rain series was used to find annual average (AA) and maximum event mean concentrations. Use of this model reduced the uncertainty of predicted AA concentrations compared to a simple stochastic method based solely on data. The predicted AA concentration, obtained by using passive sampler measurements (1 month installation) for calibration of the model, resulted in the same predicted level but with narrower model prediction bounds than by using volume-proportional samples for calibration. This shows that passive sampling allows for a better exploitation of the resources allocated for stormwater quality monitoring.

Near-infrared reflectance spectroscopy calibrations for assessment of oil, phenols, glucosinolates and fatty acid content in the intact seeds of oilseed Brassica species.

PubMed

Sen, Rahul; Sharma, Sanjula; Kaur, Gurpreet; Banga, Surinder S

2018-01-31

Very few near-infrared reflectance spectroscopy (NIRS) calibration models are available for non-destructive estimation of seed quality traits in Brassica juncea. Those that are available also fail to adequately discern variation for oleic acid (C 18:1 ) , linolenic (C 18:3 ) fatty acids, meal glucosinolates and phenols. We report the development of a new NIRS calibration equation that is expected to fill the gaps in the existing NIRS equations. Calibrations were based on the reference values of important quality traits estimated from a purposely selected germplasm set comprising 240 genotypes of B. juncea and 193 of B. napus. We were able to develop optimal NIRS-based calibration models for oil, phenols, glucosinolates, oleic acid, linoleic acid and erucic acid for B. juncea and B. napus. Correlation coefficients (RSQ) of the external validations appeared greater than 0.7 for the majority of traits, such as oil (0.766, 0.865), phenols (0.821, 0.915), glucosinolates (0.951, 0.986), oleic acid (0.814. 0.810), linoleic acid (0.974, 0.781) and erucic acid (0.963, 0.943) for B. juncea and B. napus, respectively. The results demonstrate the robust predictive power of the developed calibration models for rapid estimation of many quality traits in intact rapeseed-mustard seeds which will assist plant breeders in effective screening and selection of lines in quality improvement breeding programmes. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

Characterizing Flow and Suspended Sediment Trends in the Sacramento River Basin, CA Using Hydrologic Simulation Program - FORTRAN (HSPF)

NASA Astrophysics Data System (ADS)

Stern, M. A.; Flint, L. E.; Flint, A. L.; Wright, S. A.; Minear, J. T.

2014-12-01

A watershed model of the Sacramento River Basin, CA was developed to simulate streamflow and suspended sediment transport to the San Francisco Bay Delta (SFBD) for fifty years (1958-2008) using the Hydrological Simulation Program - FORTRAN (HSPF). To compensate for the large model domain and sparse data, rigorous meteorological development and characterization of hydraulic geometry were employed to spatially distribute climate and hydrologic processes in unmeasured locations. Parameterization techniques sought to include known spatial information for tributaries such as soil information and slope, and then parameters were scaled up or down during calibration to retain the spatial characteristics of the land surface in un-gaged areas. Accuracy was assessed by comparing model calibration to measured streamflow. Calibration and validation of the Sacramento River ranged from "good" to "very good" performance based upon a "goodness-of-fit" statistical guideline. Model calibration to measured sediment loads were underestimated on average by 39% for the Sacramento River, and model calibration to suspended sediment concentrations were underestimated on average by 22% for the Sacramento River. Sediment loads showed a slight decreasing trend from 1958-2008 and was significant (p < 0.0025) in the lower 50% of stream flows. Hypothetical climate change scenarios were developed using the Climate Assessment Tool (CAT). Several wet and dry scenarios coupled with temperature increases were imposed on the historical base conditions to evaluate sensitivity of streamflow and sediment on potential changes in climate. Wet scenarios showed an increase of 9.7 - 17.5% in streamflow, a 7.6 - 17.5% increase in runoff, and a 30 - 93% increase in sediment loads. The dry scenarios showed a roughly 5% decrease in flow and runoff, and a 16 - 18% decrease in sediment loads. The base hydrology was most sensitive to a temperature increase of 1.5 degrees Celsius and an increase in storm intensity and frequency. The complete calibrated HSPF model will use future climate scenarios to make projections of potential hydrologic and sediment trends to the SFBD from 2000-2100.

The investigation of social networks based on multi-component random graphs

NASA Astrophysics Data System (ADS)

Zadorozhnyi, V. N.; Yudin, E. B.

2018-01-01

The methods of non-homogeneous random graphs calibration are developed for social networks simulation. The graphs are calibrated by the degree distributions of the vertices and the edges. The mathematical foundation of the methods is formed by the theory of random graphs with the nonlinear preferential attachment rule and the theory of Erdôs-Rényi random graphs. In fact, well-calibrated network graph models and computer experiments with these models would help developers (owners) of the networks to predict their development correctly and to choose effective strategies for controlling network projects.

Radiometric analysis of the longwave infrared channel of the Thematic Mapper on LANDSAT 4 and 5

NASA Technical Reports Server (NTRS)

Schott, John R.; Volchok, William J.; Biegel, Joseph D.

1986-01-01

The first objective was to evaluate the postlaunch radiometric calibration of the LANDSAT Thematic Mapper (TM) band 6 data. The second objective was to determine to what extent surface temperatures could be computed from the TM and 6 data using atmospheric propagation models. To accomplish this, ground truth data were compared to a single TM-4 band 6 data set. This comparison indicated satisfactory agreement over a narrow temperature range. The atmospheric propagation model (modified LOWTRAN 5A) was used to predict surface temperature values based on the radiance at the spacecraft. The aircraft data were calibrated using a multi-altitude profile calibration technique which had been extensively tested in previous studies. This aircraft calibration permitted measurement of surface temperatures based on the radiance reaching the aircraft. When these temperature values are evaluated, an error in the satellite's ability to predict surface temperatures can be estimated. This study indicated that by carefully accounting for various sensor calibration and atmospheric propagation effects, and expected error (1 standard deviation) in surface temperature would be 0.9 K. This assumes no error in surface emissivity and no sampling error due to target location. These results indicate that the satellite calibration is within nominal limits to within this study's ability to measure error.

Longitudinal train dynamics model for a rail transit simulation system

DOE PAGES

Wang, Jinghui; Rakha, Hesham A.

2018-01-01

The paper develops a longitudinal train dynamics model in support of microscopic railway transportation simulation. The model can be calibrated without any mechanical data making it ideal for implementation in transportation simulators. The calibration and validation work is based on data collected from the Portland light rail train fleet. The calibration procedure is mathematically formulated as a constrained non-linear optimization problem. The validity of the model is assessed by comparing instantaneous model predictions against field observations, and also evaluated in the domains of acceleration/deceleration versus speed and acceleration/deceleration versus distance. A test is conducted to investigate the adequacy of themore » model in simulation implementation. The results demonstrate that the proposed model can adequately capture instantaneous train dynamics, and provides good performance in the simulation test. Thus, the model provides a simple theoretical foundation for microscopic simulators and will significantly support the planning, management and control of railway transportation systems.« less

Global sensitivity analysis of a filtration model for submerged anaerobic membrane bioreactors (AnMBR).

PubMed

Robles, A; Ruano, M V; Ribes, J; Seco, A; Ferrer, J

2014-04-01

The results of a global sensitivity analysis of a filtration model for submerged anaerobic MBRs (AnMBRs) are assessed in this paper. This study aimed to (1) identify the less- (or non-) influential factors of the model in order to facilitate model calibration and (2) validate the modelling approach (i.e. to determine the need for each of the proposed factors to be included in the model). The sensitivity analysis was conducted using a revised version of the Morris screening method. The dynamic simulations were conducted using long-term data obtained from an AnMBR plant fitted with industrial-scale hollow-fibre membranes. Of the 14 factors in the model, six were identified as influential, i.e. those calibrated using off-line protocols. A dynamic calibration (based on optimisation algorithms) of these influential factors was conducted. The resulting estimated model factors accurately predicted membrane performance. Copyright © 2014 Elsevier Ltd. All rights reserved.

Longitudinal train dynamics model for a rail transit simulation system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jinghui; Rakha, Hesham A.

The paper develops a longitudinal train dynamics model in support of microscopic railway transportation simulation. The model can be calibrated without any mechanical data making it ideal for implementation in transportation simulators. The calibration and validation work is based on data collected from the Portland light rail train fleet. The calibration procedure is mathematically formulated as a constrained non-linear optimization problem. The validity of the model is assessed by comparing instantaneous model predictions against field observations, and also evaluated in the domains of acceleration/deceleration versus speed and acceleration/deceleration versus distance. A test is conducted to investigate the adequacy of themore » model in simulation implementation. The results demonstrate that the proposed model can adequately capture instantaneous train dynamics, and provides good performance in the simulation test. Thus, the model provides a simple theoretical foundation for microscopic simulators and will significantly support the planning, management and control of railway transportation systems.« less

A Case Study on a Combination NDVI Forecasting Model Based on the Entropy Weight Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Shengzhi; Ming, Bo; Huang, Qiang

It is critically meaningful to accurately predict NDVI (Normalized Difference Vegetation Index), which helps guide regional ecological remediation and environmental managements. In this study, a combination forecasting model (CFM) was proposed to improve the performance of NDVI predictions in the Yellow River Basin (YRB) based on three individual forecasting models, i.e., the Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Support Vector Machine (SVM) models. The entropy weight method was employed to determine the weight coefficient for each individual model depending on its predictive performance. Results showed that: (1) ANN exhibits the highest fitting capability among the four orecastingmore » models in the calibration period, whilst its generalization ability becomes weak in the validation period; MLR has a poor performance in both calibration and validation periods; the predicted results of CFM in the calibration period have the highest stability; (2) CFM generally outperforms all individual models in the validation period, and can improve the reliability and stability of predicted results through combining the strengths while reducing the weaknesses of individual models; (3) the performances of all forecasting models are better in dense vegetation areas than in sparse vegetation areas.« less

Use of the Moon for spacecraft calibration over 350-2500 nm

USGS Publications Warehouse

Kieffer, H.H.; Anderson, J.M.

1998-01-01

The Moon is the only natural object outside the Earth's atmosphere that is within the dynamic range of most imaging instruments on Earth-orbiting spacecraft. The excellent photometric stability of the Lunar surface will allow its use as a long-term instrument calibration source once the dependence of Lunar spectral radiance on phase and libration angles are well characterized. A program to provide this characterization is underway. Observations are being made in 23 bands within 350-950 nm, 7 of which correspond closely with spacecraft instrument bands. Observations in nine bands within 950-2500 nm began recently. Although at this time the absolute Lunar radiance model is preliminary and uncertainties are larger than most instrument calibration goals, changes in spacecraft instrument sensitivity can be precisely monitored and absolute calibration can be applied retroactively as the accuracy of the Lunar spectral radiance model improves. Several space-based imaging systems have already begun using the Moon for calibration and the EOS AM-1 platform will make periodic attitude maneuvers for Lunar and space calibration.

A crystal plasticity model for slip in hexagonal close packed metals based on discrete dislocation simulations

NASA Astrophysics Data System (ADS)

Messner, Mark C.; Rhee, Moono; Arsenlis, Athanasios; Barton, Nathan R.

2017-06-01

This work develops a method for calibrating a crystal plasticity model to the results of discrete dislocation (DD) simulations. The crystal model explicitly represents junction formation and annihilation mechanisms and applies these mechanisms to describe hardening in hexagonal close packed metals. The model treats these dislocation mechanisms separately from elastic interactions among populations of dislocations, which the model represents through a conventional strength-interaction matrix. This split between elastic interactions and junction formation mechanisms more accurately reproduces the DD data and results in a multi-scale model that better represents the lower scale physics. The fitting procedure employs concepts of machine learning—feature selection by regularized regression and cross-validation—to develop a robust, physically accurate crystal model. The work also presents a method for ensuring the final, calibrated crystal model respects the physical symmetries of the crystal system. Calibrating the crystal model requires fitting two linear operators: one describing elastic dislocation interactions and another describing junction formation and annihilation dislocation reactions. The structure of these operators in the final, calibrated model reflect the crystal symmetry and slip system geometry of the DD simulations.

Agricultural Policy Environmental eXtender Simulation of Three Adjacent Row-Crop Watersheds in the Claypan Region.

PubMed

Anomaa Senaviratne, G M M M; Udawatta, Ranjith P; Baffaut, Claire; Anderson, Stephen H

2013-01-01

The Agricultural Policy Environmental Extender (APEX) model is used to evaluate best management practices on pollutant loading in whole farms or small watersheds. The objectives of this study were to conduct a sensitivity analysis to determine the effect of model parameters on APEX output and use the parameterized, calibrated, and validated model to evaluate long-term benefits of grass waterways. The APEX model was used to model three (East, Center, and West) adjacent field-size watersheds with claypan soils under a no-till corn ( L.)/soybean [ (L.) Merr.] rotation. Twenty-seven parameters were sensitive for crop yield, runoff, sediment, nitrogen (dissolved and total), and phosphorous (dissolved and total) simulations. The model was calibrated using measured event-based data from the Center watershed from 1993 to 1997 and validated with data from the West and East watersheds. Simulated crop yields were within ±13% of the measured yield. The model performance for event-based runoff was excellent, with calibration and validation > 0.9 and Nash-Sutcliffe coefficients (NSC) > 0.8, respectively. Sediment and total nitrogen calibration results were satisfactory for larger rainfall events (>50 mm), with > 0.5 and NSC > 0.4, but validation results remained poor, with NSC between 0.18 and 0.3. Total phosphorous was well calibrated and validated, with > 0.8 and NSC > 0.7, respectively. The presence of grass waterways reduced annual total phosphorus loadings by 13 to 25%. The replicated study indicates that APEX provides a convenient and efficient tool to evaluate long-term benefits of conservation practices. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Intercomparison of hydrological model structures and calibration approaches in climate scenario impact projections

NASA Astrophysics Data System (ADS)

Vansteenkiste, Thomas; Tavakoli, Mohsen; Ntegeka, Victor; De Smedt, Florimond; Batelaan, Okke; Pereira, Fernando; Willems, Patrick

2014-11-01

The objective of this paper is to investigate the effects of hydrological model structure and calibration on climate change impact results in hydrology. The uncertainty in the hydrological impact results is assessed by the relative change in runoff volumes and peak and low flow extremes from historical and future climate conditions. The effect of the hydrological model structure is examined through the use of five hydrological models with different spatial resolutions and process descriptions. These were applied to a medium sized catchment in Belgium. The models vary from the lumped conceptual NAM, PDM and VHM models over the intermediate detailed and distributed WetSpa model to the fully distributed MIKE SHE model. The latter model accounts for the 3D groundwater processes and interacts bi-directionally with a full hydrodynamic MIKE 11 river model. After careful and manual calibration of these models, accounting for the accuracy of the peak and low flow extremes and runoff subflows, and the changes in these extremes for changing rainfall conditions, the five models respond in a similar way to the climate scenarios over Belgium. Future projections on peak flows are highly uncertain with expected increases as well as decreases depending on the climate scenario. The projections on future low flows are more uniform; low flows decrease (up to 60%) for all models and for all climate scenarios. However, the uncertainties in the impact projections are high, mainly in the dry season. With respect to the model structural uncertainty, the PDM model simulates significantly higher runoff peak flows under future wet scenarios, which is explained by its specific model structure. For the low flow extremes, the MIKE SHE model projects significantly lower low flows in dry scenario conditions in comparison to the other models, probably due to its large difference in process descriptions for the groundwater component, the groundwater-river interactions. The effect of the model calibration was tested by comparing the manual calibration approach with automatic calibrations of the VHM model based on different objective functions. The calibration approach did not significantly alter the model results for peak flow, but the low flow projections were again highly influenced. Model choice as well as calibration strategy hence have a critical impact on low flows, more than on peak flows. These results highlight the high uncertainty in low flow modelling, especially in a climate change context.

Measurement and analysis of electron-neutral collision frequency in the calibrated cutoff probe

DOE Office of Scientific and Technical Information (OSTI.GOV)

You, K. H.; Seo, B. H.; Kim, J. H.

2016-03-15

As collisions between electrons and neutral particles constitute one of the most representative physical phenomena in weakly ionized plasma, the electron-neutral (e-n) collision frequency is a very important plasma parameter as regards understanding the physics of this material. In this paper, we measured the e-n collision frequency in the plasma using a calibrated cutoff-probe. A highly accurate reactance spectrum of the plasma/cutoff-probe system, which is expected based on previous cutoff-probe circuit simulations [Kim et al., Appl. Phys. Lett. 99, 131502 (2011)], is obtained using the calibrated cutoff-probe method, and the e-n collision frequency is calculated based on the cutoff-probe circuitmore » model together with the high-frequency conductance model. The measured e-n collision frequency (by the calibrated cutoff-probe method) is compared and analyzed with that obtained using a Langmuir probe, with the latter being calculated from the measured electron-energy distribution functions, in wide range of gas pressure.« less

[Local Regression Algorithm Based on Net Analyte Signal and Its Application in Near Infrared Spectral Analysis].

PubMed

Zhang, Hong-guang; Lu, Jian-gang

2016-02-01

Abstract To overcome the problems of significant difference among samples and nonlinearity between the property and spectra of samples in spectral quantitative analysis, a local regression algorithm is proposed in this paper. In this algorithm, net signal analysis method(NAS) was firstly used to obtain the net analyte signal of the calibration samples and unknown samples, then the Euclidean distance between net analyte signal of the sample and net analyte signal of calibration samples was calculated and utilized as similarity index. According to the defined similarity index, the local calibration sets were individually selected for each unknown sample. Finally, a local PLS regression model was built on each local calibration sets for each unknown sample. The proposed method was applied to a set of near infrared spectra of meat samples. The results demonstrate that the prediction precision and model complexity of the proposed method are superior to global PLS regression method and conventional local regression algorithm based on spectral Euclidean distance.

3D aquifer characterization using stochastic streamline calibration

NASA Astrophysics Data System (ADS)

Jang, Minchul

2007-03-01

In this study, a new inverse approach, stochastic streamline calibration is proposed. Using both a streamline concept and a stochastic technique, stochastic streamline calibration optimizes an identified field to fit in given observation data in a exceptionally fast and stable fashion. In the stochastic streamline calibration, streamlines are adopted as basic elements not only for describing fluid flow but also for identifying the permeability distribution. Based on the streamline-based inversion by Agarwal et al. [Agarwal B, Blunt MJ. Streamline-based method with full-physics forward simulation for history matching performance data of a North sea field. SPE J 2003;8(2):171-80], Wang and Kovscek [Wang Y, Kovscek AR. Streamline approach for history matching production data. SPE J 2000;5(4):353-62], permeability is modified rather along streamlines than at the individual gridblocks. Permeabilities in the gridblocks which a streamline passes are adjusted by being multiplied by some factor such that we can match flow and transport properties of the streamline. This enables the inverse process to achieve fast convergence. In addition, equipped with a stochastic module, the proposed technique supportively calibrates the identified field in a stochastic manner, while incorporating spatial information into the field. This prevents the inverse process from being stuck in local minima and helps search for a globally optimized solution. Simulation results indicate that stochastic streamline calibration identifies an unknown permeability exceptionally quickly. More notably, the identified permeability distribution reflected realistic geological features, which had not been achieved in the original work by Agarwal et al. with the limitations of the large modifications along streamlines for matching production data only. The constructed model by stochastic streamline calibration forecasted transport of plume which was similar to that of a reference model. By this, we can expect the proposed approach to be applied to the construction of an aquifer model and forecasting of the aquifer performances of interest.

Hydrologic and hydraulic flood forecasting constrained by remote sensing data

NASA Astrophysics Data System (ADS)

Li, Y.; Grimaldi, S.; Pauwels, V. R. N.; Walker, J. P.; Wright, A. J.

2017-12-01

Flooding is one of the most destructive natural disasters, resulting in many deaths and billions of dollars of damages each year. An indispensable tool to mitigate the effect of floods is to provide accurate and timely forecasts. An operational flood forecasting system typically consists of a hydrologic model, converting rainfall data into flood volumes entering the river system, and a hydraulic model, converting these flood volumes into water levels and flood extents. Such a system is prone to various sources of uncertainties from the initial conditions, meteorological forcing, topographic data, model parameters and model structure. To reduce those uncertainties, current forecasting systems are typically calibrated and/or updated using ground-based streamflow measurements, and such applications are limited to well-gauged areas. The recent increasing availability of spatially distributed remote sensing (RS) data offers new opportunities to improve flood forecasting skill. Based on an Australian case study, this presentation will discuss the use of 1) RS soil moisture to constrain a hydrologic model, and 2) RS flood extent and level to constrain a hydraulic model.The GRKAL hydrological model is calibrated through a joint calibration scheme using both ground-based streamflow and RS soil moisture observations. A lag-aware data assimilation approach is tested through a set of synthetic experiments to integrate RS soil moisture to constrain the streamflow forecasting in real-time.The hydraulic model is LISFLOOD-FP which solves the 2-dimensional inertial approximation of the Shallow Water Equations. Gauged water level time series and RS-derived flood extent and levels are used to apply a multi-objective calibration protocol. The effectiveness with which each data source or combination of data sources constrained the parameter space will be discussed.

Extrinsic Calibration of Camera and 2D Laser Sensors without Overlap

PubMed Central

Al-Widyan, Khalid

2017-01-01

Extrinsic calibration of a camera and a 2D laser range finder (lidar) sensors is crucial in sensor data fusion applications; for example SLAM algorithms used in mobile robot platforms. The fundamental challenge of extrinsic calibration is when the camera-lidar sensors do not overlap or share the same field of view. In this paper we propose a novel and flexible approach for the extrinsic calibration of a camera-lidar system without overlap, which can be used for robotic platform self-calibration. The approach is based on the robot–world hand–eye calibration (RWHE) problem; proven to have efficient and accurate solutions. First, the system was mapped to the RWHE calibration problem modeled as the linear relationship AX=ZB, where X and Z are unknown calibration matrices. Then, we computed the transformation matrix B, which was the main challenge in the above mapping. The computation is based on reasonable assumptions about geometric structure in the calibration environment. The reliability and accuracy of the proposed approach is compared to a state-of-the-art method in extrinsic 2D lidar to camera calibration. Experimental results from real datasets indicate that the proposed approach provides better results with an L2 norm translational and rotational deviations of 314 mm and 0.12∘ respectively. PMID:29036905

Extrinsic Calibration of Camera and 2D Laser Sensors without Overlap.

PubMed

Ahmad Yousef, Khalil M; Mohd, Bassam J; Al-Widyan, Khalid; Hayajneh, Thaier

2017-10-14

Extrinsic calibration of a camera and a 2D laser range finder (lidar) sensors is crucial in sensor data fusion applications; for example SLAM algorithms used in mobile robot platforms. The fundamental challenge of extrinsic calibration is when the camera-lidar sensors do not overlap or share the same field of view. In this paper we propose a novel and flexible approach for the extrinsic calibration of a camera-lidar system without overlap, which can be used for robotic platform self-calibration. The approach is based on the robot-world hand-eye calibration (RWHE) problem; proven to have efficient and accurate solutions. First, the system was mapped to the RWHE calibration problem modeled as the linear relationship AX = ZB , where X and Z are unknown calibration matrices. Then, we computed the transformation matrix B , which was the main challenge in the above mapping. The computation is based on reasonable assumptions about geometric structure in the calibration environment. The reliability and accuracy of the proposed approach is compared to a state-of-the-art method in extrinsic 2D lidar to camera calibration. Experimental results from real datasets indicate that the proposed approach provides better results with an L2 norm translational and rotational deviations of 314 mm and 0 . 12 ∘ respectively.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tan, Chengming; Yan, Yihua; Tan, Baolin

This work presents a systematic investigation of the influence of weather conditions on the calibration errors by using Gaussian fitness, least chi-square linear fitness, and wavelet transform to analyze the calibration coefficients from observations of the Chinese Solar Broadband Radio Spectrometers (at frequency bands of 1.0–2.0 GHz, 2.6–3.8 GHz, and 5.2–7.6 GHz) during 1997–2007. We found that calibration coefficients are influenced by the local air temperature. Considering the temperature correction, the calibration error will reduce by about 10%–20% at 2800 MHz. Based on the above investigation and the calibration corrections, we further study the radio emission of the quiet Sunmore » by using an appropriate hybrid model of the quiet-Sun atmosphere. The results indicate that the numerical flux of the hybrid model is much closer to the observation flux than that of other ones.« less

[On-orbit response variation analysis of FY-3 MERSI reflective solar bands based on Dunhuang site calibration].

PubMed

Sun, Ling; Guo, Mao-Hua; Xu, Na; Zhang, Li-Jun; Liu, Jing-Jing; Hu, Xiu-Qing; Li, Yuan; Rong, Zhi-Guo; Zhao, Ze-Hui

2012-07-01

MERSI is the keystone payload of FengYun-3 and there have been two sensors operating on-orbit since 2008. The on-orbit response changes obviously at reflective solar bands (RSBs) and must be effectively monitored and corrected. However MERSI can not realize the RSBs onboard absolute radiometric calibration. This paper presents a new vicarious calibration (VC) method for RSBs based on in-situ BRDF model, and vector radiometric transfer model 6SV with gaseous absorption correction using MOTRAN. The results of synchronous VC experiments in 4 years show that the calibration uncertainties are within 5% except for band at the center of water vapor absorption, and 3% for most bands. Aqua MODIS was taken as the radiometric reference to evaluate the accuracy of this VC method. By comparison of the simulated radiation at top of atmosphere (TOA) with MODIS measurement, it was revealed that the average relative differences are within 3% for window bands with wavelengths less than 1 microm, and 5% for bands with wavelengths larger than 1 microm (except for band 7 at 2.1 microm). Besides, the synchronous nadir observation cross analysis shows the excellent agreement between re-calibrated MERSI TOA apparent reflectance and MODIS measurements. Based on the multi-year site calibration results, it was found that the calibration coefficients could be fitted with two-order polynomials, thus the daily calibration updates could be realized and the response variation between two calibration experiments could be corrected timely; there are large response changes at bands with wavelengths less than 0.6 microm, the degradation rate of the first year at band 8 (0.41 microm) is about 14%; the on-orbit response degradation is maximum at the beginning, the degradation rates slow down after one year in operation, and after two years the responses even increase at some band with wavelengths larger than 0.6 microm.

Kinect based real-time position calibration for nasal endoscopic surgical navigation system

NASA Astrophysics Data System (ADS)

Fan, Jingfan; Yang, Jian; Chu, Yakui; Ma, Shaodong; Wang, Yongtian

2016-03-01

Unanticipated, reactive motion of the patient during skull based tumor resective surgery is the source of the consequence that the nasal endoscopic tracking system is compelled to be recalibrated. To accommodate the calibration process with patient's movement, this paper developed a Kinect based Real-time positional calibration method for nasal endoscopic surgical navigation system. In this method, a Kinect scanner was employed as the acquisition part of the point cloud volumetric reconstruction of the patient's head during surgery. Then, a convex hull based registration algorithm aligned the real-time image of the patient head with a model built upon the CT scans performed in the preoperative preparation to dynamically calibrate the tracking system if a movement was detected. Experimental results confirmed the robustness of the proposed method, presenting a total tracking error within 1 mm under the circumstance of relatively violent motions. These results point out the tracking accuracy can be retained stably and the potential to expedite the calibration of the tracking system against strong interfering conditions, demonstrating high suitability for a wide range of surgical applications.

Quick method (FT-NIR) for the determination of oil and major fatty acids content in whole achenes of milk thistle (Silybum marianum (L.) Gaertn.).

PubMed

Koláčková, Pavla; Růžičková, Gabriela; Gregor, Tomáš; Šišperová, Eliška

2015-08-30

Calibration models for the Fourier transform-near infrared (FT-NIR) instrument were developed for quick and non-destructive determination of oil and fatty acids in whole achenes of milk thistle. Samples with a range of oil and fatty acid levels were collected and their transmittance spectra were obtained by the FT-NIR instrument. Based on these spectra and data gained by the means of the reference method - Soxhlet extraction and gas chromatography (GC) - calibration models were created by means of partial least square (PLS) regression analysis. Precision and accuracy of the calibration models was verified via the cross-validation of validation samples whose spectra were not part of the calibration model and also according to the root mean square error of prediction (RMSEP), root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV) and the validation coefficient of determination (R(2) ). R(2) for whole seeds were 0.96, 0.96, 0.83 and 0.67 and the RMSEP values were 0.76, 1.68, 1.24, 0.54 for oil, linoleic (C18:2), oleic (C18:1) and palmitic (C16:0) acids, respectively. The calibration models are appropriate for the non-destructive determination of oil and fatty acids levels in whole seeds of milk thistle. © 2014 Society of Chemical Industry.

Calibration, event reconstruction, data analysis, and limit calculation for the LUX dark matter experiment

NASA Astrophysics Data System (ADS)

Akerib, D. S.; Alsum, S.; Araújo, H. M.; Bai, X.; Bailey, A. J.; Balajthy, J.; Beltrame, P.; Bernard, E. P.; Bernstein, A.; Biesiadzinski, T. P.; Boulton, E. M.; Brás, P.; Byram, D.; Cahn, S. B.; Carmona-Benitez, M. C.; Chan, C.; Currie, A.; Cutter, J. E.; Davison, T. J. R.; Dobi, A.; Dobson, J. E. Y.; Druszkiewicz, E.; Edwards, B. N.; Faham, C. H.; Fallon, S. R.; Fan, A.; Fiorucci, S.; Gaitskell, R. J.; Gehman, V. M.; Genovesi, J.; Ghag, C.; Gilchriese, M. G. D.; Hall, C. R.; Hanhardt, M.; Haselschwardt, S. J.; Hertel, S. A.; Hogan, D. P.; Horn, M.; Huang, D. Q.; Ignarra, C. M.; Jacobsen, R. G.; Ji, W.; Kamdin, K.; Kazkaz, K.; Khaitan, D.; Knoche, R.; Larsen, N. A.; Lee, C.; Lenardo, B. G.; Lesko, K. T.; Lindote, A.; Lopes, M. I.; Manalaysay, A.; Mannino, R. L.; Marzioni, M. F.; McKinsey, D. N.; Mei, D.-M.; Mock, J.; Moongweluwan, M.; Morad, J. A.; Murphy, A. St. J.; Nehrkorn, C.; Nelson, H. N.; Neves, F.; O'Sullivan, K.; Oliver-Mallory, K. C.; Palladino, K. J.; Pease, E. K.; Reichhart, L.; Rhyne, C.; Shaw, S.; Shutt, T. A.; Silva, C.; Solmaz, M.; Solovov, V. N.; Sorensen, P.; Sumner, T. J.; Szydagis, M.; Taylor, D. J.; Taylor, W. C.; Tennyson, B. P.; Terman, P. A.; Tiedt, D. R.; To, W. H.; Tripathi, M.; Tvrznikova, L.; Uvarov, S.; Velan, V.; Verbus, J. R.; Webb, R. C.; White, J. T.; Whitis, T. J.; Witherell, M. S.; Wolfs, F. L. H.; Xu, J.; Yazdani, K.; Young, S. K.; Zhang, C.; LUX Collaboration

2018-05-01

The LUX experiment has performed searches for dark-matter particles scattering elastically on xenon nuclei, leading to stringent upper limits on the nuclear scattering cross sections for dark matter. Here, for results derived from 1.4 ×104 kg days of target exposure in 2013, details of the calibration, event-reconstruction, modeling, and statistical tests that underlie the results are presented. Detector performance is characterized, including measured efficiencies, stability of response, position resolution, and discrimination between electron- and nuclear-recoil populations. Models are developed for the drift field, optical properties, background populations, the electron- and nuclear-recoil responses, and the absolute rate of low-energy background events. Innovations in the analysis include in situ measurement of the photomultipliers' response to xenon scintillation photons, verification of fiducial mass with a low-energy internal calibration source, and new empirical models for low-energy signal yield based on large-sample, in situ calibrations.

HESS Opinions: The need for process-based evaluation of large-domain hyper-resolution models

NASA Astrophysics Data System (ADS)

Melsen, Lieke A.; Teuling, Adriaan J.; Torfs, Paul J. J. F.; Uijlenhoet, Remko; Mizukami, Naoki; Clark, Martyn P.

2016-03-01

A meta-analysis on 192 peer-reviewed articles reporting on applications of the variable infiltration capacity (VIC) model in a distributed way reveals that the spatial resolution at which the model is applied has increased over the years, while the calibration and validation time interval has remained unchanged. We argue that the calibration and validation time interval should keep pace with the increase in spatial resolution in order to resolve the processes that are relevant at the applied spatial resolution. We identified six time concepts in hydrological models, which all impact the model results and conclusions. Process-based model evaluation is particularly relevant when models are applied at hyper-resolution, where stakeholders expect credible results both at a high spatial and temporal resolution.

HESS Opinions: The need for process-based evaluation of large-domain hyper-resolution models

NASA Astrophysics Data System (ADS)

Melsen, L. A.; Teuling, A. J.; Torfs, P. J. J. F.; Uijlenhoet, R.; Mizukami, N.; Clark, M. P.

2015-12-01

A meta-analysis on 192 peer-reviewed articles reporting applications of the Variable Infiltration Capacity (VIC) model in a distributed way reveals that the spatial resolution at which the model is applied has increased over the years, while the calibration and validation time interval has remained unchanged. We argue that the calibration and validation time interval should keep pace with the increase in spatial resolution in order to resolve the processes that are relevant at the applied spatial resolution. We identified six time concepts in hydrological models, which all impact the model results and conclusions. Process-based model evaluation is particularly relevant when models are applied at hyper-resolution, where stakeholders expect credible results both at a high spatial and temporal resolution.

An information theoretic approach to use high-fidelity codes to calibrate low-fidelity codes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lewis, Allison, E-mail: lewis.allison10@gmail.com; Smith, Ralph; Williams, Brian

For many simulation models, it can be prohibitively expensive or physically infeasible to obtain a complete set of experimental data to calibrate model parameters. In such cases, one can alternatively employ validated higher-fidelity codes to generate simulated data, which can be used to calibrate the lower-fidelity code. In this paper, we employ an information-theoretic framework to determine the reduction in parameter uncertainty that is obtained by evaluating the high-fidelity code at a specific set of design conditions. These conditions are chosen sequentially, based on the amount of information that they contribute to the low-fidelity model parameters. The goal is tomore » employ Bayesian experimental design techniques to minimize the number of high-fidelity code evaluations required to accurately calibrate the low-fidelity model. We illustrate the performance of this framework using heat and diffusion examples, a 1-D kinetic neutron diffusion equation, and a particle transport model, and include initial results from the integration of the high-fidelity thermal-hydraulics code Hydra-TH with a low-fidelity exponential model for the friction correlation factor.« less

Procedures for adjusting regional regression models of urban-runoff quality using local data

USGS Publications Warehouse

Hoos, A.B.; Sisolak, J.K.

1993-01-01

Statistical operations termed model-adjustment procedures (MAP?s) can be used to incorporate local data into existing regression models to improve the prediction of urban-runoff quality. Each MAP is a form of regression analysis in which the local data base is used as a calibration data set. Regression coefficients are determined from the local data base, and the resulting `adjusted? regression models can then be used to predict storm-runoff quality at unmonitored sites. The response variable in the regression analyses is the observed load or mean concentration of a constituent in storm runoff for a single storm. The set of explanatory variables used in the regression analyses is different for each MAP, but always includes the predicted value of load or mean concentration from a regional regression model. The four MAP?s examined in this study were: single-factor regression against the regional model prediction, P, (termed MAP-lF-P), regression against P,, (termed MAP-R-P), regression against P, and additional local variables (termed MAP-R-P+nV), and a weighted combination of P, and a local-regression prediction (termed MAP-W). The procedures were tested by means of split-sample analysis, using data from three cities included in the Nationwide Urban Runoff Program: Denver, Colorado; Bellevue, Washington; and Knoxville, Tennessee. The MAP that provided the greatest predictive accuracy for the verification data set differed among the three test data bases and among model types (MAP-W for Denver and Knoxville, MAP-lF-P and MAP-R-P for Bellevue load models, and MAP-R-P+nV for Bellevue concentration models) and, in many cases, was not clearly indicated by the values of standard error of estimate for the calibration data set. A scheme to guide MAP selection, based on exploratory data analysis of the calibration data set, is presented and tested. The MAP?s were tested for sensitivity to the size of a calibration data set. As expected, predictive accuracy of all MAP?s for the verification data set decreased as the calibration data-set size decreased, but predictive accuracy was not as sensitive for the MAP?s as it was for the local regression models.

A Multi-Purpose, Detector-Based Photometric Calibration System for Luminous Intensity, Illuminance and Luminance

NASA Astrophysics Data System (ADS)

Lam, Brenda H. S.; Yang, Steven S. L.; Chau, Y. C.

2018-02-01

A multi-purpose detector based calibration system for luminous intensity, illuminance and luminance has been developed at the Government of the Hong Kong Special Administrative Region, Standards and Calibration Laboratory (SCL). In this paper, the measurement system and methods are described. The measurement models and contributory uncertainties were validated using the Guide to the Expression of Uncertainty in Measurement (GUM) framework and Supplement 1 to the GUM - Propagation of distributions using a Monte Carlo method in accordance with the JCGM 100:2008 and JCGM 101:2008 at the intended precision level.

Improving ROLO lunar albedo model using PLEIADES-HR satellites extra-terrestrial observations

NASA Astrophysics Data System (ADS)

Meygret, Aimé; Blanchet, Gwendoline; Colzy, Stéphane; Gross-Colzy, Lydwine

2017-09-01

The accurate on orbit radiometric calibration of optical sensors has become a challenge for space agencies which have developed different technics involving on-board calibration systems, ground targets or extra-terrestrial targets. The combination of different approaches and targets is recommended whenever possible and necessary to reach or demonstrate a high accuracy. Among these calibration targets, the moon is widely used through the well-known ROLO (RObotic Lunar Observatory) model developed by USGS. A great and worldwide recognized work was done to characterize the moon albedo which is very stable. However the more and more demanding needs for calibration accuracy have reached the limitations of the model. This paper deals with two mains limitations: the residual error when modelling the phase angle dependency and the absolute accuracy of the model which is no more acceptable for the on orbit calibration of radiometers. Thanks to PLEIADES high resolution satellites agility, a significant data base of moon and stars images was acquired, allowing to show the limitations of ROLO model and to characterize the errors. The phase angle residual dependency is modelled using PLEIADES 1B images acquired for different quasi-complete moon cycles with a phase angle varying by less than 1°. The absolute albedo residual error is modelled using PLEIADES 1A images taken over stars and the moon. The accurate knowledge of the stars spectral irradiance is transferred to the moon spectral albedo using the satellite as a transfer radiometer. This paper describes the data set used, the ROLO model residual errors and their modelling, the quality of the proposed correction and show some calibration results using this improved model.

A PHYSIOLOGICALLY BASED PHARMACOKINETIC (PBPK) MODEL FOR intravenous and ingested DIMETHYLARSINIC ACID (DMAV) IN MICE.

EPA Science Inventory

A physiologically based pharmacokinetic (PBPK) model for the organoarsenical dimethylarsinic acid (DMA(V)) was developed in mice. The model was calibrated using tissue time course data from multiple tissues in mice administered DMA(V) intravenously. The final model structure was ...

Evaluation and Enhancement of Calibration in the American College of Surgeons NSQIP Surgical Risk Calculator.

PubMed

Liu, Yaoming; Cohen, Mark E; Hall, Bruce L; Ko, Clifford Y; Bilimoria, Karl Y

2016-08-01

The American College of Surgeon (ACS) NSQIP Surgical Risk Calculator has been widely adopted as a decision aid and informed consent tool by surgeons and patients. Previous evaluations showed excellent discrimination and combined discrimination and calibration, but model calibration alone, and potential benefits of recalibration, were not explored. Because lack of calibration can lead to systematic errors in assessing surgical risk, our objective was to assess calibration and determine whether spline-based adjustments could improve it. We evaluated Surgical Risk Calculator model calibration, as well as discrimination, for each of 11 outcomes modeled from nearly 3 million patients (2010 to 2014). Using independent random subsets of data, we evaluated model performance for the Development (60% of records), Validation (20%), and Test (20%) datasets, where prediction equations from the Development dataset were recalibrated using restricted cubic splines estimated from the Validation dataset. We also evaluated performance on data subsets composed of higher-risk operations. The nonrecalibrated Surgical Risk Calculator performed well, but there was a slight tendency for predicted risk to be overestimated for lowest- and highest-risk patients and underestimated for moderate-risk patients. After recalibration, this distortion was eliminated, and p values for miscalibration were most often nonsignificant. Calibration was also excellent for subsets of higher-risk operations, though observed calibration was reduced due to instability associated with smaller sample sizes. Performance of NSQIP Surgical Risk Calculator models was shown to be excellent and improved with recalibration. Surgeons and patients can rely on the calculator to provide accurate estimates of surgical risk. Copyright © 2016 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Using HEC-HMS: Application to Karkheh river basin

USDA-ARS?s Scientific Manuscript database

This paper aims to facilitate the use of HEC-HMS model using a systematic event-based technique for manual calibration of soil moisture accounting and snowmelt degree-day parameters. Manual calibration, which helps ensure the HEC-HMS parameter values are physically-relevant, is often a time-consumin...

Calibration of groundwater vulnerability mapping using the generalized reduced gradient method.

PubMed

Elçi, Alper

2017-12-01

Groundwater vulnerability assessment studies are essential in water resources management. Overlay-and-index methods such as DRASTIC are widely used for mapping of groundwater vulnerability, however, these methods mainly suffer from a subjective selection of model parameters. The objective of this study is to introduce a calibration procedure that results in a more accurate assessment of groundwater vulnerability. The improvement of the assessment is formulated as a parameter optimization problem using an objective function that is based on the correlation between actual groundwater contamination and vulnerability index values. The non-linear optimization problem is solved with the generalized-reduced-gradient (GRG) method, which is numerical algorithm based optimization method. To demonstrate the applicability of the procedure, a vulnerability map for the Tahtali stream basin is calibrated using nitrate concentration data. The calibration procedure is easy to implement and aims the maximization of correlation between observed pollutant concentrations and groundwater vulnerability index values. The influence of each vulnerability parameter in the calculation of the vulnerability index is assessed by performing a single-parameter sensitivity analysis. Results of the sensitivity analysis show that all factors are effective on the final vulnerability index. Calibration of the vulnerability map improves the correlation between index values and measured nitrate concentrations by 19%. The regression coefficient increases from 0.280 to 0.485. It is evident that the spatial distribution and the proportions of vulnerability class areas are significantly altered with the calibration process. Although the applicability of the calibration method is demonstrated on the DRASTIC model, the applicability of the approach is not specific to a certain model and can also be easily applied to other overlay-and-index methods. Copyright © 2017 Elsevier B.V. All rights reserved.

Calibration of groundwater vulnerability mapping using the generalized reduced gradient method

NASA Astrophysics Data System (ADS)

Elçi, Alper

2017-12-01

Groundwater vulnerability assessment studies are essential in water resources management. Overlay-and-index methods such as DRASTIC are widely used for mapping of groundwater vulnerability, however, these methods mainly suffer from a subjective selection of model parameters. The objective of this study is to introduce a calibration procedure that results in a more accurate assessment of groundwater vulnerability. The improvement of the assessment is formulated as a parameter optimization problem using an objective function that is based on the correlation between actual groundwater contamination and vulnerability index values. The non-linear optimization problem is solved with the generalized-reduced-gradient (GRG) method, which is numerical algorithm based optimization method. To demonstrate the applicability of the procedure, a vulnerability map for the Tahtali stream basin is calibrated using nitrate concentration data. The calibration procedure is easy to implement and aims the maximization of correlation between observed pollutant concentrations and groundwater vulnerability index values. The influence of each vulnerability parameter in the calculation of the vulnerability index is assessed by performing a single-parameter sensitivity analysis. Results of the sensitivity analysis show that all factors are effective on the final vulnerability index. Calibration of the vulnerability map improves the correlation between index values and measured nitrate concentrations by 19%. The regression coefficient increases from 0.280 to 0.485. It is evident that the spatial distribution and the proportions of vulnerability class areas are significantly altered with the calibration process. Although the applicability of the calibration method is demonstrated on the DRASTIC model, the applicability of the approach is not specific to a certain model and can also be easily applied to other overlay-and-index methods.

Examination of simplified travel demand model. [Internal volume forecasting model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, R.L. Jr.; McFarlane, W.J.

1978-01-01

A simplified travel demand model, the Internal Volume Forecasting (IVF) model, proposed by Low in 1972 is evaluated as an alternative to the conventional urban travel demand modeling process. The calibration of the IVF model for a county-level study area in Central Wisconsin results in what appears to be a reasonable model; however, analysis of the structure of the model reveals two primary mis-specifications. Correction of the mis-specifications leads to a simplified gravity model version of the conventional urban travel demand models. Application of the original IVF model to ''forecast'' 1960 traffic volumes based on the model calibrated for 1970more » produces accurate estimates. Shortcut and ad hoc models may appear to provide reasonable results in both the base and horizon years; however, as shown by the IVF mode, such models will not always provide a reliable basis for transportation planning and investment decisions.« less

Efficient Reduction and Analysis of Model Predictive Error

NASA Astrophysics Data System (ADS)

Doherty, J.

2006-12-01

Most groundwater models are calibrated against historical measurements of head and other system states before being used to make predictions in a real-world context. Through the calibration process, parameter values are estimated or refined such that the model is able to reproduce historical behaviour of the system at pertinent observation points reasonably well. Predictions made by the model are deemed to have greater integrity because of this. Unfortunately, predictive integrity is not as easy to achieve as many groundwater practitioners would like to think. The level of parameterisation detail estimable through the calibration process (especially where estimation takes place on the basis of heads alone) is strictly limited, even where full use is made of modern mathematical regularisation techniques such as those encapsulated in the PEST calibration package. (Use of these mechanisms allows more information to be extracted from a calibration dataset than is possible using simpler regularisation devices such as zones of piecewise constancy.) Where a prediction depends on aspects of parameterisation detail that are simply not inferable through the calibration process (which is often the case for predictions related to contaminant movement, and/or many aspects of groundwater/surface water interaction), then that prediction may be just as much in error as it would have been if the model had not been calibrated at all. Model predictive error arises from two sources. These are (a) the presence of measurement noise within the calibration dataset through which linear combinations of parameters spanning the "calibration solution space" are inferred, and (b) the sensitivity of the prediction to members of the "calibration null space" spanned by linear combinations of parameters which are not inferable through the calibration process. The magnitude of the former contribution depends on the level of measurement noise. The magnitude of the latter contribution (which often dominates the former) depends on the "innate variability" of hydraulic properties within the model domain. Knowledge of both of these is a prerequisite for characterisation of the magnitude of possible model predictive error. Unfortunately, in most cases, such knowledge is incomplete and subjective. Nevertheless, useful analysis of model predictive error can still take place. The present paper briefly discusses the means by which mathematical regularisation can be employed in the model calibration process in order to extract as much information as possible on hydraulic property heterogeneity prevailing within the model domain, thereby reducing predictive error to the lowest that can be achieved on the basis of that dataset. It then demonstrates the means by which predictive error variance can be quantified based on information supplied by the regularised inversion process. Both linear and nonlinear predictive error variance analysis is demonstrated using a number of real-world and synthetic examples.

Wavelength selection-based nonlinear calibration for transcutaneous blood glucose sensing using Raman spectroscopy

PubMed Central

Dingari, Narahara Chari; Barman, Ishan; Kang, Jeon Woong; Kong, Chae-Ryon; Dasari, Ramachandra R.; Feld, Michael S.

2011-01-01

While Raman spectroscopy provides a powerful tool for noninvasive and real time diagnostics of biological samples, its translation to the clinical setting has been impeded by the lack of robustness of spectroscopic calibration models and the size and cumbersome nature of conventional laboratory Raman systems. Linear multivariate calibration models employing full spectrum analysis are often misled by spurious correlations, such as system drift and covariations among constituents. In addition, such calibration schemes are prone to overfitting, especially in the presence of external interferences that may create nonlinearities in the spectra-concentration relationship. To address both of these issues we incorporate residue error plot-based wavelength selection and nonlinear support vector regression (SVR). Wavelength selection is used to eliminate uninformative regions of the spectrum, while SVR is used to model the curved effects such as those created by tissue turbidity and temperature fluctuations. Using glucose detection in tissue phantoms as a representative example, we show that even a substantial reduction in the number of wavelengths analyzed using SVR lead to calibration models of equivalent prediction accuracy as linear full spectrum analysis. Further, with clinical datasets obtained from human subject studies, we also demonstrate the prospective applicability of the selected wavelength subsets without sacrificing prediction accuracy, which has extensive implications for calibration maintenance and transfer. Additionally, such wavelength selection could substantially reduce the collection time of serial Raman acquisition systems. Given the reduced footprint of serial Raman systems in relation to conventional dispersive Raman spectrometers, we anticipate that the incorporation of wavelength selection in such hardware designs will enhance the possibility of miniaturized clinical systems for disease diagnosis in the near future. PMID:21895336

The benefit of 3D laser scanning technology in the generation and calibration of FEM models for health assessment of concrete structures.

PubMed

Yang, Hao; Xu, Xiangyang; Neumann, Ingo

2014-11-19

Terrestrial laser scanning technology (TLS) is a new technique for quickly getting three-dimensional information. In this paper we research the health assessment of concrete structures with a Finite Element Method (FEM) model based on TLS. The goal focuses on the benefits of 3D TLS in the generation and calibration of FEM models, in order to build a convenient, efficient and intelligent model which can be widely used for the detection and assessment of bridges, buildings, subways and other objects. After comparing the finite element simulation with surface-based measurement data from TLS, the FEM model is determined to be acceptable with an error of less than 5%. The benefit of TLS lies mainly in the possibility of a surface-based validation of results predicted by the FEM model.

Comparison between phenomenological and ab-initio reaction and relaxation models in DSMC

NASA Astrophysics Data System (ADS)

Sebastião, Israel B.; Kulakhmetov, Marat; Alexeenko, Alina

2016-11-01

New state-specific vibrational-translational energy exchange and dissociation models, based on ab-initio data, are implemented in direct simulation Monte Carlo (DSMC) method and compared to the established Larsen-Borgnakke (LB) and total collision energy (TCE) phenomenological models. For consistency, both the LB and TCE models are calibrated with QCT-calculated O2+O data. The model comparison test cases include 0-D thermochemical relaxation under adiabatic conditions and 1-D normal shockwave calculations. The results show that both the ME-QCT-VT and LB models can reproduce vibrational relaxation accurately but the TCE model is unable to reproduce nonequilibrium rates even when it is calibrated to accurate equilibrium rates. The new reaction model does capture QCT-calculated nonequilibrium rates. For all investigated cases, we discuss the prediction differences based on the new model features.

Precision process calibration and CD predictions for low-k1 lithography

NASA Astrophysics Data System (ADS)

Chen, Ting; Park, Sangbong; Berger, Gabriel; Coskun, Tamer H.; de Vocht, Joep; Chen, Fung; Yu, Linda; Hsu, Stephen; van den Broeke, Doug; Socha, Robert; Park, Jungchul; Gronlund, Keith; Davis, Todd; Plachecki, Vince; Harris, Tom; Hansen, Steve; Lambson, Chuck

2005-06-01

Leading resist calibration for sub-0.3 k1 lithography demands accuracy <2nm for CD through pitch. An accurately calibrated resist process is the prerequisite for establishing production-worthy manufacturing under extreme low k1. From an integrated imaging point of view, the following key components must be simultaneously considered during the calibration - high numerical aperture (NA>0.8) imaging characteristics, customized illuminations (measured vs. modeled pupil profiles), resolution enhancement technology (RET) mask with OPC, reticle metrology, and resist thin film substrate. For imaging at NA approaching unity, polarized illumination can impact significantly the contrast formation in the resist film stack, and therefore it is an important factor to consider in the CD-based resist calibration. For aggressive DRAM memory core designs at k1<0.3, pattern-specific illumination optimization has proven to be critical for achieving the required imaging performance. Various optimization techniques from source profile optimization with fixed mask design to the combined source and mask optimization have been considered for customer designs and available imaging capabilities. For successful low-k1 process development, verification of the optimization results can only be made with a sufficiently tunable resist model that can predicate the wafer printing accurately under various optimized process settings. We have developed, for resist patterning under aggressive low-k1 conditions, a novel 3D diffusion model equipped with double-Gaussian convolution in each dimension. Resist calibration with the new diffusion model has demonstrated a fitness and CD predication accuracy that rival or outperform the traditional 3D physical resist models. In this work, we describe our empirical approach to achieving the nm-scale precision for advanced lithography process calibrations, using either measured 1D CD through-pitch or 2D memory core patterns. We show that for ArF imaging, the current resist development and diffusion modeling can readily achieve ~1-2nm max CD errors for common 1D through-pitch and aggressive 2D memory core resist patterns. Sensitivities of the calibrated models to various process parameters are analyzed, including the comparison between the measured and modeled (Gaussian or GRAIL) pupil profiles. We also report our preliminary calibration results under selected polarized illumination conditions.

InGaAs tunnel diodes for the calibration of semi-classical and quantum mechanical band-to-band tunneling models

NASA Astrophysics Data System (ADS)

Smets, Quentin; Verreck, Devin; Verhulst, Anne S.; Rooyackers, Rita; Merckling, Clément; Van De Put, Maarten; Simoen, Eddy; Vandervorst, Wilfried; Collaert, Nadine; Thean, Voon Y.; Sorée, Bart; Groeseneken, Guido; Heyns, Marc M.

2014-05-01

Promising predictions are made for III-V tunnel-field-effect transistor (FET), but there is still uncertainty on the parameters used in the band-to-band tunneling models. Therefore, two simulators are calibrated in this paper; the first one uses a semi-classical tunneling model based on Kane's formalism, and the second one is a quantum mechanical simulator implemented with an envelope function formalism. The calibration is done for In0.53Ga0.47As using several p+/intrinsic/n+ diodes with different intrinsic region thicknesses. The dopant profile is determined by SIMS and capacitance-voltage measurements. Error bars are used based on statistical and systematic uncertainties in the measurement techniques. The obtained parameters are in close agreement with theoretically predicted values and validate the semi-classical and quantum mechanical models. Finally, the models are applied to predict the input characteristics of In0.53Ga0.47As n- and p-lineTFET, with the n-lineTFET showing competitive performance compared to MOSFET.

Sensitivity of planetary cruise navigation to earth orientation calibration errors

NASA Technical Reports Server (NTRS)

Estefan, J. A.; Folkner, W. M.

1995-01-01

A detailed analysis was conducted to determine the sensitivity of spacecraft navigation errors to the accuracy and timeliness of Earth orientation calibrations. Analyses based on simulated X-band (8.4-GHz) Doppler and ranging measurements acquired during the interplanetary cruise segment of the Mars Pathfinder heliocentric trajectory were completed for the nominal trajectory design and for an alternative trajectory with a longer transit time. Several error models were developed to characterize the effect of Earth orientation on navigational accuracy based on current and anticipated Deep Space Network calibration strategies. The navigational sensitivity of Mars Pathfinder to calibration errors in Earth orientation was computed for each candidate calibration strategy with the Earth orientation parameters included as estimated parameters in the navigation solution. In these cases, the calibration errors contributed 23 to 58% of the total navigation error budget, depending on the calibration strategy being assessed. Navigation sensitivity calculations were also performed for cases in which Earth orientation calibration errors were not adjusted in the navigation solution. In these cases, Earth orientation calibration errors contributed from 26 to as much as 227% of the total navigation error budget. The final analysis suggests that, not only is the method used to calibrate Earth orientation vitally important for precision navigation of Mars Pathfinder, but perhaps equally important is the method for inclusion of the calibration errors in the navigation solutions.

Software Tools For Building Decision-support Models For Flood Emergency Situations

NASA Astrophysics Data System (ADS)

Garrote, L.; Molina, M.; Ruiz, J. M.; Mosquera, J. C.

The SAIDA decision-support system was developed by the Spanish Ministry of the Environment to provide assistance to decision-makers during flood situations. SAIDA has been tentatively implemented in two test basins: Jucar and Guadalhorce, and the Ministry is currently planning to have it implemented in all major Spanish basins in a few years' time. During the development cycle of SAIDA, the need for providing as- sistance to end-users in model definition and calibration was clearly identified. System developers usually emphasise abstraction and generality with the goal of providing a versatile software environment. End users, on the other hand, require concretion and specificity to adapt the general model to their local basins. As decision-support models become more complex, the gap between model developers and users gets wider: Who takes care of model definition, calibration and validation?. Initially, model developers perform these tasks, but the scope is usually limited to a few small test basins. Before the model enters operational stage, end users must get involved in model construction and calibration, in order to gain confidence in the model recommendations. However, getting the users involved in these activities is a difficult task. The goal of this re- search is to develop representation techniques for simulation and management models in order to define, develop and validate a mechanism, supported by a software envi- ronment, oriented to provide assistance to the end-user in building decision models for the prediction and management of river floods in real time. The system is based on three main building blocks: A library of simulators of the physical system, an editor to assist the user in building simulation models, and a machine learning method to calibrate decision models based on the simulation models provided by the user.

Effect of soil property uncertainties on permafrost thaw projections: a calibration-constrained analysis: Modeling Archive

DOE Data Explorer

J.C. Rowland; D.R. Harp; C.J. Wilson; A.L. Atchley; V.E. Romanovsky; E.T. Coon; S.L. Painter

2016-02-02

This Modeling Archive is in support of an NGEE Arctic publication available at doi:10.5194/tc-10-341-2016. This dataset contains an ensemble of thermal-hydro soil parameters including porosity, thermal conductivity, thermal conductivity shape parameters, and residual saturation of peat and mineral soil. The ensemble was generated using a Null-Space Monte Carlo analysis of parameter uncertainty based on a calibration to soil temperatures collected at the Barrow Environmental Observatory site by the NGEE team. The micro-topography of ice wedge polygons present at the site is included in the analysis using three 1D column models to represent polygon center, rim and trough features. The Arctic Terrestrial Simulator (ATS) was used in the calibration to model multiphase thermal and hydrological processes in the subsurface.

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study.

PubMed

Attia, Khalid A M; Nassar, Mohammed W I; El-Zeiny, Mohamed B; Serag, Ahmed

2017-01-05

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration. Copyright © 2016 Elsevier B.V. All rights reserved.

An Improved Fast Self-Calibration Method for Hybrid Inertial Navigation System under Stationary Condition

PubMed Central

Liu, Bingqi; Wei, Shihui; Su, Guohua; Wang, Jiping; Lu, Jiazhen

2018-01-01

The navigation accuracy of the inertial navigation system (INS) can be greatly improved when the inertial measurement unit (IMU) is effectively calibrated and compensated, such as gyro drifts and accelerometer biases. To reduce the requirement for turntable precision in the classical calibration method, a continuous dynamic self-calibration method based on a three-axis rotating frame for the hybrid inertial navigation system is presented. First, by selecting a suitable IMU frame, the error models of accelerometers and gyros are established. Then, by taking the navigation errors during rolling as the observations, the overall twenty-one error parameters of hybrid inertial navigation system (HINS) are identified based on the calculation of the intermediate parameter. The actual experiment verifies that the method can identify all error parameters of HINS and this method has equivalent accuracy to the classical calibration on a high-precision turntable. In addition, this method is rapid, simple and feasible. PMID:29695041

An Improved Fast Self-Calibration Method for Hybrid Inertial Navigation System under Stationary Condition.

PubMed

Liu, Bingqi; Wei, Shihui; Su, Guohua; Wang, Jiping; Lu, Jiazhen

2018-04-24

The navigation accuracy of the inertial navigation system (INS) can be greatly improved when the inertial measurement unit (IMU) is effectively calibrated and compensated, such as gyro drifts and accelerometer biases. To reduce the requirement for turntable precision in the classical calibration method, a continuous dynamic self-calibration method based on a three-axis rotating frame for the hybrid inertial navigation system is presented. First, by selecting a suitable IMU frame, the error models of accelerometers and gyros are established. Then, by taking the navigation errors during rolling as the observations, the overall twenty-one error parameters of hybrid inertial navigation system (HINS) are identified based on the calculation of the intermediate parameter. The actual experiment verifies that the method can identify all error parameters of HINS and this method has equivalent accuracy to the classical calibration on a high-precision turntable. In addition, this method is rapid, simple and feasible.

Preliminary research on eddy current bobbin quantitative test for heat exchange tube in nuclear power plant

NASA Astrophysics Data System (ADS)

Qi, Pan; Shao, Wenbin; Liao, Shusheng

2016-02-01

For quantitative defects detection research on heat transfer tube in nuclear power plants (NPP), two parts of work are carried out based on the crack as the main research objects. (1) Production optimization of calibration tube. Firstly, ASME, RSEM and homemade crack calibration tubes are applied to quantitatively analyze the defects depth on other designed crack test tubes, and then the judgment with quantitative results under crack calibration tube with more accuracy is given. Base on that, weight analysis of influence factors for crack depth quantitative test such as crack orientation, length, volume and so on can be undertaken, which will optimize manufacture technology of calibration tubes. (2) Quantitative optimization of crack depth. Neural network model with multi-calibration curve adopted to optimize natural crack test depth generated in in-service tubes shows preliminary ability to improve quantitative accuracy.

Automating calibration, sensitivity and uncertainty analysis of complex models using the R package Flexible Modeling Environment (FME): SWAT as an example

USGS Publications Warehouse

Wu, Y.; Liu, S.

2012-01-01

Parameter optimization and uncertainty issues are a great challenge for the application of large environmental models like the Soil and Water Assessment Tool (SWAT), which is a physically-based hydrological model for simulating water and nutrient cycles at the watershed scale. In this study, we present a comprehensive modeling environment for SWAT, including automated calibration, and sensitivity and uncertainty analysis capabilities through integration with the R package Flexible Modeling Environment (FME). To address challenges (e.g., calling the model in R and transferring variables between Fortran and R) in developing such a two-language coupling framework, 1) we converted the Fortran-based SWAT model to an R function (R-SWAT) using the RFortran platform, and alternatively 2) we compiled SWAT as a Dynamic Link Library (DLL). We then wrapped SWAT (via R-SWAT) with FME to perform complex applications including parameter identifiability, inverse modeling, and sensitivity and uncertainty analysis in the R environment. The final R-SWAT-FME framework has the following key functionalities: automatic initialization of R, running Fortran-based SWAT and R commands in parallel, transferring parameters and model output between SWAT and R, and inverse modeling with visualization. To examine this framework and demonstrate how it works, a case study simulating streamflow in the Cedar River Basin in Iowa in the United Sates was used, and we compared it with the built-in auto-calibration tool of SWAT in parameter optimization. Results indicate that both methods performed well and similarly in searching a set of optimal parameters. Nonetheless, the R-SWAT-FME is more attractive due to its instant visualization, and potential to take advantage of other R packages (e.g., inverse modeling and statistical graphics). The methods presented in the paper are readily adaptable to other model applications that require capability for automated calibration, and sensitivity and uncertainty analysis.

Early Prediction of Intensive Care Unit-Acquired Weakness: A Multicenter External Validation Study.

PubMed

Witteveen, Esther; Wieske, Luuk; Sommers, Juultje; Spijkstra, Jan-Jaap; de Waard, Monique C; Endeman, Henrik; Rijkenberg, Saskia; de Ruijter, Wouter; Sleeswijk, Mengalvio; Verhamme, Camiel; Schultz, Marcus J; van Schaik, Ivo N; Horn, Janneke

2018-01-01

An early diagnosis of intensive care unit-acquired weakness (ICU-AW) is often not possible due to impaired consciousness. To avoid a diagnostic delay, we previously developed a prediction model, based on single-center data from 212 patients (development cohort), to predict ICU-AW at 2 days after ICU admission. The objective of this study was to investigate the external validity of the original prediction model in a new, multicenter cohort and, if necessary, to update the model. Newly admitted ICU patients who were mechanically ventilated at 48 hours after ICU admission were included. Predictors were prospectively recorded, and the outcome ICU-AW was defined by an average Medical Research Council score <4. In the validation cohort, consisting of 349 patients, we analyzed performance of the original prediction model by assessment of calibration and discrimination. Additionally, we updated the model in this validation cohort. Finally, we evaluated a new prediction model based on all patients of the development and validation cohort. Of 349 analyzed patients in the validation cohort, 190 (54%) developed ICU-AW. Both model calibration and discrimination of the original model were poor in the validation cohort. The area under the receiver operating characteristics curve (AUC-ROC) was 0.60 (95% confidence interval [CI]: 0.54-0.66). Model updating methods improved calibration but not discrimination. The new prediction model, based on all patients of the development and validation cohort (total of 536 patients) had a fair discrimination, AUC-ROC: 0.70 (95% CI: 0.66-0.75). The previously developed prediction model for ICU-AW showed poor performance in a new independent multicenter validation cohort. Model updating methods improved calibration but not discrimination. The newly derived prediction model showed fair discrimination. This indicates that early prediction of ICU-AW is still challenging and needs further attention.

Estimating daily time series of streamflow using hydrological model calibrated based on satellite observations of river water surface width: Toward real world applications.

PubMed

Sun, Wenchao; Ishidaira, Hiroshi; Bastola, Satish; Yu, Jingshan

2015-05-01

Lacking observation data for calibration constrains applications of hydrological models to estimate daily time series of streamflow. Recent improvements in remote sensing enable detection of river water-surface width from satellite observations, making possible the tracking of streamflow from space. In this study, a method calibrating hydrological models using river width derived from remote sensing is demonstrated through application to the ungauged Irrawaddy Basin in Myanmar. Generalized likelihood uncertainty estimation (GLUE) is selected as a tool for automatic calibration and uncertainty analysis. Of 50,000 randomly generated parameter sets, 997 are identified as behavioral, based on comparing model simulation with satellite observations. The uncertainty band of streamflow simulation can span most of 10-year average monthly observed streamflow for moderate and high flow conditions. Nash-Sutcliffe efficiency is 95.7% for the simulated streamflow at the 50% quantile. These results indicate that application to the target basin is generally successful. Beyond evaluating the method in a basin lacking streamflow data, difficulties and possible solutions for applications in the real world are addressed to promote future use of the proposed method in more ungauged basins. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Global-scale regionalization of hydrological model parameters using streamflow data from many small catchments

NASA Astrophysics Data System (ADS)

Beck, Hylke; de Roo, Ad; van Dijk, Albert; McVicar, Tim; Miralles, Diego; Schellekens, Jaap; Bruijnzeel, Sampurno; de Jeu, Richard

2015-04-01

Motivated by the lack of large-scale model parameter regionalization studies, a large set of 3328 small catchments (< 10000 km2) around the globe was used to set up and evaluate five model parameterization schemes at global scale. The HBV-light model was chosen because of its parsimony and flexibility to test the schemes. The catchments were calibrated against observed streamflow (Q) using an objective function incorporating both behavioral and goodness-of-fit measures, after which the catchment set was split into subsets of 1215 donor and 2113 evaluation catchments based on the calibration performance. The donor catchments were subsequently used to derive parameter sets that were transferred to similar grid cells based on a similarity measure incorporating climatic and physiographic characteristics, thereby producing parameter maps with global coverage. Overall, there was a lack of suitable donor catchments for mountainous and tropical environments. The schemes with spatially-uniform parameter sets (EXP2 and EXP3) achieved the worst Q estimation performance in the evaluation catchments, emphasizing the importance of parameter regionalization. The direct transfer of calibrated parameter sets from donor catchments to similar grid cells (scheme EXP1) performed best, although there was still a large performance gap between EXP1 and HBV-light calibrated against observed Q. The schemes with parameter sets obtained by simultaneously calibrating clusters of similar donor catchments (NC10 and NC58) performed worse than EXP1. The relatively poor Q estimation performance achieved by two (uncalibrated) macro-scale hydrological models suggests there is considerable merit in regionalizing the parameters of such models. The global HBV-light parameter maps and ancillary data are freely available via http://water.jrc.ec.europa.eu.

A Synthesis of VIIRS Solar and Lunar Calibrations

NASA Technical Reports Server (NTRS)

Eplee, Robert E.; Turpie, Kevin R.; Meister, Gerhard; Patt, Frederick S.; Fireman, Gwyn F.; Franz, Bryan A.; McClain, Charles R.

2013-01-01

The NASA VIIRS Ocean Science Team (VOST) has developed two independent calibrations of the SNPP VIIRS moderate resolution reflective solar bands using solar diffuser and lunar observations through June 2013. Fits to the solar calibration time series show mean residuals per band of 0.078-0.10%. There are apparent residual lunar libration correlations in the lunar calibration time series that are not accounted for by the ROLO photometric model of the Moon. Fits to the lunar time series that account for residual librations show mean residuals per band of 0.071-0.17%. Comparison of the solar and lunar time series shows that the relative differences in the two calibrations are 0.12-0.31%. Relative uncertainties in the VIIRS solar and lunar calibration time series are comparable to those achieved for SeaWiFS, Aqua MODIS, and Terra MODIS. Intercomparison of the VIIRS lunar time series with those from SeaWiFS, Aqua MODIS, and Terra MODIS shows that the scatter in the VIIRS lunar observations is consistent with that observed for the heritage instruments. Based on these analyses, the VOST has derived a calibration lookup table for VIIRS ocean color data based on fits to the solar calibration time series.

Model calibration and validation for OFMSW and sewage sludge co-digestion reactors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Esposito, G., E-mail: giovanni.esposito@unicas.it; Frunzo, L., E-mail: luigi.frunzo@unina.it; Panico, A., E-mail: anpanico@unina.it

2011-12-15

Highlights: > Disintegration is the limiting step of the anaerobic co-digestion process. > Disintegration kinetic constant does not depend on the waste particle size. > Disintegration kinetic constant depends only on the waste nature and composition. > The model calibration can be performed on organic waste of any particle size. - Abstract: A mathematical model has recently been proposed by the authors to simulate the biochemical processes that prevail in a co-digestion reactor fed with sewage sludge and the organic fraction of municipal solid waste. This model is based on the Anaerobic Digestion Model no. 1 of the International Watermore » Association, which has been extended to include the co-digestion processes, using surface-based kinetics to model the organic waste disintegration and conversion to carbohydrates, proteins and lipids. When organic waste solids are present in the reactor influent, the disintegration process is the rate-limiting step of the overall co-digestion process. The main advantage of the proposed modeling approach is that the kinetic constant of such a process does not depend on the waste particle size distribution (PSD) and rather depends only on the nature and composition of the waste particles. The model calibration aimed to assess the kinetic constant of the disintegration process can therefore be conducted using organic waste samples of any PSD, and the resulting value will be suitable for all the organic wastes of the same nature as the investigated samples, independently of their PSD. This assumption was proven in this study by biomethane potential experiments that were conducted on organic waste samples with different particle sizes. The results of these experiments were used to calibrate and validate the mathematical model, resulting in a good agreement between the simulated and observed data for any investigated particle size of the solid waste. This study confirms the strength of the proposed model and calibration procedure, which can thus be used to assess the treatment efficiency and predict the methane production of full-scale digesters.« less

[Study on the characteristics of radiance calibration using nonuniformity extended source].

PubMed

Wang, Jian-Wei; Huang, Min; Xiangli, Bin; Tu, Xiao-Long

2013-07-01

Integrating sphere and diffuser are always used as extended source, and they have different effects on radiance calibration of imaging spectrometer with parameter difference. In the present paper, a mathematical model based on the theory of radiative transfer and calibration principle is founded to calculate the irradiance and calibration coefficients on CCD, taking relatively poor uniformity lights-board calibration system for example. The effects of the nonuniformity on the calibration was analyzed, which makes up the correlation of calibration coefficient matrix under ideal and unideal situation. The results show that the nonuniformity makes the viewing angle and the position of the point of intersection of the optical axis and the diffuse reflection plate have relatively large effects on calibration, while the observing distance's effect is small; under different viewing angles, a deviation value can be found that makes the calibration results closest to the desired results. So, the calibration error can be reduced by choosing appropriate deviation value.

Transferability of species distribution models for the detection of an invasive alien bryophyte using imaging spectroscopy data

NASA Astrophysics Data System (ADS)

Skowronek, Sandra; Van De Kerchove, Ruben; Rombouts, Bjorn; Aerts, Raf; Ewald, Michael; Warrie, Jens; Schiefer, Felix; Garzon-Lopez, Carol; Hattab, Tarek; Honnay, Olivier; Lenoir, Jonathan; Rocchini, Duccio; Schmidtlein, Sebastian; Somers, Ben; Feilhauer, Hannes

2018-06-01

Remote sensing is a promising tool for detecting invasive alien plant species. Mapping and monitoring those species requires accurate detection. So far, most studies relied on models that are locally calibrated and validated against available field data. Consequently, detecting invasive alien species at new study areas requires the acquisition of additional field data which can be expensive and time-consuming. Model transfer might thus provide a viable alternative. Here, we mapped the distribution of the invasive alien bryophyte Campylopus introflexus to i) assess the feasibility of spatially transferring locally calibrated models for species detection between four different heathland areas in Germany and Belgium and ii) test the potential of combining calibration data from different sites in one species distribution model (SDM). In a first step, four different SDMs were locally calibrated and validated by combining field data and airborne imaging spectroscopy data with a spatial resolution ranging from 1.8 m to 4 m and a spectral resolution of about 10 nm (244 bands). A one-class classifier, Maxent, which is based on the comparison of probability densities, was used to generate all SDMs. In a second step, each model was transferred to the three other study areas and the performance of the models for predicting C. introflexus occurrences was assessed. Finally, models combining calibration data from three study areas were built and tested on the remaining fourth site. In this step, different combinations of Maxent modelling parameters were tested. For the local models, the area under the curve for a test dataset (test AUC) was between 0.57-0.78, while the test AUC for the single transfer models ranged between 0.45-0.89. For the combined models the test AUC was between 0.54-0.9. The success of transferring models calibrated in one site to another site highly depended on the respective study site; the combined models provided higher test AUC values than the locally calibrated models for three out of four study sites. Furthermore, we also demonstrated the importance of optimizing the Maxent modelling parameters. Overall, our results indicate the potential of a combined model to map C. introflexus without the need for new calibration data.

Error analysis of mechanical system and wavelength calibration of monochromator

NASA Astrophysics Data System (ADS)

Zhang, Fudong; Chen, Chen; Liu, Jie; Wang, Zhihong

2018-02-01

This study focuses on improving the accuracy of a grating monochromator on the basis of the grating diffraction equation in combination with an analysis of the mechanical transmission relationship between the grating, the sine bar, and the screw of the scanning mechanism. First, the relationship between the mechanical error in the monochromator with the sine drive and the wavelength error is analyzed. Second, a mathematical model of the wavelength error and mechanical error is developed, and an accurate wavelength calibration method based on the sine bar's length adjustment and error compensation is proposed. Based on the mathematical model and calibration method, experiments using a standard light source with known spectral lines and a pre-adjusted sine bar length are conducted. The model parameter equations are solved, and subsequent parameter optimization simulations are performed to determine the optimal length ratio. Lastly, the length of the sine bar is adjusted. The experimental results indicate that the wavelength accuracy is ±0.3 nm, which is better than the original accuracy of ±2.6 nm. The results confirm the validity of the error analysis of the mechanical system of the monochromator as well as the validity of the calibration method.

Integrating model behavior, optimization, and sensitivity/uncertainty analysis: overview and application of the MOUSE software toolbox

USDA-ARS?s Scientific Manuscript database

This paper provides an overview of the Model Optimization, Uncertainty, and SEnsitivity Analysis (MOUSE) software application, an open-source, Java-based toolbox of visual and numerical analysis components for the evaluation of environmental models. MOUSE is based on the OPTAS model calibration syst...

An improved error assessment for the GEM-T1 gravitational model

NASA Technical Reports Server (NTRS)

Lerch, F. J.; Marsh, J. G.; Klosko, S. M.; Pavlis, E. C.; Patel, G. B.; Chinn, D. S.; Wagner, C. A.

1988-01-01

Several tests were designed to determine the correct error variances for the Goddard Earth Model (GEM)-T1 gravitational solution which was derived exclusively from satellite tracking data. The basic method employs both wholly independent and dependent subset data solutions and produces a full field coefficient estimate of the model uncertainties. The GEM-T1 errors were further analyzed using a method based upon eigenvalue-eigenvector analysis which calibrates the entire covariance matrix. Dependent satellite and independent altimetric and surface gravity data sets, as well as independent satellite deep resonance information, confirm essentially the same error assessment. These calibrations (utilizing each of the major data subsets within the solution) yield very stable calibration factors which vary by approximately 10 percent over the range of tests employed. Measurements of gravity anomalies obtained from altimetry were also used directly as observations to show that GEM-T1 is calibrated. The mathematical representation of the covariance error in the presence of unmodeled systematic error effects in the data is analyzed and an optimum weighting technique is developed for these conditions. This technique yields an internal self-calibration of the error model, a process which GEM-T1 is shown to approximate.

A Taxonomy-Based Approach to Shed Light on the Babel of Mathematical Models for Rice Simulation

NASA Technical Reports Server (NTRS)

Confalonieri, Roberto; Bregaglio, Simone; Adam, Myriam; Ruget, Francoise; Li, Tao; Hasegawa, Toshihiro; Yin, Xinyou; Zhu, Yan; Boote, Kenneth; Buis, Samuel;

Modelling exploration of non-stationary hydrological system

NASA Astrophysics Data System (ADS)

Kim, Kue Bum; Kwon, Hyun-Han; Han, Dawei

2015-04-01

Traditional hydrological modelling assumes that the catchment does not change with time (i.e., stationary conditions) which means the model calibrated for the historical period is valid for the future period. However, in reality, due to change of climate and catchment conditions this stationarity assumption may not be valid in the future. It is a challenge to make the hydrological model adaptive to the future climate and catchment conditions that are not observable at the present time. In this study a lumped conceptual rainfall-runoff model called IHACRES was applied to a catchment in southwest England. Long observation data from 1961 to 2008 were used and seasonal calibration (in this study only summer period is further explored because it is more sensitive to climate and land cover change than the other three seasons) has been done since there are significant seasonal rainfall patterns. We expect that the model performance can be improved by calibrating the model based on individual seasons. The data is split into calibration and validation periods with the intention of using the validation period to represent the future unobserved situations. The success of the non-stationary model will depend not only on good performance during the calibration period but also the validation period. Initially, the calibration is based on changing the model parameters with time. Methodology is proposed to adapt the parameters using the step forward and backward selection schemes. However, in the validation both the forward and backward multiple parameter changing models failed. One problem is that the regression with time is not reliable since the trend may not be in a monotonic linear relationship with time. The second issue is that changing multiple parameters makes the selection process very complex which is time consuming and not effective in the validation period. As a result, two new concepts are explored. First, only one parameter is selected for adjustment while the other parameters are set as constant. Secondly, regression is made against climate condition instead of against time. It has been found that such a new approach is very effective and this non-stationary model worked very well both in the calibration and validation period. Although the catchment is specific in southwest England and the data are for only the summer period, the methodology proposed in this study is general and applicable to other catchments. We hope this study will stimulate the hydrological community to explore a variety of sites so that valuable experiences and knowledge could be gained to improve our understanding of such a complex modelling issue in climate change impact assessment.

A novel multivariate approach using science-based calibration for direct coating thickness determination in real-time NIR process monitoring.

PubMed

Möltgen, C-V; Herdling, T; Reich, G

2013-11-01

This study demonstrates an approach, using science-based calibration (SBC), for direct coating thickness determination on heart-shaped tablets in real-time. Near-Infrared (NIR) spectra were collected during four full industrial pan coating operations. The tablets were coated with a thin hydroxypropyl methylcellulose (HPMC) film up to a film thickness of 28 μm. The application of SBC permits the calibration of the NIR spectral data without using costly determined reference values. This is due to the fact that SBC combines classical methods to estimate the coating signal and statistical methods for the noise estimation. The approach enabled the use of NIR for the measurement of the film thickness increase from around 8 to 28 μm of four independent batches in real-time. The developed model provided a spectroscopic limit of detection for the coating thickness of 0.64 ± 0.03 μm root-mean square (RMS). In the commonly used statistical methods for calibration, such as Partial Least Squares (PLS), sufficiently varying reference values are needed for calibration. For thin non-functional coatings this is a challenge because the quality of the model depends on the accuracy of the selected calibration standards. The obvious and simple approach of SBC eliminates many of the problems associated with the conventional statistical methods and offers an alternative for multivariate calibration. Copyright © 2013 Elsevier B.V. All rights reserved.

Data filtering with support vector machines in geometric camera calibration.

PubMed

Ergun, B; Kavzoglu, T; Colkesen, I; Sahin, C

2010-02-01

The use of non-metric digital cameras in close-range photogrammetric applications and machine vision has become a popular research agenda. Being an essential component of photogrammetric evaluation, camera calibration is a crucial stage for non-metric cameras. Therefore, accurate camera calibration and orientation procedures have become prerequisites for the extraction of precise and reliable 3D metric information from images. The lack of accurate inner orientation parameters can lead to unreliable results in the photogrammetric process. A camera can be well defined with its principal distance, principal point offset and lens distortion parameters. Different camera models have been formulated and used in close-range photogrammetry, but generally sensor orientation and calibration is performed with a perspective geometrical model by means of the bundle adjustment. In this study, support vector machines (SVMs) using radial basis function kernel is employed to model the distortions measured for Olympus Aspherical Zoom lens Olympus E10 camera system that are later used in the geometric calibration process. It is intended to introduce an alternative approach for the on-the-job photogrammetric calibration stage. Experimental results for DSLR camera with three focal length settings (9, 18 and 36 mm) were estimated using bundle adjustment with additional parameters, and analyses were conducted based on object point discrepancies and standard errors. Results show the robustness of the SVMs approach on the correction of image coordinates by modelling total distortions on-the-job calibration process using limited number of images.

Effect of Using Extreme Years in Hydrologic Model Calibration Performance

NASA Astrophysics Data System (ADS)

Goktas, R. K.; Tezel, U.; Kargi, P. G.; Ayvaz, T.; Tezyapar, I.; Mesta, B.; Kentel, E.

2017-12-01

Hydrological models are useful in predicting and developing management strategies for controlling the system behaviour. Specifically they can be used for evaluating streamflow at ungaged catchments, effect of climate change, best management practices on water resources, or identification of pollution sources in a watershed. This study is a part of a TUBITAK project named "Development of a geographical information system based decision-making tool for water quality management of Ergene Watershed using pollutant fingerprints". Within the scope of this project, first water resources in Ergene Watershed is studied. Streamgages found in the basin are identified and daily streamflow measurements are obtained from State Hydraulic Works of Turkey. Streamflow data is analysed using box-whisker plots, hydrographs and flow-duration curves focusing on identification of extreme periods, dry or wet. Then a hydrological model is developed for Ergene Watershed using HEC-HMS in the Watershed Modeling System (WMS) environment. The model is calibrated for various time periods including dry and wet ones and the performance of calibration is evaluated using Nash-Sutcliffe Efficiency (NSE), correlation coefficient, percent bias (PBIAS) and root mean square error. It is observed that calibration period affects the model performance, and the main purpose of the development of the hydrological model should guide calibration period selection. Acknowledgement: This study is funded by The Scientific and Technological Research Council of Turkey (TUBITAK) under Project Number 115Y064.

HYDROGRAV - Hydrological model calibration and terrestrial water storage monitoring from GRACE gravimetry and satellite altimetry - First results

NASA Astrophysics Data System (ADS)

Andersen, O. B.; Krogh, P. E.; Michailovsky, C.; Bauer-Gottwein, P.; Christiansen, L.; Berry, P.; Garlick, J.

2008-12-01

Space-borne and ground-based time-lapse gravity observations provide new data for water balance monitoring and hydrological model calibration in the future. The HYDROGRAV project (www.hydrograv.dk) will explore the utility of time-lapse gravity surveys for hydrological model calibration and terrestrial water storage monitoring. Merging remote sensing data from GRACE with other remote sensing data like satellite altimetry and also ground based observations are important to hydrological model calibration and water balance monitoring of large regions and can serve as either supplement or as vital information in un-gauged regions. A system of GRACE custom designed Mass Concentration blocks (Mascons) have been designed to model time-variable gravity changes for the largest basins in Southern Africa (Zambezi, Okavango, Limpopo and Orange) covering an area of 9 mill km2 with a resolution of 1 by 1.25 degree. Satellite altimetry have been used to derive high resolution point-wise river height in some of the un-gauged rivers in the region by using dedicated retracking to recovers nearly un-interrupted time series over these rivers. First result from the HYDROGRAV project analyzing GRACE derived mass change from 2002 to 2008 along with in-situ gravity time-lapse observations and radar altimetry monitoring of surface water for the southern Africa river basins will be presented.

High-precision method of binocular camera calibration with a distortion model.

PubMed

Li, Weimin; Shan, Siyu; Liu, Hui

2017-03-10

A high-precision camera calibration method for binocular stereo vision system based on a multi-view template and alternative bundle adjustment is presented in this paper. The proposed method could be achieved by taking several photos on a specially designed calibration template that has diverse encoded points in different orientations. In this paper, the method utilized the existing algorithm used for monocular camera calibration to obtain the initialization, which involves a camera model, including radial lens distortion and tangential distortion. We created a reference coordinate system based on the left camera coordinate to optimize the intrinsic parameters of left camera through alternative bundle adjustment to obtain optimal values. Then, optimal intrinsic parameters of the right camera can be obtained through alternative bundle adjustment when we create a reference coordinate system based on the right camera coordinate. We also used all intrinsic parameters that were acquired to optimize extrinsic parameters. Thus, the optimal lens distortion parameters and intrinsic and extrinsic parameters were obtained. Synthetic and real data were used to test the method. The simulation results demonstrate that the maximum mean absolute relative calibration errors are about 3.5e-6 and 1.2e-6 for the focal length and the principal point, respectively, under zero-mean Gaussian noise with 0.05 pixels standard deviation. The real result shows that the reprojection error of our model is about 0.045 pixels with the relative standard deviation of 1.0e-6 over the intrinsic parameters. The proposed method is convenient, cost-efficient, highly precise, and simple to carry out.

Parameter regionalization of a monthly water balance model for the conterminous United States

NASA Astrophysics Data System (ADS)

Bock, A. R.; Hay, L. E.; McCabe, G. J.; Markstrom, S. L.; Atkinson, R. D.

2015-09-01

A parameter regionalization scheme to transfer parameter values and model uncertainty information from gaged to ungaged areas for a monthly water balance model (MWBM) was developed and tested for the conterminous United States (CONUS). The Fourier Amplitude Sensitivity Test, a global-sensitivity algorithm, was implemented on a MWBM to generate parameter sensitivities on a set of 109 951 hydrologic response units (HRUs) across the CONUS. The HRUs were grouped into 110 calibration regions based on similar parameter sensitivities. Subsequently, measured runoff from 1575 streamgages within the calibration regions were used to calibrate the MWBM parameters to produce parameter sets for each calibration region. Measured and simulated runoff at the 1575 streamgages showed good correspondence for the majority of the CONUS, with a median computed Nash-Sutcliffe Efficiency coefficient of 0.76 over all streamgages. These methods maximize the use of available runoff information, resulting in a calibrated CONUS-wide application of the MWBM suitable for providing estimates of water availability at the HRU resolution for both gaged and ungaged areas of the CONUS.

Data-base development for water-quality modeling of the Patuxent River basin, Maryland

USGS Publications Warehouse

Fisher, G.T.; Summers, R.M.

1987-01-01

Procedures and rationale used to develop a data base and data management system for the Patuxent Watershed Nonpoint Source Water Quality Monitoring and Modeling Program of the Maryland Department of the Environment and the U.S. Geological Survey are described. A detailed data base and data management system has been developed to facilitate modeling of the watershed for water quality planning purposes; statistical analysis; plotting of meteorologic, hydrologic and water quality data; and geographic data analysis. The system is Maryland 's prototype for development of a basinwide water quality management program. A key step in the program is to build a calibrated and verified water quality model of the basin using the Hydrological Simulation Program--FORTRAN (HSPF) hydrologic model, which has been used extensively in large-scale basin modeling. The compilation of the substantial existing data base for preliminary calibration of the basin model, including meteorologic, hydrologic, and water quality data from federal and state data bases and a geographic information system containing digital land use and soils data is described. The data base development is significant in its application of an integrated, uniform approach to data base management and modeling. (Lantz-PTT)

Vicarious calibration of GOES imager visible channel using the moon

USGS Publications Warehouse

Wu, X.; Stone, T.C.; Yu, F.; Han, D.

2006-01-01

In this paper, we study the feasibility of a method for vicarious calibration of the GOES Imager visible channel using the Moon. The measured Moon irradiance from 26 undipped moon imagers exhausted all the potential Moon appearances between July 1998 and December 2005, together with the seven scheduled Moon observation data obtained after November 2005, were compared with the USGS lunar model results to estimate the degradation rate of the GOES-10 Imager visible channel. A total of nine methods of determining the space count and identifying lunar pixels were employed in this study to measure the GOES-10 Moon irradiance. Our results show that the selected mean and the masking Moon appears the best method. Eight of the nine resulting degradation rates range from 4.5%/year to 5.0%/year during the nearly nine years of data, which are consistent with most other degradation rates obtained for GOES-10 based on different references. In particular, the degradation rate from the Moon-based calibration (4.5%/year) agrees very well with the MODIS-based calibration (4.4%/year) over the same period, confirming the capability of relative and absolute calibration based on the Moon. Finally, our estimate of lunar calibration precision as applied to GOES-10 is 3.5%.

QSPR modeling of octanol/water partition coefficient of antineoplastic agents by balance of correlations.

PubMed

Toropov, Andrey A; Toropova, Alla P; Raska, Ivan; Benfenati, Emilio

2010-04-01

Three different splits into the subtraining set (n = 22), the set of calibration (n = 21), and the test set (n = 12) of 55 antineoplastic agents have been examined. By the correlation balance of SMILES-based optimal descriptors quite satisfactory models for the octanol/water partition coefficient have been obtained on all three splits. The correlation balance is the optimization of a one-variable model with a target function that provides both the maximal values of the correlation coefficient for the subtraining and calibration set and the minimum of the difference between the above-mentioned correlation coefficients. Thus, the calibration set is a preliminary test set. Copyright (c) 2009 Elsevier Masson SAS. All rights reserved.

Application of global kinetic models to HMX beta-delta transition and cookoff processes.

PubMed

Wemhoff, Aaron P; Burnham, Alan K; Nichols, Albert L

2007-03-08

The reduction of the number of reactions in kinetic models for both the HMX (octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine) beta-delta phase transition and thermal cookoff provides an attractive alternative to traditional multi-stage kinetic models due to reduced calibration effort requirements. In this study, we use the LLNL code ALE3D to provide calibrated kinetic parameters for a two-reaction bidirectional beta-delta HMX phase transition model based on Sandia instrumented thermal ignition (SITI) and scaled thermal explosion (STEX) temperature history curves, and a Prout-Tompkins cookoff model based on one-dimensional time to explosion (ODTX) data. Results show that the two-reaction bidirectional beta-delta transition model presented here agrees as well with STEX and SITI temperature history curves as a reversible four-reaction Arrhenius model yet requires an order of magnitude less computational effort. In addition, a single-reaction Prout-Tompkins model calibrated to ODTX data provides better agreement with ODTX data than a traditional multistep Arrhenius model and can contain up to 90% fewer chemistry-limited time steps for low-temperature ODTX simulations. Manual calibration methods for the Prout-Tompkins kinetics provide much better agreement with ODTX experimental data than parameters derived from differential scanning calorimetry (DSC) measurements at atmospheric pressure. The predicted surface temperature at explosion for STEX cookoff simulations is a weak function of the cookoff model used, and a reduction of up to 15% of chemistry-limited time steps can be achieved by neglecting the beta-delta transition for this type of simulation. Finally, the inclusion of the beta-delta transition model in the overall kinetics model can affect the predicted time to explosion by 1% for the traditional multistep Arrhenius approach, and up to 11% using a Prout-Tompkins cookoff model.

A Calibration Method for Nanowire Biosensors to Suppress Device-to-device Variation

PubMed Central

Ishikawa, Fumiaki N.; Curreli, Marco; Chang, Hsiao-Kang; Chen, Po-Chiang; Zhang, Rui; Cote, Richard J.; Thompson, Mark E.; Zhou, Chongwu

2009-01-01

Nanowire/nanotube biosensors have stimulated significant interest; however the inevitable device-to-device variation in the biosensor performance remains a great challenge. We have developed an analytical method to calibrate nanowire biosensor responses that can suppress the device-to-device variation in sensing response significantly. The method is based on our discovery of a strong correlation between the biosensor gate dependence (dIds/dVg) and the absolute response (absolute change in current, ΔI). In2O3 nanowire based biosensors for streptavidin detection were used as the model system. Studying the liquid gate effect and ionic concentration dependence of strepavidin sensing indicates that electrostatic interaction is the dominant mechanism for sensing response. Based on this sensing mechanism and transistor physics, a linear correlation between the absolute sensor response (ΔI) and the gate dependence (dIds/dVg) is predicted and confirmed experimentally. Using this correlation, a calibration method was developed where the absolute response is divided by dIds/dVg for each device, and the calibrated responses from different devices behaved almost identically. Compared to the common normalization method (normalization of the conductance/resistance/current by the initial value), this calibration method was proved advantageous using a conventional transistor model. The method presented here substantially suppresses device-to-device variation, allowing the use of nanosensors in large arrays. PMID:19921812

[Improving apple fruit quality predictions by effective correction of Vis-NIR laser diffuse reflecting images].

PubMed

Qing, Zhao-shen; Ji, Bao-ping; Shi, Bo-lin; Zhu, Da-zhou; Tu, Zhen-hua; Zude, Manuela

2008-06-01

In the present study, improved laser-induced light backscattering imaging was studied regarding its potential for analyzing apple SSC and fruit flesh firmness. Images of the diffuse reflection of light on the fruit surface were obtained from Fuji apples using laser diodes emitting at five wavelength bands (680, 780, 880, 940 and 980 nm). Image processing algorithms were tested to correct for dissimilar equator and shape of fruit, and partial least squares (PLS) regression analysis was applied to calibrate on the fruit quality parameter. In comparison to the calibration based on corrected frequency with the models built by raw data, the former improved r from 0. 78 to 0.80 and from 0.87 to 0.89 for predicting SSC and firmness, respectively. Comparing models based on mean value of intensities with results obtained by frequency of intensities, the latter gave higher performance for predicting Fuji SSC and firmness. Comparing calibration for predicting SSC based on the corrected frequency of intensities and the results obtained from raw data set, the former improved root mean of standard error of prediction (RMSEP) from 1.28 degrees to 0.84 degrees Brix. On the other hand, in comparison to models for analyzing flesh firmness built by means of corrected frequency of intensities with the calibrations based on raw data, the former gave the improvement in RMSEP from 8.23 to 6.17 N x cm(-2).

Real time polymer nanocomposites-based physical nanosensors: theory and modeling.

PubMed

Bellucci, Stefano; Shunin, Yuri; Gopeyenko, Victor; Lobanova-Shunina, Tamara; Burlutskaya, Nataly; Zhukovskii, Yuri

2017-09-01

Functionalized carbon nanotubes and graphene nanoribbons nanostructures, serving as the basis for the creation of physical pressure and temperature nanosensors, are considered as tools for ecological monitoring and medical applications. Fragments of nanocarbon inclusions with different morphologies, presenting a disordered system, are regarded as models for nanocomposite materials based on carbon nanoсluster suspension in dielectric polymer environments (e.g., epoxy resins). We have formulated the approach of conductivity calculations for carbon-based polymer nanocomposites using the effective media cluster approach, disordered systems theory and conductivity mechanisms analysis, and obtained the calibration dependences. Providing a proper description of electric responses in nanosensoring systems, we demonstrate the implementation of advanced simulation models suitable for real time control nanosystems. We also consider the prospects and prototypes of the proposed physical nanosensor models providing the comparisons with experimental calibration dependences.

Real time polymer nanocomposites-based physical nanosensors: theory and modeling

NASA Astrophysics Data System (ADS)

Bellucci, Stefano; Shunin, Yuri; Gopeyenko, Victor; Lobanova-Shunina, Tamara; Burlutskaya, Nataly; Zhukovskii, Yuri

2017-09-01

Functionalized carbon nanotubes and graphene nanoribbons nanostructures, serving as the basis for the creation of physical pressure and temperature nanosensors, are considered as tools for ecological monitoring and medical applications. Fragments of nanocarbon inclusions with different morphologies, presenting a disordered system, are regarded as models for nanocomposite materials based on carbon nanoсluster suspension in dielectric polymer environments (e.g., epoxy resins). We have formulated the approach of conductivity calculations for carbon-based polymer nanocomposites using the effective media cluster approach, disordered systems theory and conductivity mechanisms analysis, and obtained the calibration dependences. Providing a proper description of electric responses in nanosensoring systems, we demonstrate the implementation of advanced simulation models suitable for real time control nanosystems. We also consider the prospects and prototypes of the proposed physical nanosensor models providing the comparisons with experimental calibration dependences.

Aerosol backscatter lidar calibration and data interpretation

NASA Technical Reports Server (NTRS)

Kavaya, M. J.; Menzies, R. T.

1984-01-01

A treatment of the various factors involved in lidar data acquisition and analysis is presented. This treatment highlights sources of fundamental, systematic, modeling, and calibration errors that may affect the accurate interpretation and calibration of lidar aerosol backscatter data. The discussion primarily pertains to ground based, pulsed CO2 lidars that probe the troposphere and are calibrated using large, hard calibration targets. However, a large part of the analysis is relevant to other types of lidar systems such as lidars operating at other wavelengths; continuous wave (CW) lidars; lidars operating in other regions of the atmosphere; lidars measuring nonaerosol elastic or inelastic backscatter; airborne or Earth-orbiting lidar platforms; and lidars employing combinations of the above characteristics.

Radiometric calibration of an airborne multispectral scanner. [of Thematic Mapper Simulator

NASA Technical Reports Server (NTRS)

Markham, Brian L.; Ahmad, Suraiya P.; Jackson, Ray D.; Moran, M. S.; Biggar, Stuart F.; Gellman, David I.; Slater, Philip N.

1991-01-01

The absolute radiometric calibration of the NS001 Thematic Mapper Simulator reflective channels was examined based on laboratory tests and in-flight comparisons to ground measurements. The NS001 data are calibrated in-flight by reference to the NS001 internal integrating sphere source. This source's power supply or monitoring circuitry exhibited greater instability in-flight during 1988-1989 than in the laboratory. Extrapolating laboratory behavior to in-flight data resulted in 7-20 percent radiance errors relative to ground measurements and atmospheric modeling. Assuming constancy in the source's output between laboraotry and in-flight resulted in generally smaller errors. Upgrades to the source's power supply and monitoring circuitry in 1990 improved its in-flight stability, though in-flight ground reflectance based calibration tests have not yet been performed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wemhoff, A P; Burnham, A K; Nichols III, A L

The reduction of the number of reactions in kinetic models for both the HMX beta-delta phase transition and thermal cookoff provides an attractive alternative to traditional multi-stage kinetic models due to reduced calibration effort requirements. In this study, we use the LLNL code ALE3D to provide calibrated kinetic parameters for a two-reaction bidirectional beta-delta HMX phase transition model based on Sandia Instrumented Thermal Ignition (SITI) and Scaled Thermal Explosion (STEX) temperature history curves, and a Prout-Tompkins cookoff model based on One-Dimensional Time to Explosion (ODTX) data. Results show that the two-reaction bidirectional beta-delta transition model presented here agrees as wellmore » with STEX and SITI temperature history curves as a reversible four-reaction Arrhenius model, yet requires an order of magnitude less computational effort. In addition, a single-reaction Prout-Tompkins model calibrated to ODTX data provides better agreement with ODTX data than a traditional multi-step Arrhenius model, and can contain up to 90% less chemistry-limited time steps for low-temperature ODTX simulations. Manual calibration methods for the Prout-Tompkins kinetics provide much better agreement with ODTX experimental data than parameters derived from Differential Scanning Calorimetry (DSC) measurements at atmospheric pressure. The predicted surface temperature at explosion for STEX cookoff simulations is a weak function of the cookoff model used, and a reduction of up to 15% of chemistry-limited time steps can be achieved by neglecting the beta-delta transition for this type of simulation. Finally, the inclusion of the beta-delta transition model in the overall kinetics model can affect the predicted time to explosion by 1% for the traditional multi-step Arrhenius approach, while up to 11% using a Prout-Tompkins cookoff model.« less

Simple transfer calibration method for a Cimel Sun-Moon photometer: calculating lunar calibration coefficients from Sun calibration constants.

PubMed

Li, Zhengqiang; Li, Kaitao; Li, Donghui; Yang, Jiuchun; Xu, Hua; Goloub, Philippe; Victori, Stephane

2016-09-20

The Cimel new technologies allow both daytime and nighttime aerosol optical depth (AOD) measurements. Although the daytime AOD calibration protocols are well established, accurate and simple nighttime calibration is still a challenging task. Standard lunar-Langley and intercomparison calibration methods both require specific conditions in terms of atmospheric stability and site condition. Additionally, the lunar irradiance model also has some known limits on its uncertainty. This paper presents a simple calibration method that transfers the direct-Sun calibration constant, V_0,Sun, to the lunar irradiance calibration coefficient, C_Moon. Our approach is a pure calculation method, independent of site limits, e.g., Moon phase. The method is also not affected by the lunar irradiance model limitations, which is the largest error source of traditional calibration methods. Besides, this new transfer calibration approach is easy to use in the field since C_Moon can be obtained directly once V_0,Sun is known. Error analysis suggests that the average uncertainty of C_Moon over the 440-1640 nm bands obtained with the transfer method is 2.4%-2.8%, depending on the V_0,Sun approach (Langley or intercomparison), which is comparable with that of lunar-Langley approach, theoretically. In this paper, the Sun-Moon transfer and the Langley methods are compared based on site measurements in Beijing, and the day-night measurement continuity and performance are analyzed.

Integrating SMOS brightness temperatures with a new conceptual spatially distributed hydrological model for improving flood and drought predictions at large scale.

NASA Astrophysics Data System (ADS)

Hostache, Renaud; Rains, Dominik; Chini, Marco; Lievens, Hans; Verhoest, Niko E. C.; Matgen, Patrick

2017-04-01

Motivated by climate change and its impact on the scarcity or excess of water in many parts of the world, several agencies and research institutions have taken initiatives in monitoring and predicting the hydrologic cycle at a global scale. Such a monitoring/prediction effort is important for understanding the vulnerability to extreme hydrological events and for providing early warnings. This can be based on an optimal combination of hydro-meteorological models and remote sensing, in which satellite measurements can be used as forcing or calibration data or for regularly updating the model states or parameters. Many advances have been made in these domains and the near future will bring new opportunities with respect to remote sensing as a result of the increasing number of spaceborn sensors enabling the large scale monitoring of water resources. Besides of these advances, there is currently a tendency to refine and further complicate physically-based hydrologic models to better capture the hydrologic processes at hand. However, this may not necessarily be beneficial for large-scale hydrology, as computational efforts are therefore increasing significantly. As a matter of fact, a novel thematic science question that is to be investigated is whether a flexible conceptual model can match the performance of a complex physically-based model for hydrologic simulations at large scale. In this context, the main objective of this study is to investigate how innovative techniques that allow for the estimation of soil moisture from satellite data can help in reducing errors and uncertainties in large scale conceptual hydro-meteorological modelling. A spatially distributed conceptual hydrologic model has been set up based on recent developments of the SUPERFLEX modelling framework. As it requires limited computational efforts, this model enables early warnings for large areas. Using as forcings the ERA-Interim public dataset and coupled with the CMEM radiative transfer model, SUPERFLEX is capable of predicting runoff, soil moisture, and SMOS-like brightness temperature time series. Such a model is traditionally calibrated using only discharge measurements. In this study we designed a multi-objective calibration procedure based on both discharge measurements and SMOS-derived brightness temperature observations in order to evaluate the added value of remotely sensed soil moisture data in the calibration process. As a test case we set up the SUPERFLEX model for the large scale Murray-Darling catchment in Australia ( 1 Million km2). When compared to in situ soil moisture time series, model predictions show good agreement resulting in correlation coefficients exceeding 70 % and Root Mean Squared Errors below 1 %. When benchmarked with the physically based land surface model CLM, SUPERFLEX exhibits similar performance levels. By adapting the runoff routing function within the SUPERFLEX model, the predicted discharge results in a Nash Sutcliff Efficiency exceeding 0.7 over both the calibration and the validation periods.

Spatial and Temporal Self-Calibration of a Hydroeconomic Model

NASA Astrophysics Data System (ADS)

Howitt, R. E.; Hansen, K. M.

2008-12-01

Hydroeconomic modeling of water systems where risk and reliability of water supply are of critical importance must address explicitly how to model water supply uncertainty. When large fluctuations in annual precipitation and significant variation in flows by location are present, a model which solves with perfect foresight of future water conditions may be inappropriate for some policy and research questions. We construct a simulation-optimization model with limited foresight of future water conditions using positive mathematical programming and self-calibration techniques. This limited foresight netflow (LFN) model signals the value of storing water for future use and reflects a more accurate economic value of water at key locations, given that future water conditions are unknown. Failure to explicitly model this uncertainty could lead to undervaluation of storage infrastructure and contractual mechanisms for managing water supply risk. A model based on sequentially updated information is more realistic, since water managers make annual storage decisions without knowledge of yet to be realized future water conditions. The LFN model runs historical hydrological conditions through the current configuration of the California water system to determine the economically efficient allocation of water under current economic conditions and infrastructure. The model utilizes current urban and agricultural demands, storage and conveyance infrastructure, and the state's hydrological history to indicate the scarcity value of water at key locations within the state. Further, the temporal calibration penalty functions vary by year type, reflecting agricultural water users' ability to alter cropping patterns in response to water conditions. The model employs techniques from positive mathematical programming (Howitt, 1995; Howitt, 1998; Cai and Wang, 2006) to generate penalty functions that are applied to deviations from observed data. The functions are applied to monthly flows across key nodes on the network and to annual carryover storage at ground and surface water storage facilities. To our knowledge, this is the first hydroeconomic model to perform spatial and temporal calibration simultaneously. The base for the LFN model is CALVIN, a hydroeconomic optimization model of the California water system developed at the University of California, Davis (Draper, et al. 2003). The LFN model, programmed in GAMS, is nonlinear, which permits incorporation of dynamic groundwater pumping costs that reflect head elevation. Hydropower production, also nonlinear in storage levels, could be added in the future. In this paper, we describe model implementation and performance over a sequence of water years drawn from the historical hydrologic record in California. Preliminary findings indicate that calibration occurs within acceptable limits and simulations replicate base case results well. Cai, X., and Wang, D. 2006. "Calibrating Holistic Water Resources-Economic Models." Journal of Water Resources Planning and Management November-December. Draper, A.J., M.W. Jenkins, K.W. Kirby, J.R. Lund, and R.E. Howitt. 2003. "Economic-Engineering Optimization for California Water Management." Journal of Water Resources Planning and Management 129(3):155-164. Howitt, R.E. 1995. "Positive Mathematical Programming." American Journal of Agricultural Economics 77:329-342. Howitt, R.E. 1998. "Self-Calibrating Network Flow Models." Working Paper, Department of Agricultural and Resource Economics, University of California, Davis. October 1998. class="ab'>

Improving contact layer patterning using SEM contour based etch model

NASA Astrophysics Data System (ADS)

Weisbuch, François; Lutich, Andrey; Schatz, Jirka; Hertzsch, Tino; Moll, Hans-Peter

2016-10-01

The patterning of the contact layer is modulated by strong etch effects that are highly dependent on the geometry of the contacts. Such litho-etch biases need to be corrected to ensure a good pattern fidelity. But aggressive designs contain complex shapes that can hardly be compensated with etch bias table and are difficult to characterize with standard CD metrology. In this work we propose to implement a model based etch compensation method able to deal with any contact configuration. With the help of SEM contours, it was possible to get reliable 2D measurements particularly helpful to calibrate the etch model. The selections of calibration structures was optimized in combination with model form to achieve an overall errRMS of 3nm allowing the implementation of the model in production.

A Kinematic Calibration Process for Flight Robotic Arms

NASA Technical Reports Server (NTRS)

Collins, Curtis L.; Robinson, Matthew L.

2013-01-01

The Mars Science Laboratory (MSL) robotic arm is ten times more massive than any Mars robotic arm before it, yet with similar accuracy and repeatability positioning requirements. In order to assess and validate these requirements, a higher-fidelity model and calibration processes were needed. Kinematic calibration of robotic arms is a common and necessary process to ensure good positioning performance. Most methodologies assume a rigid arm, high-accuracy data collection, and some kind of optimization of kinematic parameters. A new detailed kinematic and deflection model of the MSL robotic arm was formulated in the design phase and used to update the initial positioning and orientation accuracy and repeatability requirements. This model included a higher-fidelity link stiffness matrix representation, as well as a link level thermal expansion model. In addition, it included an actuator backlash model. Analytical results highlighted the sensitivity of the arm accuracy to its joint initialization methodology. Because of this, a new technique for initializing the arm joint encoders through hardstop calibration was developed. This involved selecting arm configurations to use in Earth-based hardstop calibration that had corresponding configurations on Mars with the same joint torque to ensure repeatability in the different gravity environment. The process used to collect calibration data for the arm included the use of multiple weight stand-in turrets with enough metrology targets to reconstruct the full six-degree-of-freedom location of the rover and tool frames. The follow-on data processing of the metrology data utilized a standard differential formulation and linear parameter optimization technique.

Calibration Modeling Methodology to Optimize Performance for Low Range Applications

NASA Technical Reports Server (NTRS)

McCollum, Raymond A.; Commo, Sean A.; Parker, Peter A.

2010-01-01

Calibration is a vital process in characterizing the performance of an instrument in an application environment and seeks to obtain acceptable accuracy over the entire design range. Often, project requirements specify a maximum total measurement uncertainty, expressed as a percent of full-scale. However in some applications, we seek to obtain enhanced performance at the low range, therefore expressing the accuracy as a percent of reading should be considered as a modeling strategy. For example, it is common to desire to use a force balance in multiple facilities or regimes, often well below its designed full-scale capacity. This paper presents a general statistical methodology for optimizing calibration mathematical models based on a percent of reading accuracy requirement, which has broad application in all types of transducer applications where low range performance is required. A case study illustrates the proposed methodology for the Mars Entry Atmospheric Data System that employs seven strain-gage based pressure transducers mounted on the heatshield of the Mars Science Laboratory mission.

A two-scale Weibull approach to the failure of porous ceramic structures made by robocasting: possibilities and limits

PubMed Central

Genet, Martin; Houmard, Manuel; Eslava, Salvador; Saiz, Eduardo; Tomsia, Antoni P.

2012-01-01

This paper introduces our approach to modeling the mechanical behavior of cellular ceramics, through the example of calcium phosphate scaffolds made by robocasting for bone-tissue engineering. The Weibull theory is used to deal with the scaffolds’ constitutive rods statistical failure, and the Sanchez-Palencia theory of periodic homogenization is used to link the rod- and scaffold-scales. Uniaxial compression of scaffolds and three-point bending of rods were performed to calibrate and validate the model. If calibration based on rod-scale data leads to over-conservative predictions of scaffold’s properties (as rods’ successive failures are not taken into account), we show that, for a given rod diameter, calibration based on scaffold-scale data leads to very satisfactory predictions for a wide range of rod spacing, i.e. of scaffold porosity, as well as for different loading conditions. This work establishes the proposed model as a reliable tool for understanding and optimizing cellular ceramics’ mechanical properties. PMID:23439936

Scaling field data to calibrate and validate moderate spatial resolution remote sensing models

USGS Publications Warehouse

Baccini, A.; Friedl, M.A.; Woodcock, C.E.; Zhu, Z.

2007-01-01

Validation and calibration are essential components of nearly all remote sensing-based studies. In both cases, ground measurements are collected and then related to the remote sensing observations or model results. In many situations, and particularly in studies that use moderate resolution remote sensing, a mismatch exists between the sensor's field of view and the scale at which in situ measurements are collected. The use of in situ measurements for model calibration and validation, therefore, requires a robust and defensible method to spatially aggregate ground measurements to the scale at which the remotely sensed data are acquired. This paper examines this challenge and specifically considers two different approaches for aggregating field measurements to match the spatial resolution of moderate spatial resolution remote sensing data: (a) landscape stratification; and (b) averaging of fine spatial resolution maps. The results show that an empirically estimated stratification based on a regression tree method provides a statistically defensible and operational basis for performing this type of procedure. 

Parallel and Preemptable Dynamically Dimensioned Search Algorithms for Single and Multi-objective Optimization in Water Resources

NASA Astrophysics Data System (ADS)

Tolson, B.; Matott, L. S.; Gaffoor, T. A.; Asadzadeh, M.; Shafii, M.; Pomorski, P.; Xu, X.; Jahanpour, M.; Razavi, S.; Haghnegahdar, A.; Craig, J. R.

2015-12-01

We introduce asynchronous parallel implementations of the Dynamically Dimensioned Search (DDS) family of algorithms including DDS, discrete DDS, PA-DDS and DDS-AU. These parallel algorithms are unique from most existing parallel optimization algorithms in the water resources field in that parallel DDS is asynchronous and does not require an entire population (set of candidate solutions) to be evaluated before generating and then sending a new candidate solution for evaluation. One key advance in this study is developing the first parallel PA-DDS multi-objective optimization algorithm. The other key advance is enhancing the computational efficiency of solving optimization problems (such as model calibration) by combining a parallel optimization algorithm with the deterministic model pre-emption concept. These two efficiency techniques can only be combined because of the asynchronous nature of parallel DDS. Model pre-emption functions to terminate simulation model runs early, prior to completely simulating the model calibration period for example, when intermediate results indicate the candidate solution is so poor that it will definitely have no influence on the generation of further candidate solutions. The computational savings of deterministic model preemption available in serial implementations of population-based algorithms (e.g., PSO) disappear in synchronous parallel implementations as these algorithms. In addition to the key advances above, we implement the algorithms across a range of computation platforms (Windows and Unix-based operating systems from multi-core desktops to a supercomputer system) and package these for future modellers within a model-independent calibration software package called Ostrich as well as MATLAB versions. Results across multiple platforms and multiple case studies (from 4 to 64 processors) demonstrate the vast improvement over serial DDS-based algorithms and highlight the important role model pre-emption plays in the performance of parallel, pre-emptable DDS algorithms. Case studies include single- and multiple-objective optimization problems in water resources model calibration and in many cases linear or near linear speedups are observed.

Application of near-infrared spectroscopy for estimation of non-structural carbohydrates in foliar samples of Eucalyptus globulus Labilladière.

PubMed

Quentin, A G; Rodemann, T; Doutreleau, M-F; Moreau, M; Davies, N W; Millard, Peter

2017-01-31

Near-infrared reflectance spectroscopy (NIRS) is frequently used for the assessment of key nutrients of forage or crops but remains underused in ecological and physiological studies, especially to quantify non-structural carbohydrates. The aim of this study was to develop calibration models to assess the content in soluble sugars (fructose, glucose, sucrose) and starch in foliar material of Eucalyptus globulus. A partial least squares (PLS) regression was used on the sample spectral data and was compared to the contents measured using standard wet chemistry methods. The calibration models were validated using a completely independent set of samples. We used key indicators such as the ratio of prediction to deviation (RPD) and the range error ratio to give an assessment of the performance of the calibration models. Accurate calibration models were obtained for fructose and sucrose content (R2 > 0.85, root mean square error of prediction (RMSEP) of 0.95%–1.26% in the validation models), followed by sucrose and total soluble sugar content (R2 ~ 0.70 and RMSEP > 2.3%). In comparison to the others, calibration of the starch model performed very poorly with RPD = 1.70. This study establishes the ability of the NIRS calibration model to infer soluble sugar content in foliar samples of E. globulus in a rapid and cost-effective way. We suggest a complete redevelopment of the starch analysis using more specific quantification such as an HPLC-based technique to reach higher performance in the starch model. Overall, NIRS could serve as a high-throughput phenotyping tool to study plant response to stress factors.

Network operability of ground-based microwave radiometers: Calibration and standardization efforts

NASA Astrophysics Data System (ADS)

Pospichal, Bernhard; Löhnert, Ulrich; Küchler, Nils; Czekala, Harald

2017-04-01

Ground-based microwave radiometers (MWR) are already widely used by national weather services and research institutions all around the world. Most of the instruments operate continuously and are beginning to be implemented into data assimilation for atmospheric models. Especially their potential for continuously observing boundary-layer temperature profiles as well as integrated water vapor and cloud liquid water path makes them valuable for improving short-term weather forecasts. However until now, most MWR have been operated as stand-alone instruments. In order to benefit from a network of these instruments, standardization of calibration, operation and data format is necessary. In the frame of TOPROF (COST Action ES1303) several efforts have been undertaken, such as uncertainty and bias assessment, or calibration intercomparison campaigns. The goal was to establish protocols for providing quality controlled (QC) MWR data and their uncertainties. To this end, standardized calibration procedures for MWR have been developed and recommendations for radiometer users compiled. Based on the results of the TOPROF campaigns, a new, high-accuracy liquid-nitrogen calibration load has been introduced for MWR manufactured by Radiometer Physics GmbH (RPG). The new load improves the accuracy of the measurements considerably and will lead to even more reliable atmospheric observations. Next to the recommendations for set-up, calibration and operation of ground-based MWR within a future network, we will present homogenized methods to determine the accuracy of a running calibration as well as means for automatic data quality control. This sets the stage for the planned microwave calibration center at JOYCE (Jülich Observatory for Cloud Evolution), which will be shortly introduced.

An Innovative Software Tool Suite for Power Plant Model Validation and Parameter Calibration using PMU Measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Yuanyuan; Diao, Ruisheng; Huang, Renke

Maintaining good quality of power plant stability models is of critical importance to ensure the secure and economic operation and planning of today’s power grid with its increasing stochastic and dynamic behavior. According to North American Electric Reliability (NERC) standards, all generators in North America with capacities larger than 10 MVA are required to validate their models every five years. Validation is quite costly and can significantly affect the revenue of generator owners, because the traditional staged testing requires generators to be taken offline. Over the past few years, validating and calibrating parameters using online measurements including phasor measurement unitsmore » (PMUs) and digital fault recorders (DFRs) has been proven to be a cost-effective approach. In this paper, an innovative open-source tool suite is presented for validating power plant models using PPMV tool, identifying bad parameters with trajectory sensitivity analysis, and finally calibrating parameters using an ensemble Kalman filter (EnKF) based algorithm. The architectural design and the detailed procedures to run the tool suite are presented, with results of test on a realistic hydro power plant using PMU measurements for 12 different events. The calibrated parameters of machine, exciter, governor and PSS models demonstrate much better performance than the original models for all the events and show the robustness of the proposed calibration algorithm.« less

Effects of linking a soil-water-balance model with a groundwater-flow model

USGS Publications Warehouse

Stanton, Jennifer S.; Ryter, Derek W.; Peterson, Steven M.

2013-01-01

A previously published regional groundwater-flow model in north-central Nebraska was sequentially linked with the recently developed soil-water-balance (SWB) model to analyze effects to groundwater-flow model parameters and calibration results. The linked models provided a more detailed spatial and temporal distribution of simulated recharge based on hydrologic processes, improvement of simulated groundwater-level changes and base flows at specific sites in agricultural areas, and a physically based assessment of the relative magnitude of recharge for grassland, nonirrigated cropland, and irrigated cropland areas. Root-mean-squared (RMS) differences between the simulated and estimated or measured target values for the previously published model and linked models were relatively similar and did not improve for all types of calibration targets. However, without any adjustment to the SWB-generated recharge, the RMS difference between simulated and estimated base-flow target values for the groundwater-flow model was slightly smaller than for the previously published model, possibly indicating that the volume of recharge simulated by the SWB code was closer to actual hydrogeologic conditions than the previously published model provided. Groundwater-level and base-flow hydrographs showed that temporal patterns of simulated groundwater levels and base flows were more accurate for the linked models than for the previously published model at several sites, particularly in agricultural areas.

Using combined hydrological variables for extracting functional signatures of catchments to better assess the acceptability of model structures in conceptual catchment modelling

NASA Astrophysics Data System (ADS)

Fovet, O.; Hrachowitz, M.; RUIZ, L.; Gascuel-odoux, C.; Savenije, H.

2013-12-01

While most hydrological models reproduce the general flow dynamics of a system, they frequently fail to adequately mimic system internal processes. This is likely to make them inadequate to simulate solutes transport. For example, the hysteresis between storage and discharge, which is often observed in shallow hard-rock aquifers, is rarely well reproduced by models. One main reason is that this hysteresis has little weight in the calibration because objective functions are based on time series of individual variables. This reduces the ability of classical calibration/validation procedures to assess the relevance of the conceptual hypothesis associated with hydrological models. Calibrating models on variables derived from the combination of different individual variables (like stream discharge and groundwater levels) is a way to insure that models will be accepted based on their consistency. Here we therefore test the value of this more systems-like approach to test different hypothesis on the behaviour of a small experimental low-land catchment in French Brittany (ORE AgrHys) where a high hysteresis is observed on the stream flow vs. shallow groundwater level relationship. Several conceptual models were applied to this site, and calibrated using objective functions based on metrics of this hysteresis. The tested model structures differed with respect to the storage function in each reservoir, the storage-discharge function in each reservoir, the deep loss expressions (as constant or variable fraction), the number of reservoirs (from 1 to 4) and their organization (parallel, series). The observed hysteretic groundwater level-discharge relationship was not satisfactorily reproduced by most of the tested models except for the most complex ones. Those were thus more consistent, their underlying hypotheses are probably more realistic even though their performance for simulating observed stream flow was decreased. Selecting models based on such systems-like approach is likely to improve their efficiency for environmental application e.g. on solute transport issues. The next step would be to apply the same approach with variables combining hydrological and biogeochemical variables.

Soybean Physiology Calibration in the Community Land Model

NASA Astrophysics Data System (ADS)

Drewniak, B. A.; Bilionis, I.; Constantinescu, E. M.

2014-12-01

With the large influence of agricultural land use on biophysical and biogeochemical cycles, integrating cultivation into Earth System Models (ESMs) is increasingly important. The Community Land Model (CLM) was augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. However, the strong nonlinearity of ESMs makes parameter fitting a difficult task. In this study, our goal is to calibrate ten of the CLM-Crop parameters for one crop type, soybean, in order to improve model projection of plant development and carbon fluxes. We used measurements of gross primary productivity, net ecosystem exchange, and plant biomass from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC). Our scheme can perform model calibration using very few evaluations and, by exploiting parallelism, at a fraction of the time required by plain vanilla Markov Chain Monte Carlo (MCMC). We present the results from a twin experiment (self-validation) and calibration results and validation using real observations from an AmeriFlux tower site in the Midwestern United States, for the soybean crop type. The improved model will help researchers understand how climate affects crop production and resulting carbon fluxes, and additionally, how cultivation impacts climate.

Use and Customization of Risk Scores for Predicting Cardiovascular Events Using Electronic Health Record Data.

PubMed

Wolfson, Julian; Vock, David M; Bandyopadhyay, Sunayan; Kottke, Thomas; Vazquez-Benitez, Gabriela; Johnson, Paul; Adomavicius, Gediminas; O'Connor, Patrick J

2017-04-24

Clinicians who are using the Framingham Risk Score (FRS) or the American College of Cardiology/American Heart Association Pooled Cohort Equations (PCE) to estimate risk for their patients based on electronic health data (EHD) face 4 questions. (1) Do published risk scores applied to EHD yield accurate estimates of cardiovascular risk? (2) Are FRS risk estimates, which are based on data that are up to 45 years old, valid for a contemporary patient population seeking routine care? (3) Do the PCE make the FRS obsolete? (4) Does refitting the risk score using EHD improve the accuracy of risk estimates? Data were extracted from the EHD of 84 116 adults aged 40 to 79 years who received care at a large healthcare delivery and insurance organization between 2001 and 2011. We assessed calibration and discrimination for 4 risk scores: published versions of FRS and PCE and versions obtained by refitting models using a subset of the available EHD. The published FRS was well calibrated (calibration statistic K=9.1, miscalibration ranging from 0% to 17% across risk groups), but the PCE displayed modest evidence of miscalibration (calibration statistic K=43.7, miscalibration from 9% to 31%). Discrimination was similar in both models (C-index=0.740 for FRS, 0.747 for PCE). Refitting the published models using EHD did not substantially improve calibration or discrimination. We conclude that published cardiovascular risk models can be successfully applied to EHD to estimate cardiovascular risk; the FRS remains valid and is not obsolete; and model refitting does not meaningfully improve the accuracy of risk estimates. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

Evaluation of iodide deficiency in the lactating rat and pup using a biologically based dose-response model

EPA Science Inventory

A biologically-based dose response (BBDR) model for the hypothalamic-pituitary thyroid (BPT) axis in the lactating rat and nursing pup was developed to describe the perturbations caused by iodide deficiency on the HPT axis. Model calibrations, carried out by adjusting key model p...

Evaluation of iodide deficiency in the lactating rat and pup using a biologically based dose response (BBDR) Model***

EPA Science Inventory

A biologically-based dose response (BBDR) model for the hypothalamic-pituitary thyroid (HPT) axis in the lactating rat and nursing pup was developed to describe the perturbations caused by iodide deficiency on the 1-IPT axis. Model calibrations, carried out by adjusting key model...

Development of Long-term Datasets from Satellite BUV Instruments: The "Soft" Calibration Approach

NASA Technical Reports Server (NTRS)

Bhartia, Pawan K.; Taylor, Steven; Jaross, Glen

2005-01-01

The first BUV instrument was launched in April 1970 on NASA's Nimbus4 satellite. More than a dozen instruments, broadly based on the same principle, but using very different technologies, have been launched in the last 35 years on NASA, NOAA, Japanese and European satellites. In this paper we describe the basic principles of the "soft" calibration approach that we have successfully applied to the data from many of these instruments to produce a consistent long-term record of total ozone, ozone profile and aerosols. This approach is based on using accurate radiative transfer models and assumed/known properties of the atmosphere in ultraviolet to derive calibration parameters. Although the accuracy of the results inevitably depends upon how well the assumed atmospheric properties are known, the technique has several built-in cross- checks that improve the robustness of the method. To develop further confidence in the data the soft calibration technique can be combined with data collected from few well- calibrated ground-based instruments. We will use examples from past and present BUV instruments to show how the method works.

Technical note: Bayesian calibration of dynamic ruminant nutrition models.

PubMed

Reed, K F; Arhonditsis, G B; France, J; Kebreab, E

2016-08-01

Mechanistic models of ruminant digestion and metabolism have advanced our understanding of the processes underlying ruminant animal physiology. Deterministic modeling practices ignore the inherent variation within and among individual animals and thus have no way to assess how sources of error influence model outputs. We introduce Bayesian calibration of mathematical models to address the need for robust mechanistic modeling tools that can accommodate error analysis by remaining within the bounds of data-based parameter estimation. For the purpose of prediction, the Bayesian approach generates a posterior predictive distribution that represents the current estimate of the value of the response variable, taking into account both the uncertainty about the parameters and model residual variability. Predictions are expressed as probability distributions, thereby conveying significantly more information than point estimates in regard to uncertainty. Our study illustrates some of the technical advantages of Bayesian calibration and discusses the future perspectives in the context of animal nutrition modeling. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

ShakeMap Atlas 2.0: an improved suite of recent historical earthquake ShakeMaps for global hazard analyses and loss model calibration

USGS Publications Warehouse

Garcia, D.; Mah, R.T.; Johnson, K.L.; Hearne, M.G.; Marano, K.D.; Lin, K.-W.; Wald, D.J.

2012-01-01

We introduce the second version of the U.S. Geological Survey ShakeMap Atlas, which is an openly-available compilation of nearly 8,000 ShakeMaps of the most significant global earthquakes between 1973 and 2011. This revision of the Atlas includes: (1) a new version of the ShakeMap software that improves data usage and uncertainty estimations; (2) an updated earthquake source catalogue that includes regional locations and finite fault models; (3) a refined strategy to select prediction and conversion equations based on a new seismotectonic regionalization scheme; and (4) vastly more macroseismic intensity and ground-motion data from regional agencies All these changes make the new Atlas a self-consistent, calibrated ShakeMap catalogue that constitutes an invaluable resource for investigating near-source strong ground-motion, as well as for seismic hazard, scenario, risk, and loss-model development. To this end, the Atlas will provide a hazard base layer for PAGER loss calibration and for the Earthquake Consequences Database within the Global Earthquake Model initiative.

[New method of mixed gas infrared spectrum analysis based on SVM].

PubMed

Bai, Peng; Xie, Wen-Jun; Liu, Jun-Hua

2007-07-01

A new method of infrared spectrum analysis based on support vector machine (SVM) for mixture gas was proposed. The kernel function in SVM was used to map the seriously overlapping absorption spectrum into high-dimensional space, and after transformation, the high-dimensional data could be processed in the original space, so the regression calibration model was established, then the regression calibration model with was applied to analyze the concentration of component gas. Meanwhile it was proved that the regression calibration model with SVM also could be used for component recognition of mixture gas. The method was applied to the analysis of different data samples. Some factors such as scan interval, range of the wavelength, kernel function and penalty coefficient C that affect the model were discussed. Experimental results show that the component concentration maximal Mean AE is 0.132%, and the component recognition accuracy is higher than 94%. The problems of overlapping absorption spectrum, using the same method for qualitative and quantitative analysis, and limit number of training sample, were solved. The method could be used in other mixture gas infrared spectrum analyses, promising theoretic and application values.

Modeling complex aquifer systems: a case study in Baton Rouge, Louisiana (USA)

NASA Astrophysics Data System (ADS)

Pham, Hai V.; Tsai, Frank T.-C.

2017-05-01

This study targets two challenges in groundwater model development: grid generation and model calibration for aquifer systems that are fluvial in origin. Realistic hydrostratigraphy can be developed using a large quantity of well log data to capture the complexity of an aquifer system. However, generating valid groundwater model grids to be consistent with the complex hydrostratigraphy is non-trivial. Model calibration can also become intractable for groundwater models that intend to match the complex hydrostratigraphy. This study uses the Baton Rouge aquifer system, Louisiana (USA), to illustrate a technical need to cope with grid generation and model calibration issues. A grid generation technique is introduced based on indicator kriging to interpolate 583 wireline well logs in the Baton Rouge area to derive a hydrostratigraphic architecture with fine vertical discretization. Then, an upscaling procedure is developed to determine a groundwater model structure with 162 layers that captures facies geometry in the hydrostratigraphic architecture. To handle model calibration for such a large model, this study utilizes a derivative-free optimization method in parallel computing to complete parameter estimation in a few months. The constructed hydrostratigraphy indicates the Baton Rouge aquifer system is fluvial in origin. The calibration result indicates hydraulic conductivity for Miocene sands is higher than that for Pliocene to Holocene sands and indicates the Baton Rouge fault and the Denham Springs-Scotlandville fault to be low-permeability leaky aquifers. The modeling result shows significantly low groundwater level in the "2,000-foot" sand due to heavy pumping, indicating potential groundwater upward flow from the "2,400-foot" sand.

Space based optical staring sensor LOS determination and calibration using GCPs observation

NASA Astrophysics Data System (ADS)

Chen, Jun; An, Wei; Deng, Xinpu; Yang, Jungang; Sha, Zhichao

2016-10-01

Line of sight (LOS) attitude determination and calibration is the key prerequisite of tracking and location of targets in space based infrared (IR) surveillance systems (SBIRS) and the LOS determination and calibration of staring sensor is one of the difficulties. This paper provides a novel methodology for removing staring sensor bias through the use of Ground Control Points (GCPs) detected in the background field of the sensor. Based on researching the imaging model and characteristics of the staring sensor of SBIRS geostationary earth orbit part (GEO), the real time LOS attitude determination and calibration algorithm using landmark control point is proposed. The influential factors (including the thermal distortions error, assemble error, and so on) of staring sensor LOS attitude error are equivalent to bias angle of LOS attitude. By establishing the observation equation of GCPs and the state transition equation of bias angle, and using an extend Kalman filter (EKF), the real time estimation of bias angle and the high precision sensor LOS attitude determination and calibration are achieved. The simulation results show that the precision and timeliness of the proposed algorithm meet the request of target tracking and location process in space based infrared surveillance system.

Accounting for temporal variation in soil hydrological properties when simulating surface runoff on tilled plots

NASA Astrophysics Data System (ADS)

Chahinian, Nanée; Moussa, Roger; Andrieux, Patrick; Voltz, Marc

2006-07-01

Tillage operations are known to greatly influence local overland flow, infiltration and depressional storage by altering soil hydraulic properties and soil surface roughness. The calibration of runoff models for tilled fields is not identical to that of untilled fields, as it has to take into consideration the temporal variability of parameters due to the transient nature of surface crusts. In this paper, we seek the application of a rainfall-runoff model and the development of a calibration methodology to take into account the impact of tillage on overland flow simulation at the scale of a tilled plot (3240 m 2) located in southern France. The selected model couples the (Morel-Seytoux, H.J., 1978. Derivation of equations for variable rainfall infiltration. Water Resources Research. 14(4), 561-568). Infiltration equation to a transfer function based on the diffusive wave equation. The parameters to be calibrated are the hydraulic conductivity at natural saturation Ks, the surface detention Sd and the lag time ω. A two-step calibration procedure is presented. First, eleven rainfall-runoff events are calibrated individually and the variability of the calibrated parameters are analysed. The individually calibrated Ks values decrease monotonously according to the total amount of rainfall since tillage. No clear relationship is observed between the two parameters Sd and ω, and the date of tillage. However, the lag time ω increases inversely with the peakflow of the events. Fairly good agreement is observed between the simulated and measured hydrographs of the calibration set. Simple mathematical laws describing the evolution of Ks and ω are selected, while Sd is considered constant. The second step involves the collective calibration of the law of evolution of each parameter on the whole calibration set. This procedure is calibrated on 11 events and validated on ten runoff inducing and four non-runoff inducing rainfall events. The suggested calibration methodology seems robust and can be transposed to other gauged sites.

Suborbital Reusable Launch Vehicles as an Opportunity to Consolidate and Calibrate Ground Based and Satellite Instruments

NASA Astrophysics Data System (ADS)

Papadopoulos, K.

2014-12-01

XCOR Aerospace, a commercial space company, is planning to provide frequent, low cost access to near-Earth space on the Lynx suborbital Reusable Launch Vehicle (sRLV). Measurements in the external vacuum environment can be made and can launch from most runways on a limited lead time. Lynx can operate as a platform to perform suborbital in situ measurements and remote sensing to supplement models and simulations with new data points. These measurements can serve as a quantitative link to existing instruments and be used as a basis to calibrate detectors on spacecraft. Easier access to suborbital data can improve the longevity and cohesiveness of spacecraft and ground-based resources. A study of how these measurements can be made on Lynx sRLV will be presented. At the boundary between terrestrial and space weather, measurements from instruments on Lynx can help develop algorithms to optimize the consolidation of ground and satellite based data as well as assimilate global models with new data points. For example, current tides and the equatorial electrojet, essential to understanding the Thermosphere-Ionosphere system, can be measured in situ frequently and on short notice. Furthermore, a negative-ion spectrometer and a Faraday cup, can take measurements of the D-region ion composition. A differential GPS receiver can infer the spatial gradient of ionospheric electron density. Instruments and optics on spacecraft degrade over time, leading to calibration drift. Lynx can be a cost effective platform for deploying a reference instrument to calibrate satellites with a frequent and fast turnaround and a successful return of the instrument. A calibrated reference instrument on Lynx can make collocated observations as another instrument and corrections are made for the latter, thus ensuring data consistency and mission longevity. Aboard a sRLV, atmospheric conditions that distort remotely sensed data (ground and spacecraft based) can be measured in situ. Moreover, an active instrument can be deployed in a sRLV under a satellite track, and serve as a "standard candle" for instruments on satellites. Yearly calibrations of the Solar Extreme Ultraviolet Experiment (SEE) instrument aboard the TIMED orbiter using sounding rockets depict the necessity of calibrations and illustrates calibration frequency.

Groundwater-flow model of the northern High Plains aquifer in Colorado, Kansas, Nebraska, South Dakota, and Wyoming

USGS Publications Warehouse

Peterson, Steven M.; Flynn, Amanda T.; Traylor, Jonathan P.

2016-12-13

The High Plains aquifer is a nationally important water resource underlying about 175,000 square miles in parts of eight states: Colorado, Kansas, Oklahoma, Nebraska, New Mexico, South Dakota, Texas, and Wyoming. Droughts across much of the Northern High Plains from 2001 to 2007 have combined with recent (2004) legislative mandates to elevate concerns regarding future availability of groundwater and the need for additional information to support science-based water-resource management. To address these needs, the U.S. Geological Survey began the High Plains Groundwater Availability Study to provide a tool for water-resource managers and other stakeholders to assess the status and availability of groundwater resources.A transient groundwater-flow model was constructed using the U.S. Geological Survey modular three-dimensional finite-difference groundwater-flow model with Newton-Rhapson solver (MODFLOW–NWT). The model uses an orthogonal grid of 565 rows and 795 columns, and each grid cell measures 3,281 feet per side, with one variably thick vertical layer, simulated as unconfined. Groundwater flow was simulated for two distinct periods: (1) the period before substantial groundwater withdrawals, or before about 1940, and (2) the period of increasing groundwater withdrawals from May 1940 through April 2009. A soil-water-balance model was used to estimate recharge from precipitation and groundwater withdrawals for irrigation. The soil-water-balance model uses spatially distributed soil and landscape properties with daily weather data and estimated historical land-cover maps to calculate spatial and temporal variations in potential recharge. Mean annual recharge estimated for 1940–49, early in the history of groundwater development, and 2000–2009, late in the history of groundwater development, was 3.3 and 3.5 inches per year, respectively.Primary model calibration was completed using statistical techniques through parameter estimation using the parameter estimation suite of software with Tikhonov regularization. Calibration targets for the groundwater model included 343,067 groundwater levels measured in wells and 10,820 estimated monthly stream base flows at streamgages. A total of 1,312 parameters were adjusted during calibration to improve the match between calibration targets and simulated equivalents. Comparison of calibration targets to simulated equivalents indicated that, at the regional scale, the model correctly reproduced groundwater levels and stream base flows for 1940–2009. This comparison indicates that the model can be used to examine the likely response of the aquifer system to potential future stresses.Mean calibrated recharge for 1940–49 and 2000–2009 was smaller than that estimated with the soil-water-balance model. This indicated that although the general spatial patterns of recharge estimated with the soil-water-balance model were approximately correct at the regional scale of the Northern High Plains aquifer, the soil-water-balance model had overestimated recharge, and adjustments were needed to decrease recharge to improve the match of the groundwater model to calibration targets. The largest components of the simulated groundwater budgets were recharge from precipitation, recharge from canal seepage, outflows to evapotranspiration, and outflows to stream base flow. Simulated outflows to irrigation wells increased from 7 percent of total outflows in 1940–49 to 38 percent of 1970–79 total outflows and 49 percent of 2000–2009 total outflows.

Photogrammetry Applied to Wind Tunnel Testing

NASA Technical Reports Server (NTRS)

Liu, Tian-Shu; Cattafesta, L. N., III; Radeztsky, R. H.; Burner, A. W.

2000-01-01

In image-based measurements, quantitative image data must be mapped to three-dimensional object space. Analytical photogrammetric methods, which may be used to accomplish this task, are discussed from the viewpoint of experimental fluid dynamicists. The Direct Linear Transformation (DLT) for camera calibration, used in pressure sensitive paint, is summarized. An optimization method for camera calibration is developed that can be used to determine the camera calibration parameters, including those describing lens distortion, from a single image. Combined with the DLT method, this method allows a rapid and comprehensive in-situ camera calibration and therefore is particularly useful for quantitative flow visualization and other measurements such as model attitude and deformation in production wind tunnels. The paper also includes a brief description of typical photogrammetric applications to temperature- and pressure-sensitive paint measurements and model deformation measurements in wind tunnels.

Solving the robot-world, hand-eye(s) calibration problem with iterative methods

USDA-ARS?s Scientific Manuscript database

Robot-world, hand-eye calibration is the problem of determining the transformation between the robot end effector and a camera, as well as the transformation between the robot base and the world coordinate system. This relationship has been modeled as AX = ZB, where X and Z are unknown homogeneous ...

The Geostationary Lightning Mapper: Its Performance and Calibration

NASA Astrophysics Data System (ADS)

Christian, H. J., Jr.

2015-12-01

The Geostationary Lightning Mapper (GLM) has been developed to be an operational instrument on the GOES-R series of spacecraft. The GLM is a unique instrument, unlike other meteorological instruments, both in how it operates and in the information content that it provides. Instrumentally, it is an event detector, rather than an imager. While processing almost a billion pixels per second with 14 bits of resolution, the event detection process reduces the required telemetry bandwidth by almost 105, thus keeping the telemetry requirements modest and enabling efficient ground processing that leads to rapid data distribution to operational users. The GLM was designed to detect about 90 percent of the total lightning flashes within its almost hemispherical field of view. Based on laboratory calibration, we expect the on-orbit detection efficiency to be closer to 85%, making it the highest performing, large area coverage total lightning detector. It has a number of unique design features that will enable it have near uniform special resolution over most of its field of view and to operate with minimal impact on performance during solar eclipses. The GLM has no dedicated on-orbit calibration system, thus the ground-based calibration provides the bases for the predicted radiometric performance. A number of problems were encountered during the calibration of Flight Model 1. The issues arouse from GLM design features including its wide field of view, fast lens, the narrow-band interference filters located in both object and collimated space and the fact that the GLM is inherently a event detector yet the calibration procedures required both calibration of images and events. The GLM calibration techniques were based on those developed for the Lightning Imaging Sensor calibration, but there are enough differences between the sensors that the initial GLM calibration suggested that it is significantly more sensitive than its design parameters. The calibration discrepancies have been resolved and will be discussed. Absolute calibration will be verified on-orbit using vicarious cloud reflections. In addition to details of the GLM calibration, the presentation will address the unique design of the GLM, its features, capabilities and performance.

Effect of experimental design on the prediction performance of calibration models based on near-infrared spectroscopy for pharmaceutical applications.

PubMed

Bondi, Robert W; Igne, Benoît; Drennen, James K; Anderson, Carl A

2012-12-01

Near-infrared spectroscopy (NIRS) is a valuable tool in the pharmaceutical industry, presenting opportunities for online analyses to achieve real-time assessment of intermediates and finished dosage forms. The purpose of this work was to investigate the effect of experimental designs on prediction performance of quantitative models based on NIRS using a five-component formulation as a model system. The following experimental designs were evaluated: five-level, full factorial (5-L FF); three-level, full factorial (3-L FF); central composite; I-optimal; and D-optimal. The factors for all designs were acetaminophen content and the ratio of microcrystalline cellulose to lactose monohydrate. Other constituents included croscarmellose sodium and magnesium stearate (content remained constant). Partial least squares-based models were generated using data from individual experimental designs that related acetaminophen content to spectral data. The effect of each experimental design was evaluated by determining the statistical significance of the difference in bias and standard error of the prediction for that model's prediction performance. The calibration model derived from the I-optimal design had similar prediction performance as did the model derived from the 5-L FF design, despite containing 16 fewer design points. It also outperformed all other models estimated from designs with similar or fewer numbers of samples. This suggested that experimental-design selection for calibration-model development is critical, and optimum performance can be achieved with efficient experimental designs (i.e., optimal designs).

Enhancing the NOAA National Water Center WRF-Hydro model architecture to improve representation of the Midwest and Southwest CONUS climate regions

NASA Astrophysics Data System (ADS)

Lahmers, T. M.; Castro, C. L.; Gupta, H. V.; Gochis, D.; Dugger, A. L.; Smith, M.

2016-12-01

The NOAA National Water Model (NWM), which is based on the WRF-Hydro architecture, became operational in June of 2016 to produce streamflow forecasts nationwide. In order to improve the physical process representation of NWM/WRF-Hydro, a parameterized channel infiltration function is added to the Muskingum-Cunge channel routing scheme. Representation of transmission losses along streams was previously not supported by WRF-Hydro, even though most channels in the southwest CONUS have a high depth to groundwater, and are consequently a source for recharge throughout the region. The LSM, routing grid, baseflow bucket model, and channel parameters of the modified version of NWM/WRF-Hydro are calibrated using spatial regularization in selected basins in the Midwest and Southwest CONUS. WRF-Hydro is calibrated and tested in the Verde, San Pedro, Little Sioux, Nishnabotna, and Wapsipinicon basins. The model is forced with NCEP Stage-IV and NLDAS-2 precipitation for calibration, and the effects of the precipitation climatology, including extreme events, on model performance are considered. This work advances the regional performance of WRF-Hydro through process enhancement and calibration that is highly relevant for improving model fidelity in semi-arid climates.

SCS-CN based time-distributed sediment yield model

NASA Astrophysics Data System (ADS)

Tyagi, J. V.; Mishra, S. K.; Singh, Ranvir; Singh, V. P.

2008-05-01

SummaryA sediment yield model is developed to estimate the temporal rates of sediment yield from rainfall events on natural watersheds. The model utilizes the SCS-CN based infiltration model for computation of rainfall-excess rate, and the SCS-CN-inspired proportionality concept for computation of sediment-excess. For computation of sedimentographs, the sediment-excess is routed to the watershed outlet using a single linear reservoir technique. Analytical development of the model shows the ratio of the potential maximum erosion (A) to the potential maximum retention (S) of the SCS-CN method is constant for a watershed. The model is calibrated and validated on a number of events using the data of seven watersheds from India and the USA. Representative values of the A/S ratio computed for the watersheds from calibration are used for the validation of the model. The encouraging results of the proposed simple four parameter model exhibit its potential in field application.

Improved quantification of important beer quality parameters based on nonlinear calibration methods applied to FT-MIR spectra.

PubMed

Cernuda, Carlos; Lughofer, Edwin; Klein, Helmut; Forster, Clemens; Pawliczek, Marcin; Brandstetter, Markus

2017-01-01

During the production process of beer, it is of utmost importance to guarantee a high consistency of the beer quality. For instance, the bitterness is an essential quality parameter which has to be controlled within the specifications at the beginning of the production process in the unfermented beer (wort) as well as in final products such as beer and beer mix beverages. Nowadays, analytical techniques for quality control in beer production are mainly based on manual supervision, i.e., samples are taken from the process and analyzed in the laboratory. This typically requires significant lab technicians efforts for only a small fraction of samples to be analyzed, which leads to significant costs for beer breweries and companies. Fourier transform mid-infrared (FT-MIR) spectroscopy was used in combination with nonlinear multivariate calibration techniques to overcome (i) the time consuming off-line analyses in beer production and (ii) already known limitations of standard linear chemometric methods, like partial least squares (PLS), for important quality parameters Speers et al. (J I Brewing. 2003;109(3):229-235), Zhang et al. (J I Brewing. 2012;118(4):361-367) such as bitterness, citric acid, total acids, free amino nitrogen, final attenuation, or foam stability. The calibration models are established with enhanced nonlinear techniques based (i) on a new piece-wise linear version of PLS by employing fuzzy rules for local partitioning the latent variable space and (ii) on extensions of support vector regression variants (-PLSSVR and ν-PLSSVR), for overcoming high computation times in high-dimensional problems and time-intensive and inappropriate settings of the kernel parameters. Furthermore, we introduce a new model selection scheme based on bagged ensembles in order to improve robustness and thus predictive quality of the final models. The approaches are tested on real-world calibration data sets for wort and beer mix beverages, and successfully compared to linear methods, showing a clear out-performance in most cases and being able to meet the model quality requirements defined by the experts at the beer company. Figure Workflow for calibration of non-Linear model ensembles from FT-MIR spectra in beer production .

Simulation of the Shallow Ground-Water-Flow System near Grindstone Creek and the Community of New Post, Sawyer County, Wisconsin

USGS Publications Warehouse

Juckem, Paul F.; Hunt, Randall J.

2007-01-01

A two-dimensional, steady-state ground-water-flow model of Grindstone Creek, the New Post community, and the surrounding areas was developed using the analytic element computer code GFLOW. The parameter estimation code UCODE was used to obtain a best fit of the model to measured water levels and streamflows. The calibrated model was then used to simulate the effect of ground-water pumping on base flow in Grindstone Creek. Local refinements to the regional model were subsequently added in the New Post area, and local water-level data were used to evaluate the regional model calibration. The locally refined New Post model was also used to simulate the areal extent of capture for two existing water-supply wells and two possible replacement wells. Calibration of the regional Grindstone Creek simulation resulted in horizontal hydraulic conductivity values of 58.2 feet per day (ft/d) for the regional glacial and sandstone aquifer and 7.9 ft/d for glacial thrust-mass areas. Ground-water recharge in the calibrated regional model was 10.1 inches per year. Simulation of a golf-course irrigation well, located roughly 4,000 feet away from the creek, and pumping at 46 gallons per minute (0.10 cubic feet per second, ft3/s), reduced base flow in Grindstone Creek by 0.05 ft3/s, or 0.6 percent of the median base flow during water year 2003, compared to the calibrated model simulation without pumping. A simulation of peak pumping periods (347 gallons per minute or 0.77 ft3/s) reduced base flow in Grindstone Creek by 0.4 ft3/s (4.9 percent of the median base flow). Capture zones for existing and possible replacement wells delineated by the local New Post simulation extend from the well locations to an area south of the pumping well locations. Shallow crystalline bedrock, generally located south of the community, limits the extent of the aquifer and thus the southerly extent of the capture zones. Simulated steady-state pumping at a rate of 9,600 gallons per day (gal/d) from a possible replacement well near the Chippewa Flowage induced 70 gal/d of water from the flowage to enter the aquifer. Although no water-quality samples were collected from the Chippewa Flowage or the ground-water system, surface-water leakage into the ground-water system could potentially change the local water quality in the aquifer.

Calibration of micro-capacitance measurement system for thermal barrier coating testing

NASA Astrophysics Data System (ADS)

Ren, Yuan; Chen, Dixiang; Wan, Chengbiao; Tian, Wugang; Pan, Mengchun

2018-06-01

In order to comprehensively evaluate the thermal barrier coating system of an engine blade, an integrated planar sensor combining electromagnetic coils with planar capacitors is designed, in which the capacitance measurement accuracy of the planar capacitor is a key factor. The micro-capacitance measurement system is built based on an impedance analyzer. Because of the influence of non-ideal factors on the measuring system, there is an obvious difference between the measured value and the actual value. It is necessary to calibrate the measured results and eliminate the difference. In this paper, the measurement model of a planar capacitive sensor is established, and the relationship between the measured value and the actual value of capacitance is deduced. The model parameters are estimated with the least square method, and the calibration accuracy is evaluated with experiments under different dielectric conditions. The capacitance measurement error is reduced from 29% ˜ 46.5% to around 1% after calibration, which verifies the feasibility of the calibration method.

Parameter regionalization of a monthly water balance model for the conterminous United States

USGS Publications Warehouse

Bock, Andrew R.; Hay, Lauren E.; McCabe, Gregory J.; Markstrom, Steven L.; Atkinson, R. Dwight

2016-01-01

A parameter regionalization scheme to transfer parameter values from gaged to ungaged areas for a monthly water balance model (MWBM) was developed and tested for the conterminous United States (CONUS). The Fourier Amplitude Sensitivity Test, a global-sensitivity algorithm, was implemented on a MWBM to generate parameter sensitivities on a set of 109 951 hydrologic response units (HRUs) across the CONUS. The HRUs were grouped into 110 calibration regions based on similar parameter sensitivities. Subsequently, measured runoff from 1575 streamgages within the calibration regions were used to calibrate the MWBM parameters to produce parameter sets for each calibration region. Measured and simulated runoff at the 1575 streamgages showed good correspondence for the majority of the CONUS, with a median computed Nash–Sutcliffe efficiency coefficient of 0.76 over all streamgages. These methods maximize the use of available runoff information, resulting in a calibrated CONUS-wide application of the MWBM suitable for providing estimates of water availability at the HRU resolution for both gaged and ungaged areas of the CONUS.

A standardized method for the calibration of thermodynamic data for the prediction of gas chromatographic retention times.

PubMed

McGinitie, Teague M; Ebrahimi-Najafabadi, Heshmatollah; Harynuk, James J

2014-02-21

A new method for calibrating thermodynamic data to be used in the prediction of analyte retention times is presented. The method allows thermodynamic data collected on one column to be used in making predictions across columns of the same stationary phase but with varying geometries. This calibration is essential as slight variances in the column inner diameter and stationary phase film thickness between columns or as a column ages will adversely affect the accuracy of predictions. The calibration technique uses a Grob standard mixture along with a Nelder-Mead simplex algorithm and a previously developed model of GC retention times based on a three-parameter thermodynamic model to estimate both inner diameter and stationary phase film thickness. The calibration method is highly successful with the predicted retention times for a set of alkanes, ketones and alcohols having an average error of 1.6s across three columns. Copyright © 2014 Elsevier B.V. All rights reserved.

Parameter regionalization of a monthly water balance model for the conterminous United States

NASA Astrophysics Data System (ADS)

Bock, Andrew R.; Hay, Lauren E.; McCabe, Gregory J.; Markstrom, Steven L.; Atkinson, R. Dwight

2016-07-01

A parameter regionalization scheme to transfer parameter values from gaged to ungaged areas for a monthly water balance model (MWBM) was developed and tested for the conterminous United States (CONUS). The Fourier Amplitude Sensitivity Test, a global-sensitivity algorithm, was implemented on a MWBM to generate parameter sensitivities on a set of 109 951 hydrologic response units (HRUs) across the CONUS. The HRUs were grouped into 110 calibration regions based on similar parameter sensitivities. Subsequently, measured runoff from 1575 streamgages within the calibration regions were used to calibrate the MWBM parameters to produce parameter sets for each calibration region. Measured and simulated runoff at the 1575 streamgages showed good correspondence for the majority of the CONUS, with a median computed Nash-Sutcliffe efficiency coefficient of 0.76 over all streamgages. These methods maximize the use of available runoff information, resulting in a calibrated CONUS-wide application of the MWBM suitable for providing estimates of water availability at the HRU resolution for both gaged and ungaged areas of the CONUS.

A Critical Plane-energy Model for Multiaxial Fatigue Life Prediction of Homogeneous and Heterogeneous Materials

NASA Astrophysics Data System (ADS)

Wei, Haoyang

A new critical plane-energy model is proposed in this thesis for multiaxial fatigue life prediction of homogeneous and heterogeneous materials. Brief review of existing methods, especially on the critical plane-based and energy-based methods, are given first. Special focus is on one critical plane approach which has been shown to work for both brittle and ductile metals. The key idea is to automatically change the critical plane orientation with respect to different materials and stress states. One potential drawback of the developed model is that it needs an empirical calibration parameter for non-proportional multiaxial loadings since only the strain terms are used and the out-of-phase hardening cannot be considered. The energy-based model using the critical plane concept is proposed with help of the Mroz-Garud hardening rule to explicitly include the effect of non-proportional hardening under fatigue cyclic loadings. Thus, the empirical calibration for non-proportional loading is not needed since the out-of-phase hardening is naturally included in the stress calculation. The model predictions are compared with experimental data from open literature and it is shown the proposed model can work for both proportional and non-proportional loadings without the empirical calibration. Next, the model is extended for the fatigue analysis of heterogeneous materials integrating with finite element method. Fatigue crack initiation of representative volume of heterogeneous materials is analyzed using the developed critical plane-energy model and special focus is on the microstructure effect on the multiaxial fatigue life predictions. Several conclusions and future work is drawn based on the proposed study.

CrowdWater - Can people observe what models need?

NASA Astrophysics Data System (ADS)

van Meerveld, I. H. J.; Seibert, J.; Vis, M.; Etter, S.; Strobl, B.

2017-12-01

CrowdWater (www.crowdwater.ch) is a citizen science project that explores the usefulness of crowd-sourced data for hydrological model calibration and prediction. Hydrological models are usually calibrated based on observed streamflow data but it is likely easier for people to estimate relative stream water levels, such as the water level above or below a rock, than streamflow. Relative stream water levels may, therefore, be a more suitable variable for citizen science projects than streamflow. In order to test this assumption, we held surveys near seven different sized rivers in Switzerland and asked more than 450 volunteers to estimate the water level class based on a picture with a virtual staff gauge. The results show that people can generally estimate the relative water level well, although there were also a few outliers. We also asked the volunteers to estimate streamflow based on the stick method. The median estimated streamflow was close to the observed streamflow but the spread in the streamflow estimates was large and there were very large outliers, suggesting that crowd-based streamflow data is highly uncertain. In order to determine the potential value of water level class data for model calibration, we converted streamflow time series for 100 catchments in the US to stream level class time series and used these to calibrate the HBV model. The model was then validated using the streamflow data. The results of this modeling exercise show that stream level class data are useful for constraining a simple runoff model. Time series of only two stream level classes, e.g. above or below a rock in the stream, were already informative, especially when the class boundary was chosen towards the highest stream levels. There was hardly any improvement in model performance when more than five water level classes were used. This suggests that if crowd-sourced stream level observations are available for otherwise ungauged catchments, these data can be used to constrain a simple runoff model and to generate simulated streamflow time series from the level observations.

Clinical results from a noninvasive blood glucose monitor

NASA Astrophysics Data System (ADS)

Blank, Thomas B.; Ruchti, Timothy L.; Lorenz, Alex D.; Monfre, Stephen L.; Makarewicz, M. R.; Mattu, Mutua; Hazen, Kevin

2002-05-01

Non-invasive blood glucose monitoring has long been proposed as a means for advancing the management of diabetes through increased measurement and control. The use of a near-infrared, NIR, spectroscopy based methodology for noninvasive monitoring has been pursued by a number of groups. The accuracy of the NIR measurement technology is limited by challenges related to the instrumentation, the heterogeneity and time-variant nature of skin tissue, and the complexity of the calibration methodology. In this work, we discuss results from a clinical study that targeted the evaluation of individual calibrations for each subject based on a series of controlled calibration visits. While the customization of the calibrations to individuals was intended to reduce model complexity, the extensive requirements for each individual set of calibration data were difficult to achieve and required several days of measurement. Through the careful selection of a small subset of data from all samples collected on the 138 study participants in a previous study, we have developed a methodology for applying a single standard calibration to multiple persons. The standard calibrations have been applied to a plurality of individuals and shown to be persistent over periods greater than 24 weeks.

Stochastic Modeling of Overtime Occupancy and Its Application in Building Energy Simulation and Calibration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun, Kaiyu; Yan, Da; Hong, Tianzhen

2014-02-28

Overtime is a common phenomenon around the world. Overtime drives both internal heat gains from occupants, lighting and plug-loads, and HVAC operation during overtime periods. Overtime leads to longer occupancy hours and extended operation of building services systems beyond normal working hours, thus overtime impacts total building energy use. Current literature lacks methods to model overtime occupancy because overtime is stochastic in nature and varies by individual occupants and by time. To address this gap in the literature, this study aims to develop a new stochastic model based on the statistical analysis of measured overtime occupancy data from an officemore » building. A binomial distribution is used to represent the total number of occupants working overtime, while an exponential distribution is used to represent the duration of overtime periods. The overtime model is used to generate overtime occupancy schedules as an input to the energy model of a second office building. The measured and simulated cooling energy use during the overtime period is compared in order to validate the overtime model. A hybrid approach to energy model calibration is proposed and tested, which combines ASHRAE Guideline 14 for the calibration of the energy model during normal working hours, and a proposed KS test for the calibration of the energy model during overtime. The developed stochastic overtime model and the hybrid calibration approach can be used in building energy simulations to improve the accuracy of results, and better understand the characteristics of overtime in office buildings.« less

The cost of uniqueness in groundwater model calibration

NASA Astrophysics Data System (ADS)

Moore, Catherine; Doherty, John

2006-04-01

Calibration of a groundwater model requires that hydraulic properties be estimated throughout a model domain. This generally constitutes an underdetermined inverse problem, for which a solution can only be found when some kind of regularization device is included in the inversion process. Inclusion of regularization in the calibration process can be implicit, for example through the use of zones of constant parameter value, or explicit, for example through solution of a constrained minimization problem in which parameters are made to respect preferred values, or preferred relationships, to the degree necessary for a unique solution to be obtained. The "cost of uniqueness" is this: no matter which regularization methodology is employed, the inevitable consequence of its use is a loss of detail in the calibrated field. This, in turn, can lead to erroneous predictions made by a model that is ostensibly "well calibrated". Information made available as a by-product of the regularized inversion process allows the reasons for this loss of detail to be better understood. In particular, it is easily demonstrated that the estimated value for an hydraulic property at any point within a model domain is, in fact, a weighted average of the true hydraulic property over a much larger area. This averaging process causes loss of resolution in the estimated field. Where hydraulic conductivity is the hydraulic property being estimated, high averaging weights exist in areas that are strategically disposed with respect to measurement wells, while other areas may contribute very little to the estimated hydraulic conductivity at any point within the model domain, this possibly making the detection of hydraulic conductivity anomalies in these latter areas almost impossible. A study of the post-calibration parameter field covariance matrix allows further insights into the loss of system detail incurred through the calibration process to be gained. A comparison of pre- and post-calibration parameter covariance matrices shows that the latter often possess a much smaller spectral bandwidth than the former. It is also demonstrated that, as an inevitable consequence of the fact that a calibrated model cannot replicate every detail of the true system, model-to-measurement residuals can show a high degree of spatial correlation, a fact which must be taken into account when assessing these residuals either qualitatively, or quantitatively in the exploration of model predictive uncertainty. These principles are demonstrated using a synthetic case in which spatial parameter definition is based on pilot points, and calibration is implemented using both zones of piecewise constancy and constrained minimization regularization.

Calibration, event reconstruction, data analysis and limits calculation for the LUX dark matter experiment

DOE PAGES

Akerib, DS; Alsum, S; Araújo, HM; ...

2018-01-05

The LUX experiment has performed searches for dark matter particles scattering elastically on xenon nuclei, leading to stringent upper limits on the nuclear scattering cross sections for dark matter. Here, for results derived frommore » $${1.4}\\times 10^{4}\\;\\mathrm{kg\\,days}$$ of target exposure in 2013, details of the calibration, event-reconstruction, modeling, and statistical tests that underlie the results are presented. Detector performance is characterized, including measured efficiencies, stability of response, position resolution, and discrimination between electron- and nuclear-recoil populations. Models are developed for the drift field, optical properties, background populations, the electron- and nuclear-recoil responses, and the absolute rate of low-energy background events. Innovations in the analysis include in situ measurement of the photomultipliers' response to xenon scintillation photons, verification of fiducial mass with a low-energy internal calibration source, and new empirical models for low-energy signal yield based on large-sample, in situ calibrations.« less

Calibration, event reconstruction, data analysis, and limit calculation for the LUX dark matter experiment

DOE PAGES

Akerib, D. S.; Alsum, S.; Araújo, H. M.; ...

2018-05-31

Here, the LUX experiment has performed searches for dark matter particles scattering elastically on xenon nuclei, leading to stringent upper limits on the nuclear scattering cross sections for dark matter. Here, for results derived frommore » $${1.4}\\times 10^{4}\\;\\mathrm{kg\\,days}$$ of target exposure in 2013, details of the calibration, event-reconstruction, modeling, and statistical tests that underlie the results are presented. Detector performance is characterized, including measured efficiencies, stability of response, position resolution, and discrimination between electron- and nuclear-recoil populations. Models are developed for the drift field, optical properties, background populations, the electron- and nuclear-recoil responses, and the absolute rate of low-energy background events. Innovations in the analysis include in situ measurement of the photomultipliers' response to xenon scintillation photons, verification of fiducial mass with a low-energy internal calibration source, and new empirical models for low-energy signal yield based on large-sample, in situ calibrations.« less

Predictive process simulation of cryogenic implants for leading edge transistor design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gossmann, Hans-Joachim; Zographos, Nikolas; Park, Hugh

2012-11-06

Two cryogenic implant TCAD-modules have been developed: (i) A continuum-based compact model targeted towards a TCAD production environment calibrated against an extensive data-set for all common dopants. Ion-specific calibration parameters related to damage generation and dynamic annealing were used and resulted in excellent fits to the calibration data-set. (ii) A Kinetic Monte Carlo (kMC) model including the full time dependence of ion-exposure that a particular spot on the wafer experiences, as well as the resulting temperature vs. time profile of this spot. It was calibrated by adjusting damage generation and dynamic annealing parameters. The kMC simulations clearly demonstrate the importancemore » of the time-structure of the beam for the amorphization process: Assuming an average dose-rate does not capture all of the physics and may lead to incorrect conclusions. The model enables optimization of the amorphization process through tool parameters such as scan speed or beam height.« less

Calibration, event reconstruction, data analysis, and limit calculation for the LUX dark matter experiment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Akerib, D. S.; Alsum, S.; Araújo, H. M.

Here, the LUX experiment has performed searches for dark matter particles scattering elastically on xenon nuclei, leading to stringent upper limits on the nuclear scattering cross sections for dark matter. Here, for results derived frommore » $${1.4}\\times 10^{4}\\;\\mathrm{kg\\,days}$$ of target exposure in 2013, details of the calibration, event-reconstruction, modeling, and statistical tests that underlie the results are presented. Detector performance is characterized, including measured efficiencies, stability of response, position resolution, and discrimination between electron- and nuclear-recoil populations. Models are developed for the drift field, optical properties, background populations, the electron- and nuclear-recoil responses, and the absolute rate of low-energy background events. Innovations in the analysis include in situ measurement of the photomultipliers' response to xenon scintillation photons, verification of fiducial mass with a low-energy internal calibration source, and new empirical models for low-energy signal yield based on large-sample, in situ calibrations.« less

Calibrating the Spatiotemporal Root Density Distribution for Macroscopic Water Uptake Models Using Tikhonov Regularization

NASA Astrophysics Data System (ADS)

Li, N.; Yue, X. Y.

2018-03-01

Macroscopic root water uptake models proportional to a root density distribution function (RDDF) are most commonly used to model water uptake by plants. As the water uptake is difficult and labor intensive to measure, these models are often calibrated by inverse modeling. Most previous inversion studies assume RDDF to be constant with depth and time or dependent on only depth for simplification. However, under field conditions, this function varies with type of soil and root growth and thus changes with both depth and time. This study proposes an inverse method to calibrate both spatially and temporally varying RDDF in unsaturated water flow modeling. To overcome the difficulty imposed by the ill-posedness, the calibration is formulated as an optimization problem in the framework of the Tikhonov regularization theory, adding additional constraint to the objective function. Then the formulated nonlinear optimization problem is numerically solved with an efficient algorithm on the basis of the finite element method. The advantage of our method is that the inverse problem is translated into a Tikhonov regularization functional minimization problem and then solved based on the variational construction, which circumvents the computational complexity in calculating the sensitivity matrix involved in many derivative-based parameter estimation approaches (e.g., Levenberg-Marquardt optimization). Moreover, the proposed method features optimization of RDDF without any prior form, which is applicable to a more general root water uptake model. Numerical examples are performed to illustrate the applicability and effectiveness of the proposed method. Finally, discussions on the stability and extension of this method are presented.

A novel method for determining calibration and behavior of PVDF ultrasonic hydrophone probes in the frequency range up to 100 MHz.

PubMed

Bleeker, H J; Lewin, P A

2000-01-01

A new calibration technique for PVDF ultrasonic hydrophone probes is described. Current implementation of the technique allows determination of hydrophone frequency response between 2 and 100 MHz and is based on the comparison of theoretically predicted and experimentally determined pressure-time waveforms produced by a focused, circular source. The simulation model was derived from the time domain algorithm that solves the non linear KZK (Khokhlov-Zabolotskaya-Kuznetsov) equation describing acoustic wave propagation. The calibration technique data were experimentally verified using independent calibration procedures in the frequency range from 2 to 40 MHz using a combined time delay spectrometry and reciprocity approach or calibration data provided by the National Physical Laboratory (NPL), UK. The results of verification indicated good agreement between the results obtained using KZK and the above-mentioned independent calibration techniques from 2 to 40 MHz, with the maximum discrepancy of 18% at 30 MHz. The frequency responses obtained using different hydrophone designs, including several membrane and needle probes, are presented, and it is shown that the technique developed provides a desirable tool for independent verification of primary calibration techniques such as those based on optical interferometry. Fundamental limitations of the presented calibration method are also examined.

Parallel computing for automated model calibration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burke, John S.; Danielson, Gary R.; Schulz, Douglas A.

2002-07-29

Natural resources model calibration is a significant burden on computing and staff resources in modeling efforts. Most assessments must consider multiple calibration objectives (for example magnitude and timing of stream flow peak). An automated calibration process that allows real time updating of data/models, allowing scientists to focus effort on improving models is needed. We are in the process of building a fully featured multi objective calibration tool capable of processing multiple models cheaply and efficiently using null cycle computing. Our parallel processing and calibration software routines have been generically, but our focus has been on natural resources model calibration. Somore » far, the natural resources models have been friendly to parallel calibration efforts in that they require no inter-process communication, only need a small amount of input data and only output a small amount of statistical information for each calibration run. A typical auto calibration run might involve running a model 10,000 times with a variety of input parameters and summary statistical output. In the past model calibration has been done against individual models for each data set. The individual model runs are relatively fast, ranging from seconds to minutes. The process was run on a single computer using a simple iterative process. We have completed two Auto Calibration prototypes and are currently designing a more feature rich tool. Our prototypes have focused on running the calibration in a distributed computing cross platform environment. They allow incorporation of?smart? calibration parameter generation (using artificial intelligence processing techniques). Null cycle computing similar to SETI@Home has also been a focus of our efforts. This paper details the design of the latest prototype and discusses our plans for the next revision of the software.« less

Development of a generic auto-calibration package for regional ecological modeling and application in the Central Plains of the United States

USGS Publications Warehouse

Wu, Yiping; Liu, Shuguang; Li, Zhengpeng; Dahal, Devendra; Young, Claudia J.; Schmidt, Gail L.; Liu, Jinxun; Davis, Brian; Sohl, Terry L.; Werner, Jeremy M.; Oeding, Jennifer

2014-01-01

Process-oriented ecological models are frequently used for predicting potential impacts of global changes such as climate and land-cover changes, which can be useful for policy making. It is critical but challenging to automatically derive optimal parameter values at different scales, especially at regional scale, and validate the model performance. In this study, we developed an automatic calibration (auto-calibration) function for a well-established biogeochemical model—the General Ensemble Biogeochemical Modeling System (GEMS)-Erosion Deposition Carbon Model (EDCM)—using data assimilation technique: the Shuffled Complex Evolution algorithm and a model-inversion R package—Flexible Modeling Environment (FME). The new functionality can support multi-parameter and multi-objective auto-calibration of EDCM at the both pixel and regional levels. We also developed a post-processing procedure for GEMS to provide options to save the pixel-based or aggregated county-land cover specific parameter values for subsequent simulations. In our case study, we successfully applied the updated model (EDCM-Auto) for a single crop pixel with a corn–wheat rotation and a large ecological region (Level II)—Central USA Plains. The evaluation results indicate that EDCM-Auto is applicable at multiple scales and is capable to handle land cover changes (e.g., crop rotations). The model also performs well in capturing the spatial pattern of grain yield production for crops and net primary production (NPP) for other ecosystems across the region, which is a good example for implementing calibration and validation of ecological models with readily available survey data (grain yield) and remote sensing data (NPP) at regional and national levels. The developed platform for auto-calibration can be readily expanded to incorporate other model inversion algorithms and potential R packages, and also be applied to other ecological models.

A Data-driven Approach for Forecasting Next-day River Discharge

NASA Astrophysics Data System (ADS)

Sharif, H. O.; Billah, K. S.

2017-12-01

This study focuses on evaluating the performance of the Soil and Water Assessment Tool (SWAT) eco-hydrological model, a simple Auto-Regressive with eXogenous input (ARX) model, and a Gene expression programming (GEP)-based model in one-day-ahead forecasting of discharge of a subtropical basin (the upper Kentucky River Basin). The three models were calibrated with daily flow at the US Geological Survey (USGS) stream gauging station not affected by flow regulation for the period of 2002-2005. The calibrated models were then validated at the same gauging station as well as another USGS gauge 88 km downstream for the period of 2008-2010. The results suggest that simple models outperform a sophisticated hydrological model with GEP having the advantage of being able to generate functional relationships that allow scientific investigation of the complex nonlinear interrelationships among input variables. Unlike SWAT, GEP, and to some extent, ARX are less sensitive to the length of the calibration time series and do not require a spin-up period.

Application of the US Geological Survey's precipitation-runoff modeling system to Williams Draw and Bush Draw basins, Jackson County, Colorado

USGS Publications Warehouse

Kuhn, Gerhard

1988-01-01

The U.S. Geological Survey 's precipitation-runoff modeling system was calibrated for this study by using daily streamflow data for April through September, 1980 and 1981, from the Williams Draw basin in Jackson County, Colorado. The calibrated model then was verified by using daily streamflow data for April through September, 1982 and 1983. Transferability of the model was tested by application to adjoining Bush Draw basin by using daily streamflow data for April through September, 1981 through 1983. Four model parameters were optimized in the calibration: (1) BST, base air temperature used to determine the form of precipitation (rain, snow, or a mixture); (2) SMAX, maximum available water-holding capacity of the soil zone; (3) TRNCF, transmission coefficient for the vegetation canopy over the snowpack; and (4) DSCOR, daily precipitation correction factor for snow. For calibration and verification, volume and timing of simulated streamflow were reasonably close to recorded streamflow; differences were least during years that had considerable snowpack accumulation and were most during years that had minimal or no snowpack accumulation. Calibration and optimization of parameters were facilitated by snowpack water-equivalent data. Application of the model to Bush Draw basin to test for transferability indicated inaccurate results in simulation of streamflow volume. Weighted values of SMAX, TRNCF, and DSCOR from the calibration basin were used for Bush Draw. The inadequate results obtained by use of weighted parameters indicate that snowpack water-equivalent data are needed for successful application of the precipitation-runoff modeling system in this area, because frequent windy conditions cause variations in snowpack accumulation. (USGS)

Calibration of micromechanical parameters for DEM simulations by using the particle filter

NASA Astrophysics Data System (ADS)

Cheng, Hongyang; Shuku, Takayuki; Thoeni, Klaus; Yamamoto, Haruyuki

2017-06-01

The calibration of DEM models is typically accomplished by trail and error. However, the procedure lacks of objectivity and has several uncertainties. To deal with these issues, the particle filter is employed as a novel approach to calibrate DEM models of granular soils. The posterior probability distribution of the microparameters that give numerical results in good agreement with the experimental response of a Toyoura sand specimen is approximated by independent model trajectories, referred as `particles', based on Monte Carlo sampling. The soil specimen is modeled by polydisperse packings with different numbers of spherical grains. Prepared in `stress-free' states, the packings are subjected to triaxial quasistatic loading. Given the experimental data, the posterior probability distribution is incrementally updated, until convergence is reached. The resulting `particles' with higher weights are identified as the calibration results. The evolutions of the weighted averages and posterior probability distribution of the micro-parameters are plotted to show the advantage of using a particle filter, i.e., multiple solutions are identified for each parameter with known probabilities of reproducing the experimental response.

Hydrodynamic modelling of a tidal delta wetland using an enhanced quasi-2D model

NASA Astrophysics Data System (ADS)

Wester, Sjoerd J.; Grimson, Rafael; Minotti, Priscilla G.; Booija, Martijn J.; Brugnach, Marcela

2018-04-01

Knowledge about the hydrological regime of wetlands is key to understand their physical and biological properties. Modelling hydrological and hydrodynamic processes within a wetland is therefore becoming increasingly important. 3D models have successfully modelled wetland dynamics but depend on very detailed bathymetry and land topography. Many 1D and 2D models of river deltas highly simplify the interaction between the river and wetland area or simply neglect the wetland area. This study proposes an enhanced quasi-2D modelling strategy that captures the interaction between river discharge and moon tides and the resulting hydrodynamics, while using the scarce data available. The water flow equations are discretised with an interconnected irregular cell scheme, in which a simplification of the 1D Saint-Venant equations is used to define the water flow between cells. The spatial structure of wetlands is based on the ecogeomorphology in complex estuarine deltas. The islands within the delta are modelled with levee cells, creek cells and an interior cell representing a shallow marsh wetland. The model is calibrated for an average year and the model performance is evaluated for another average year and additionally an extreme dry three-month period and an extreme wet three-month period. The calibration and evaluation are done based on two water level measurement stations and two discharge measurement stations, all located in the main rivers. Additional calibration is carried out with field water level measurements in a wetland area. Accurate simulations are obtained for both calibration and evaluation with high correlations between observed and simulated water levels and simulated discharges in the same order of magnitude as observed discharges. Calibration against field measurements showed that the model can successfully simulate the overflow mechanism in wetland areas. A sensitivity analysis for several wetland parameters showed that these parameters are all influencing the water level fluctuation within the wetlands to varying degrees. The enhanced quasi-2D model has the potential to accurately simulate river and wetland dynamics for large wetland areas and help to understand their hydrodynamics.

A Nonlinear Calibration Algorithm Based on Harmonic Decomposition for Two-Axis Fluxgate Sensors

PubMed Central

Liu, Shibin

2018-01-01

Nonlinearity is a prominent limitation to the calibration performance for two-axis fluxgate sensors. In this paper, a novel nonlinear calibration algorithm taking into account the nonlinearity of errors is proposed. In order to establish the nonlinear calibration model, the combined effort of all time-invariant errors is analyzed in detail, and then harmonic decomposition method is utilized to estimate the compensation coefficients. Meanwhile, the proposed nonlinear calibration algorithm is validated and compared with a classical calibration algorithm by experiments. The experimental results show that, after the nonlinear calibration, the maximum deviation of magnetic field magnitude is decreased from 1302 nT to 30 nT, which is smaller than 81 nT after the classical calibration. Furthermore, for the two-axis fluxgate sensor used as magnetic compass, the maximum error of heading is corrected from 1.86° to 0.07°, which is approximately 11% in contrast with 0.62° after the classical calibration. The results suggest an effective way to improve the calibration performance of two-axis fluxgate sensors. PMID:29789448

Using large hydrological datasets to create a robust, physically based, spatially distributed model for Great Britain

NASA Astrophysics Data System (ADS)

Lewis, Elizabeth; Kilsby, Chris; Fowler, Hayley

2014-05-01

The impact of climate change on hydrological systems requires further quantification in order to inform water management. This study intends to conduct such analysis using hydrological models. Such models are of varying forms, of which conceptual, lumped parameter models and physically-based models are two important types. The majority of hydrological studies use conceptual models calibrated against measured river flow time series in order to represent catchment behaviour. This method often shows impressive results for specific problems in gauged catchments. However, the results may not be robust under non-stationary conditions such as climate change, as physical processes and relationships amenable to change are not accounted for explicitly. Moreover, conceptual models are less readily applicable to ungauged catchments, in which hydrological predictions are also required. As such, the physically based, spatially distributed model SHETRAN is used in this study to develop a robust and reliable framework for modelling historic and future behaviour of gauged and ungauged catchments across the whole of Great Britain. In order to achieve this, a large array of data completely covering Great Britain for the period 1960-2006 has been collated and efficiently stored ready for model input. The data processed include a DEM, rainfall, PE and maps of geology, soil and land cover. A desire to make the modelling system easy for others to work with led to the development of a user-friendly graphical interface. This allows non-experts to set up and run a catchment model in a few seconds, a process that can normally take weeks or months. The quality and reliability of the extensive dataset for modelling hydrological processes has also been evaluated. One aspect of this has been an assessment of error and uncertainty in rainfall input data, as well as the effects of temporal resolution in precipitation inputs on model calibration. SHETRAN has been updated to accept gridded rainfall inputs, and UKCP09 gridded daily rainfall data has been disaggregated using hourly records to analyse the implications of using realistic sub-daily variability. Furthermore, the development of a comprehensive dataset and computationally efficient means of setting up and running catchment models has allowed for examination of how a robust parameter scheme may be derived. This analysis has been based on collective parameterisation of multiple catchments in contrasting hydrological settings and subject to varied processes. 350 gauged catchments all over the UK have been simulated, and a robust set of parameters is being sought by examining the full range of hydrological processes and calibrating to a highly diverse flow data series. The modelling system will be used to generate flow time series based on historical input data and also downscaled Regional Climate Model (RCM) forecasts using the UKCP09 Weather Generator. This will allow for analysis of flow frequency and associated future changes, which cannot be determined from the instrumental record or from lumped parameter model outputs calibrated only to historical catchment behaviour. This work will be based on the existing and functional modelling system described following some further improvements to calibration, particularly regarding simulation of groundwater-dominated catchments.

[Application of near infrared spectroscopy combined with particle swarm optimization based least square support vactor machine to rapid quantitative analysis of Corni Fructus].

PubMed

Liu, Xue-song; Sun, Fen-fang; Jin, Ye; Wu, Yong-jiang; Gu, Zhi-xin; Zhu, Li; Yan, Dong-lan

2015-12-01

A novel method was developed for the rapid determination of multi-indicators in corni fructus by means of near infrared (NIR) spectroscopy. Particle swarm optimization (PSO) based least squares support vector machine was investigated to increase the levels of quality control. The calibration models of moisture, extractum, morroniside and loganin were established using the PSO-LS-SVM algorithm. The performance of PSO-LS-SVM models was compared with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN). The calibration and validation results of PSO-LS-SVM were superior to both PLS and BP-ANN. For PSO-LS-SVM models, the correlation coefficients (r) of calibrations were all above 0.942. The optimal prediction results were also achieved by PSO-LS-SVM models with the RMSEP (root mean square error of prediction) and RSEP (relative standard errors of prediction) less than 1.176 and 15.5% respectively. The results suggest that PSO-LS-SVM algorithm has a good model performance and high prediction accuracy. NIR has a potential value for rapid determination of multi-indicators in Corni Fructus.

On-Ground Processing of Yaogan-24 Remote Sensing Satellite Attitude Data and Verification Using Geometric Field Calibration

PubMed Central

Wang, Mi; Fan, Chengcheng; Yang, Bo; Jin, Shuying; Pan, Jun

2016-01-01

Satellite attitude accuracy is an important factor affecting the geometric processing accuracy of high-resolution optical satellite imagery. To address the problem whereby the accuracy of the Yaogan-24 remote sensing satellite’s on-board attitude data processing is not high enough and thus cannot meet its image geometry processing requirements, we developed an approach involving on-ground attitude data processing and digital orthophoto (DOM) and the digital elevation model (DEM) verification of a geometric calibration field. The approach focuses on three modules: on-ground processing based on bidirectional filter, overall weighted smoothing and fitting, and evaluation in the geometric calibration field. Our experimental results demonstrate that the proposed on-ground processing method is both robust and feasible, which ensures the reliability of the observation data quality, convergence and stability of the parameter estimation model. In addition, both the Euler angle and quaternion could be used to build a mathematical fitting model, while the orthogonal polynomial fitting model is more suitable for modeling the attitude parameter. Furthermore, compared to the image geometric processing results based on on-board attitude data, the image uncontrolled and relative geometric positioning result accuracy can be increased by about 50%. PMID:27483287

Crop physiology calibration in the CLM

DOE PAGES

Bilionis, I.; Drewniak, B. A.; Constantinescu, E. M.

2015-04-15

Farming is using more of the land surface, as population increases and agriculture is increasingly applied for non-nutritional purposes such as biofuel production. This agricultural expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, the Community Land Model (CLM) has been augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. CLM-Crop development used measurementsmore » of gross primary productivity (GPP) and net ecosystem exchange (NEE) from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. In this paper, we calibrate these parameters for one crop type, soybean, in order to provide a faithful projection in terms of both plant development and net carbon exchange. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC). The model showed significant improvement of crop productivity with the new calibrated parameters. We demonstrate that the calibrated parameters are applicable across alternative years and different sites.« less

Crop physiology calibration in the CLM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bilionis, I.; Drewniak, B. A.; Constantinescu, E. M.

Farming is using more of the land surface, as population increases and agriculture is increasingly applied for non-nutritional purposes such as biofuel production. This agricultural expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, the Community Land Model (CLM) has been augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. CLM-Crop development used measurementsmore » of gross primary productivity (GPP) and net ecosystem exchange (NEE) from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. In this paper, we calibrate these parameters for one crop type, soybean, in order to provide a faithful projection in terms of both plant development and net carbon exchange. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC). The model showed significant improvement of crop productivity with the new calibrated parameters. We demonstrate that the calibrated parameters are applicable across alternative years and different sites.« less

Evaluation of the AnnAGNPS Model for Predicting Runoff and Nutrient Export in a Typical Small Watershed in the Hilly Region of Taihu Lake.

PubMed

Luo, Chuan; Li, Zhaofu; Li, Hengpeng; Chen, Xiaomin

2015-09-02

The application of hydrological and water quality models is an efficient approach to better understand the processes of environmental deterioration. This study evaluated the ability of the Annualized Agricultural Non-Point Source (AnnAGNPS) model to predict runoff, total nitrogen (TN) and total phosphorus (TP) loading in a typical small watershed of a hilly region near Taihu Lake, China. Runoff was calibrated and validated at both an annual and monthly scale, and parameter sensitivity analysis was performed for TN and TP before the two water quality components were calibrated. The results showed that the model satisfactorily simulated runoff at annual and monthly scales, both during calibration and validation processes. Additionally, results of parameter sensitivity analysis showed that the parameters Fertilizer rate, Fertilizer organic, Canopy cover and Fertilizer inorganic were more sensitive to TN output. In terms of TP, the parameters Residue mass ratio, Fertilizer rate, Fertilizer inorganic and Canopy cover were the most sensitive. Based on these sensitive parameters, calibration was performed. TN loading produced satisfactory results for both the calibration and validation processes, whereas the performance of TP loading was slightly poor. The simulation results showed that AnnAGNPS has the potential to be used as a valuable tool for the planning and management of watersheds.

Uncertainty analyses of the calibrated parameter values of a water quality model

NASA Astrophysics Data System (ADS)

Rode, M.; Suhr, U.; Lindenschmidt, K.-E.

2003-04-01

For river basin management water quality models are increasingly used for the analysis and evaluation of different management measures. However substantial uncertainties exist in parameter values depending on the available calibration data. In this paper an uncertainty analysis for a water quality model is presented, which considers the impact of available model calibration data and the variance of input variables. The investigation was conducted based on four extensive flowtime related longitudinal surveys in the River Elbe in the years 1996 to 1999 with varying discharges and seasonal conditions. For the model calculations the deterministic model QSIM of the BfG (Germany) was used. QSIM is a one dimensional water quality model and uses standard algorithms for hydrodynamics and phytoplankton dynamics in running waters, e.g. Michaelis Menten/Monod kinetics, which are used in a wide range of models. The multi-objective calibration of the model was carried out with the nonlinear parameter estimator PEST. The results show that for individual flow time related measuring surveys very good agreements between model calculation and measured values can be obtained. If these parameters are applied to deviating boundary conditions, substantial errors in model calculation can occur. These uncertainties can be decreased with an increased calibration database. More reliable model parameters can be identified, which supply reasonable results for broader boundary conditions. The extension of the application of the parameter set on a wider range of water quality conditions leads to a slight reduction of the model precision for the specific water quality situation. Moreover the investigations show that highly variable water quality variables like the algal biomass always allow a smaller forecast accuracy than variables with lower coefficients of variation like e.g. nitrate.

The MeqTrees software system and its use for third-generation calibration of radio interferometers

NASA Astrophysics Data System (ADS)

Noordam, J. E.; Smirnov, O. M.

2010-12-01

Context. The formulation of the radio interferometer measurement equation (RIME) for a generic radio telescope by Hamaker et al. has provided us with an elegant mathematical apparatus for better understanding, simulation and calibration of existing and future instruments. The calibration of the new radio telescopes (LOFAR, SKA) would be unthinkable without the RIME formalism, and new software to exploit it. Aims: The MeqTrees software system is designed to implement numerical models, and to solve for arbitrary subsets of their parameters. It may be applied to many problems, but was originally geared towards implementing Measurement Equations in radio astronomy for the purposes of simulation and calibration. The technical goal of MeqTrees is to provide a tool for rapid implementation of such models, while offering performance comparable to hand-written code. We are also pursuing the wider goal of increasing the rate of evolution of radio astronomical software, by offering a tool that facilitates rapid experimentation, and exchange of ideas (and scripts). Methods: MeqTrees is implemented as a Python-based front-end called the meqbrowser, and an efficient (C++-based) computational back-end called the meqserver. Numerical models are defined on the front-end via a Python-based Tree Definition Language (TDL), then rapidly executed on the back-end. The use of TDL facilitates an extremely short turn-around time (hours rather than weeks or months) for experimentation with new ideas. This is also helped by unprecedented visualization capabilities for all final and intermediate results. A flexible data model and a number of important optimizations in the back-end ensures that the numerical performance is comparable to that of hand-written code. Results: MeqTrees is already widely used as the simulation tool for new instruments (LOFAR, SKA) and technologies (focal plane arrays). It has demonstrated that it can achieve a noise-limited dynamic range in excess of a million, on WSRT data. It is the only package that is specifically designed to handle what we propose to call third-generation calibration (3GC), which is needed for the new generation of giant radio telescopes, but can also improve the calibration of existing instruments.

Pattern sampling for etch model calibration

NASA Astrophysics Data System (ADS)

Weisbuch, François; Lutich, Andrey; Schatz, Jirka

2017-06-01

Successful patterning requires good control of the photolithography and etch processes. While compact litho models, mainly based on rigorous physics, can predict very well the contours printed in photoresist, pure empirical etch models are less accurate and more unstable. Compact etch models are based on geometrical kernels to compute the litho-etch biases that measure the distance between litho and etch contours. The definition of the kernels as well as the choice of calibration patterns is critical to get a robust etch model. This work proposes to define a set of independent and anisotropic etch kernels -"internal, external, curvature, Gaussian, z_profile" - designed to capture the finest details of the resist contours and represent precisely any etch bias. By evaluating the etch kernels on various structures it is possible to map their etch signatures in a multi-dimensional space and analyze them to find an optimal sampling of structures to train an etch model. The method was specifically applied to a contact layer containing many different geometries and was used to successfully select appropriate calibration structures. The proposed kernels evaluated on these structures were combined to train an etch model significantly better than the standard one. We also illustrate the usage of the specific kernel "z_profile" which adds a third dimension to the description of the resist profile.

Two graphical user interfaces for managing and analyzing MODFLOW groundwater-model scenarios

USGS Publications Warehouse

Banta, Edward R.

2014-01-01

Scenario Manager and Scenario Analyzer are graphical user interfaces that facilitate the use of calibrated, MODFLOW-based groundwater models for investigating possible responses to proposed stresses on a groundwater system. Scenario Manager allows a user, starting with a calibrated model, to design and run model scenarios by adding or modifying stresses simulated by the model. Scenario Analyzer facilitates the process of extracting data from model output and preparing such display elements as maps, charts, and tables. Both programs are designed for users who are familiar with the science on which groundwater modeling is based but who may not have a groundwater modeler’s expertise in building and calibrating a groundwater model from start to finish. With Scenario Manager, the user can manipulate model input to simulate withdrawal or injection wells, time-variant specified hydraulic heads, recharge, and such surface-water features as rivers and canals. Input for stresses to be simulated comes from user-provided geographic information system files and time-series data files. A Scenario Manager project can contain multiple scenarios and is self-documenting. Scenario Analyzer can be used to analyze output from any MODFLOW-based model; it is not limited to use with scenarios generated by Scenario Manager. Model-simulated values of hydraulic head, drawdown, solute concentration, and cell-by-cell flow rates can be presented in display elements. Map data can be represented as lines of equal value (contours) or as a gradated color fill. Charts and tables display time-series data obtained from output generated by a transient-state model run or from user-provided text files of time-series data. A display element can be based entirely on output of a single model run, or, to facilitate comparison of results of multiple scenarios, an element can be based on output from multiple model runs. Scenario Analyzer can export display elements and supporting metadata as a Portable Document Format file.

Development and Application of a Process-based River System Model at a Continental Scale

NASA Astrophysics Data System (ADS)

Kim, S. S. H.; Dutta, D.; Vaze, J.; Hughes, J. D.; Yang, A.; Teng, J.

2014-12-01

Existing global and continental scale river models, mainly designed for integrating with global climate model, are of very course spatial resolutions and they lack many important hydrological processes, such as overbank flow, irrigation diversion, groundwater seepage/recharge, which operate at a much finer resolution. Thus, these models are not suitable for producing streamflow forecast at fine spatial resolution and water accounts at sub-catchment levels, which are important for water resources planning and management at regional and national scale. A large-scale river system model has been developed and implemented for water accounting in Australia as part of the Water Information Research and Development Alliance between Australia's Bureau of Meteorology (BoM) and CSIRO. The model, developed using node-link architecture, includes all major hydrological processes, anthropogenic water utilisation and storage routing that influence the streamflow in both regulated and unregulated river systems. It includes an irrigation model to compute water diversion for irrigation use and associated fluxes and stores and a storage-based floodplain inundation model to compute overbank flow from river to floodplain and associated floodplain fluxes and stores. An auto-calibration tool has been built within the modelling system to automatically calibrate the model in large river systems using Shuffled Complex Evolution optimiser and user-defined objective functions. The auto-calibration tool makes the model computationally efficient and practical for large basin applications. The model has been implemented in several large basins in Australia including the Murray-Darling Basin, covering more than 2 million km2. The results of calibration and validation of the model shows highly satisfactory performance. The model has been operalisationalised in BoM for producing various fluxes and stores for national water accounting. This paper introduces this newly developed river system model describing the conceptual hydrological framework, methods used for representing different hydrological processes in the model and the results and evaluation of the model performance. The operational implementation of the model for water accounting is discussed.

Relevance of the c-statistic when evaluating risk-adjustment models in surgery.

PubMed

Merkow, Ryan P; Hall, Bruce L; Cohen, Mark E; Dimick, Justin B; Wang, Edward; Chow, Warren B; Ko, Clifford Y; Bilimoria, Karl Y

2012-05-01

The measurement of hospital quality based on outcomes requires risk adjustment. The c-statistic is a popular tool used to judge model performance, but can be limited, particularly when evaluating specific operations in focused populations. Our objectives were to examine the interpretation and relevance of the c-statistic when used in models with increasingly similar case mix and to consider an alternative perspective on model calibration based on a graphical depiction of model fit. From the American College of Surgeons National Surgical Quality Improvement Program (2008-2009), patients were identified who underwent a general surgery procedure, and procedure groups were increasingly restricted: colorectal-all, colorectal-elective cases only, and colorectal-elective cancer cases only. Mortality and serious morbidity outcomes were evaluated using logistic regression-based risk adjustment, and model c-statistics and calibration curves were used to compare model performance. During the study period, 323,427 general, 47,605 colorectal-all, 39,860 colorectal-elective, and 21,680 colorectal cancer patients were studied. Mortality ranged from 1.0% in general surgery to 4.1% in the colorectal-all group, and serious morbidity ranged from 3.9% in general surgery to 12.4% in the colorectal-all procedural group. As case mix was restricted, c-statistics progressively declined from the general to the colorectal cancer surgery cohorts for both mortality and serious morbidity (mortality: 0.949 to 0.866; serious morbidity: 0.861 to 0.668). Calibration was evaluated graphically by examining predicted vs observed number of events over risk deciles. For both mortality and serious morbidity, there was no qualitative difference in calibration identified between the procedure groups. In the present study, we demonstrate how the c-statistic can become less informative and, in certain circumstances, can lead to incorrect model-based conclusions, as case mix is restricted and patients become more homogenous. Although it remains an important tool, caution is advised when the c-statistic is advanced as the sole measure of a model performance. Copyright © 2012 American College of Surgeons. All rights reserved.

On combination of strict Bayesian principles with model reduction technique or how stochastic model calibration can become feasible for large-scale applications

NASA Astrophysics Data System (ADS)

Oladyshkin, S.; Schroeder, P.; Class, H.; Nowak, W.

2013-12-01

Predicting underground carbon dioxide (CO2) storage represents a challenging problem in a complex dynamic system. Due to lacking information about reservoir parameters, quantification of uncertainties may become the dominant question in risk assessment. Calibration on past observed data from pilot-scale test injection can improve the predictive power of the involved geological, flow, and transport models. The current work performs history matching to pressure time series from a pilot storage site operated in Europe, maintained during an injection period. Simulation of compressible two-phase flow and transport (CO2/brine) in the considered site is computationally very demanding, requiring about 12 days of CPU time for an individual model run. For that reason, brute-force approaches for calibration are not feasible. In the current work, we explore an advanced framework for history matching based on the arbitrary polynomial chaos expansion (aPC) and strict Bayesian principles. The aPC [1] offers a drastic but accurate stochastic model reduction. Unlike many previous chaos expansions, it can handle arbitrary probability distribution shapes of uncertain parameters, and can therefore handle directly the statistical information appearing during the matching procedure. We capture the dependence of model output on these multipliers with the expansion-based reduced model. In our study we keep the spatial heterogeneity suggested by geophysical methods, but consider uncertainty in the magnitude of permeability trough zone-wise permeability multipliers. Next combined the aPC with Bootstrap filtering (a brute-force but fully accurate Bayesian updating mechanism) in order to perform the matching. In comparison to (Ensemble) Kalman Filters, our method accounts for higher-order statistical moments and for the non-linearity of both the forward model and the inversion, and thus allows a rigorous quantification of calibrated model uncertainty. The usually high computational costs of accurate filtering become very feasible for our suggested aPC-based calibration framework. However, the power of aPC-based Bayesian updating strongly depends on the accuracy of prior information. In the current study, the prior assumptions on the model parameters were not satisfactory and strongly underestimate the reservoir pressure. Thus, the aPC-based response surface used in Bootstrap filtering is fitted to a distant and poorly chosen region within the parameter space. Thanks to the iterative procedure suggested in [2] we overcome this drawback with small computational costs. The iteration successively improves the accuracy of the expansion around the current estimation of the posterior distribution. The final result is a calibrated model of the site that can be used for further studies, with an excellent match to the data. References [1] Oladyshkin S. and Nowak W. Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion. Reliability Engineering and System Safety, 106:179-190, 2012. [2] Oladyshkin S., Class H., Nowak W. Bayesian updating via Bootstrap filtering combined with data-driven polynomial chaos expansions: methodology and application to history matching for carbon dioxide storage in geological formations. Computational Geosciences, 17 (4), 671-687, 2013.

A strain-mediated corrosion model for bioabsorbable metallic stents.

PubMed

Galvin, E; O'Brien, D; Cummins, C; Mac Donald, B J; Lally, C

2017-06-01

This paper presents a strain-mediated phenomenological corrosion model, based on the discrete finite element modelling method which was developed for use with the ANSYS Implicit finite element code. The corrosion model was calibrated from experimental data and used to simulate the corrosion performance of a WE43 magnesium alloy stent. The model was found to be capable of predicting the experimentally observed plastic strain-mediated mass loss profile. The non-linear plastic strain model, extrapolated from the experimental data, was also found to adequately capture the corrosion-induced reduction in the radial stiffness of the stent over time. The model developed will help direct future design efforts towards the minimisation of plastic strain during device manufacture, deployment and in-service, in order to reduce corrosion rates and prolong the mechanical integrity of magnesium devices. The need for corrosion models that explore the interaction of strain with corrosion damage has been recognised as one of the current challenges in degradable material modelling (Gastaldi et al., 2011). A finite element based plastic strain-mediated phenomenological corrosion model was developed in this work and was calibrated based on the results of the corrosion experiments. It was found to be capable of predicting the experimentally observed plastic strain-mediated mass loss profile and the corrosion-induced reduction in the radial stiffness of the stent over time. To the author's knowledge, the results presented here represent the first experimental calibration of a plastic strain-mediated corrosion model of a corroding magnesium stent. Copyright © 2017 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Local-scale spatial modelling for interpolating climatic temperature variables to predict agricultural plant suitability

NASA Astrophysics Data System (ADS)

Webb, Mathew A.; Hall, Andrew; Kidd, Darren; Minansy, Budiman

2016-05-01

Assessment of local spatial climatic variability is important in the planning of planting locations for horticultural crops. This study investigated three regression-based calibration methods (i.e. traditional versus two optimized methods) to relate short-term 12-month data series from 170 temperature loggers and 4 weather station sites with data series from nearby long-term Australian Bureau of Meteorology climate stations. The techniques trialled to interpolate climatic temperature variables, such as frost risk, growing degree days (GDDs) and chill hours, were regression kriging (RK), regression trees (RTs) and random forests (RFs). All three calibration methods produced accurate results, with the RK-based calibration method delivering the most accurate validation measures: coefficients of determination ( R 2) of 0.92, 0.97 and 0.95 and root-mean-square errors of 1.30, 0.80 and 1.31 °C, for daily minimum, daily maximum and hourly temperatures, respectively. Compared with the traditional method of calibration using direct linear regression between short-term and long-term stations, the RK-based calibration method improved R 2 and reduced root-mean-square error (RMSE) by at least 5 % and 0.47 °C for daily minimum temperature, 1 % and 0.23 °C for daily maximum temperature and 3 % and 0.33 °C for hourly temperature. Spatial modelling indicated insignificant differences between the interpolation methods, with the RK technique tending to be the slightly better method due to the high degree of spatial autocorrelation between logger sites.

Uncertainty Analysis of Coupled Socioeconomic-Cropping Models: Building Confidence in Climate Change Decision-Support Tools for Local Stakeholders

NASA Astrophysics Data System (ADS)

Malard, J. J.; Rojas, M.; Adamowski, J. F.; Gálvez, J.; Tuy, H. A.; Melgar-Quiñonez, H.

2015-12-01

While cropping models represent the biophysical aspects of agricultural systems, system dynamics modelling offers the possibility of representing the socioeconomic (including social and cultural) aspects of these systems. The two types of models can then be coupled in order to include the socioeconomic dimensions of climate change adaptation in the predictions of cropping models.We develop a dynamically coupled socioeconomic-biophysical model of agricultural production and its repercussions on food security in two case studies from Guatemala (a market-based, intensive agricultural system and a low-input, subsistence crop-based system). Through the specification of the climate inputs to the cropping model, the impacts of climate change on the entire system can be analysed, and the participatory nature of the system dynamics model-building process, in which stakeholders from NGOs to local governmental extension workers were included, helps ensure local trust in and use of the model.However, the analysis of climate variability's impacts on agroecosystems includes uncertainty, especially in the case of joint physical-socioeconomic modelling, and the explicit representation of this uncertainty in the participatory development of the models is important to ensure appropriate use of the models by the end users. In addition, standard model calibration, validation, and uncertainty interval estimation techniques used for physically-based models are impractical in the case of socioeconomic modelling. We present a methodology for the calibration and uncertainty analysis of coupled biophysical (cropping) and system dynamics (socioeconomic) agricultural models, using survey data and expert input to calibrate and evaluate the uncertainty of the system dynamics as well as of the overall coupled model. This approach offers an important tool for local decision makers to evaluate the potential impacts of climate change and their feedbacks through the associated socioeconomic system.

Downscaling Soil Moisture in the Southern Great Plains Through a Calibrated Multifractal Model for Land Surface Modeling Applications

NASA Technical Reports Server (NTRS)

Mascaro, Giuseppe; Vivoni, Enrique R.; Deidda, Roberto

2010-01-01

Accounting for small-scale spatial heterogeneity of soil moisture (theta) is required to enhance the predictive skill of land surface models. In this paper, we present the results of the development, calibration, and performance evaluation of a downscaling model based on multifractal theory using aircraft!based (800 m) theta estimates collected during the southern Great Plains experiment in 1997 (SGP97).We first demonstrate the presence of scale invariance and multifractality in theta fields of nine square domains of size 25.6 x 25.6 sq km, approximately a satellite footprint. Then, we estimate the downscaling model parameters and evaluate the model performance using a set of different calibration approaches. Results reveal that small-scale theta distributions are adequately reproduced across the entire region when coarse predictors include a dynamic component (i.e., the spatial mean soil moisture ) and a stationary contribution accounting for static features (i.e., topography, soil texture, vegetation). For wet conditions, we found similar multifractal properties of soil moisture across all domains, which we ascribe to the signature of rainfall spatial variability. For drier states, the theta fields in the northern domains are more intermittent than in southern domains, likely because of differences in the distribution of vegetation coverage. Through our analyses, we propose a regional downscaling relation for coarse, satellite-based soil moisture estimates, based on ancillary information (static and dynamic landscape features), which can be used in the study area to characterize statistical properties of small-scale theta distribution required by land surface models and data assimilation systems.

Calibration and validation of an activated sludge model for greenhouse gases no. 1 (ASMG1): prediction of temperature-dependent N₂O emission dynamics.

PubMed

Guo, Lisha; Vanrolleghem, Peter A

2014-02-01

An activated sludge model for greenhouse gases no. 1 was calibrated with data from a wastewater treatment plant (WWTP) without control systems and validated with data from three similar plants equipped with control systems. Special about the calibration/validation approach adopted in this paper is that the data are obtained from simulations with a mathematical model that is widely accepted to describe effluent quality and operating costs of actual WWTPs, the Benchmark Simulation Model No. 2 (BSM2). The calibration also aimed at fitting the model to typical observed nitrous oxide (N₂O) emission data, i.e., a yearly average of 0.5% of the influent total nitrogen load emitted as N₂O-N. Model validation was performed by challenging the model in configurations with different control strategies. The kinetic term describing the dissolved oxygen effect on the denitrification by ammonia-oxidizing bacteria (AOB) was modified into a Haldane term. Both original and Haldane-modified models passed calibration and validation. Even though their yearly averaged values were similar, the two models presented different dynamic N₂O emissions under cold temperature conditions and control. Therefore, data collected in such situations can potentially permit model discrimination. Observed seasonal trends in N₂O emissions are simulated well with both original and Haldane-modified models. A mechanistic explanation based on the temperature-dependent interaction between heterotrophic and autotrophic N₂O pathways was provided. Finally, while adding the AOB denitrification pathway to a model with only heterotrophic N₂O production showed little impact on effluent quality and operating cost criteria, it clearly affected N2O emission productions.

Diffuse sunlight based calibration of the water vapor channel in the upc raman lidar

NASA Astrophysics Data System (ADS)

Muñoz-Porcar, Constantino; Comeron, Adolfo; Sicard, Michaël; Barragan, Ruben; Garcia-Vizcaino, David; Rodríguez-Gómez, Alejandro; Rocadenbosch, Francesc

2018-04-01

A method for determining the calibration factor of the water vapor channel of a Raman lidar, based on zenith measurements of diffuse sunlight and on assumptions regarding some system parameters and Raman scattering models, has been applied to the lidar system of Universitat Politècnica de Catalunya (UPC; Technical University of Catalonia, Spain). Results will be analyzed in terms of stability and comparison with typical methods relying on simultaneous radiosonde measurements.

Hydro-abrasive erosion on coated Pelton runners: Partial calibration of the IEC model based on measurements in HPP Fieschertal

NASA Astrophysics Data System (ADS)

Felix, D.; Abgottspon, A.; Albayrak, I.; Boes, R. M.

2016-11-01

At medium- and high-head hydropower plants (HPPs) on sediment-laden rivers, hydro-abrasive erosion on hydraulic turbines is a major economic issue. For optimization of such HPPs, there is an interest in equations to predict erosion depths. Such a semi-empirical equation suitable for engineering practice is proposed in the relevant guideline of the International Electrotechnical Commission (IEC 62364). However, for Pelton turbines no numerical values of the model's calibration parameters have been available yet. In the scope of a research project at the high-head HPP Fieschertal, Switzerland, the particle load and the erosion on the buckets of two hard-coated 32 MW-Pelton runners have been measured since 2012. Based on three years of field data, the numerical values of a group of calibration parameters of the IEC erosion model were determined for five application cases: (i) reduction of splitter height, (ii) increase of splitter width and (iii) increase of cut-out depth due to erosion of mainly base material, as well as erosion of coating on (iv) the splitter crests and (v) inside the buckets. Further laboratory and field investigations are recommended to quantify the effects of individual parameters as well as to improve, generalize and validate erosion models for uncoated and coated Pelton turbines.

A cosmology-independent calibration of type Ia supernovae data

NASA Astrophysics Data System (ADS)

Hauret, C.; Magain, P.; Biernaux, J.

2018-06-01

Recently, the common methodology used to transform type Ia supernovae (SNe Ia) into genuine standard candles has been suffering criticism. Indeed, it assumes a particular cosmological model (namely the flat ΛCDM) to calibrate the standardisation corrections parameters, i.e. the dependency of the supernova peak absolute magnitude on its colour, post-maximum decline rate and host galaxy mass. As a result, this assumption could make the data compliant to the assumed cosmology and thus nullify all works previously conducted on model comparison. In this work, we verify the viability of these hypotheses by developing a cosmology-independent approach to standardise SNe Ia data from the recent JLA compilation. Our resulting corrections turn out to be very close to the ΛCDM-based corrections. Therefore, even if a ΛCDM-based calibration is questionable from a theoretical point of view, the potential compliance of SNe Ia data does not happen in practice for the JLA compilation. Previous works of model comparison based on these data do not have to be called into question. However, as this cosmology-independent standardisation method has the same degree of complexity than the model-dependent one, it is worth using it in future works, especially if smaller samples are considered, such as the superluminous type Ic supernovae.

The value of oxygen-isotope data and multiple discharge records in calibrating a fully-distributed, physically-based rainfall-runoff model (CRUM3) to improve predictive capability

NASA Astrophysics Data System (ADS)

Neill, Aaron; Reaney, Sim

2015-04-01

Fully-distributed, physically-based rainfall-runoff models attempt to capture some of the complexity of the runoff processes that operate within a catchment, and have been used to address a variety of issues including water quality and the effect of climate change on flood frequency. Two key issues are prevalent, however, which call into question the predictive capability of such models. The first is the issue of parameter equifinality which can be responsible for large amounts of uncertainty. The second is whether such models make the right predictions for the right reasons - are the processes operating within a catchment correctly represented, or do the predictive abilities of these models result only from the calibration process? The use of additional data sources, such as environmental tracers, has been shown to help address both of these issues, by allowing for multi-criteria model calibration to be undertaken, and by permitting a greater understanding of the processes operating in a catchment and hence a more thorough evaluation of how well catchment processes are represented in a model. Using discharge and oxygen-18 data sets, the ability of the fully-distributed, physically-based CRUM3 model to represent the runoff processes in three sub-catchments in Cumbria, NW England has been evaluated. These catchments (Morland, Dacre and Pow) are part of the of the River Eden demonstration test catchment project. The oxygen-18 data set was firstly used to derive transit-time distributions and mean residence times of water for each of the catchments to gain an integrated overview of the types of processes that were operating. A generalised likelihood uncertainty estimation procedure was then used to calibrate the CRUM3 model for each catchment based on a single discharge data set from each catchment. Transit-time distributions and mean residence times of water obtained from the model using the top 100 behavioural parameter sets for each catchment were then compared to those derived from the oxygen-18 data to see how well the model captured catchment dynamics. The value of incorporating the oxygen-18 data set, as well as discharge data sets from multiple as opposed to single gauging stations in each catchment, in the calibration process to improve the predictive capability of the model was then investigated. This was achieved by assessing by how much the identifiability of the model parameters and the ability of the model to represent the runoff processes operating in each catchment improved with the inclusion of the additional data sets with respect to the likely costs that would be incurred in obtaining the data sets themselves.

Comparative study of transient hydraulic tomography with varying parameterizations and zonations: Laboratory sandbox investigation

NASA Astrophysics Data System (ADS)

Luo, Ning; Zhao, Zhanfeng; Illman, Walter A.; Berg, Steven J.

2017-11-01

Transient hydraulic tomography (THT) is a robust method of aquifer characterization to estimate the spatial distributions (or tomograms) of both hydraulic conductivity (K) and specific storage (Ss). However, the highly-parameterized nature of the geostatistical inversion approach renders it computationally intensive for large-scale investigations. In addition, geostatistics-based THT may produce overly smooth tomograms when head data used to constrain the inversion is limited. Therefore, alternative model conceptualizations for THT need to be examined. To investigate this, we simultaneously calibrated different groundwater models with varying parameterizations and zonations using two cases of different pumping and monitoring data densities from a laboratory sandbox. Specifically, one effective parameter model, four geology-based zonation models with varying accuracy and resolution, and five geostatistical models with different prior information are calibrated. Model performance is quantitatively assessed by examining the calibration and validation results. Our study reveals that highly parameterized geostatistical models perform the best among the models compared, while the zonation model with excellent knowledge of stratigraphy also yields comparable results. When few pumping tests with sparse monitoring intervals are available, the incorporation of accurate or simplified geological information into geostatistical models reveals more details in heterogeneity and yields more robust validation results. However, results deteriorate when inaccurate geological information are incorporated. Finally, our study reveals that transient inversions are necessary to obtain reliable K and Ss estimates for making accurate predictions of transient drawdown events.

SURFplus Model Calibration for PBX 9502

DOE Office of Scientific and Technical Information (OSTI.GOV)

Menikoff, Ralph

2017-12-06

The SURFplus reactive burn model is calibrated for the TATB based explosive PBX 9502 at three initial temperatures; hot (75 C), ambient (23 C) and cold (-55 C). The CJ state depends on the initial temperature due to the variation in the initial density and initial specific energy of the PBX reactants. For the reactants, a porosity model for full density TATB is used. This allows the initial PBX density to be set to its measured value even though the coeffcient of thermal expansion for the TATB and the PBX differ. The PBX products EOS is taken as independent ofmore » the initial PBX state. The initial temperature also affects the sensitivity to shock initiation. The model rate parameters are calibrated to Pop plot data, the failure diameter, the limiting detonation speed just above the failure diameters, and curvature effect data for small curvature.« less

Sentinel-1 Precise Orbit Calibration and Validation

NASA Astrophysics Data System (ADS)

Monti Guarnieri, Andrea; Mancon, Simone; Tebaldini, Stefano

2015-05-01

In this paper, we propose a model-based procedure to calibrate and validate Sentinel-1 orbit products by the Multi-Squint (MS) phase. The technique allows to calibrate an interferometric pair geometry by refining the slave orbit with reference to the orbit of a master image. Accordingly, we state the geometric model of the InSAR phase as function of positioning errors of targets and slave track; and the MS phase model as derivative of the InSAR phase geometric model with respect to the squint angle. In this paper we focus on the TOPSAR acquisition modes of Sentinel-1 (IW and EW) assuming at the most a linear error in the known slave trajectory. In particular, we describe a dedicated methodology to prevent InSAR phase artifacts on data acquired by the TOPSAR acquisition mode. Experimental results obtained by interferometric pairs acquired by Sentinel-1 sensor will be displayed.