varying input parameters: Topics by Science.gov

Sample records for varying input parameters

Analysis and selection of optimal function implementations in massively parallel computer

DOEpatents

Archer, Charles Jens [Rochester, MN; Peters, Amanda [Rochester, MN; Ratterman, Joseph D [Rochester, MN

2011-05-31

An apparatus, program product and method optimize the operation of a parallel computer system by, in part, collecting performance data for a set of implementations of a function capable of being executed on the parallel computer system based upon the execution of the set of implementations under varying input parameters in a plurality of input dimensions. The collected performance data may be used to generate selection program code that is configured to call selected implementations of the function in response to a call to the function under varying input parameters. The collected performance data may be used to perform more detailed analysis to ascertain the comparative performance of the set of implementations of the function under the varying input parameters.
CLASSIFYING MEDICAL IMAGES USING MORPHOLOGICAL APPEARANCE MANIFOLDS.

PubMed

Varol, Erdem; Gaonkar, Bilwaj; Davatzikos, Christos

2013-12-31

Input features for medical image classification algorithms are extracted from raw images using a series of pre processing steps. One common preprocessing step in computational neuroanatomy and functional brain mapping is the nonlinear registration of raw images to a common template space. Typically, the registration methods used are parametric and their output varies greatly with changes in parameters. Most results reported previously perform registration using a fixed parameter setting and use the results as input to the subsequent classification step. The variation in registration results due to choice of parameters thus translates to variation of performance of the classifiers that depend on the registration step for input. Analogous issues have been investigated in the computer vision literature, where image appearance varies with pose and illumination, thereby making classification vulnerable to these confounding parameters. The proposed methodology addresses this issue by sampling image appearances as registration parameters vary, and shows that better classification accuracies can be obtained this way, compared to the conventional approach.
Distributed Time-Varying Formation Robust Tracking for General Linear Multiagent Systems With Parameter Uncertainties and External Disturbances.

PubMed

Hua, Yongzhao; Dong, Xiwang; Li, Qingdong; Ren, Zhang

2017-05-18

This paper investigates the time-varying formation robust tracking problems for high-order linear multiagent systems with a leader of unknown control input in the presence of heterogeneous parameter uncertainties and external disturbances. The followers need to accomplish an expected time-varying formation in the state space and track the state trajectory produced by the leader simultaneously. First, a time-varying formation robust tracking protocol with a totally distributed form is proposed utilizing the neighborhood state information. With the adaptive updating mechanism, neither any global knowledge about the communication topology nor the upper bounds of the parameter uncertainties, external disturbances and leader's unknown input are required in the proposed protocol. Then, in order to determine the control parameters, an algorithm with four steps is presented, where feasible conditions for the followers to accomplish the expected time-varying formation tracking are provided. Furthermore, based on the Lyapunov-like analysis theory, it is proved that the formation tracking error can converge to zero asymptotically. Finally, the effectiveness of the theoretical results is verified by simulation examples.
Meter circuit for tuning RF amplifiers

NASA Technical Reports Server (NTRS)

Longthorne, J. E.

1973-01-01

Circuit computes and indicates efficiency of RF amplifier as inputs and other parameters are varied. Voltage drop across internal resistance of ammeter is amplified by operational amplifier and applied to one multiplier input. Other input is obtained through two resistors from positive terminal of power supply.
Can Simulation Credibility Be Improved Using Sensitivity Analysis to Understand Input Data Effects on Model Outcome?

NASA Technical Reports Server (NTRS)

Myers, Jerry G.; Young, M.; Goodenow, Debra A.; Keenan, A.; Walton, M.; Boley, L.

2015-01-01

Model and simulation (MS) credibility is defined as, the quality to elicit belief or trust in MS results. NASA-STD-7009 [1] delineates eight components (Verification, Validation, Input Pedigree, Results Uncertainty, Results Robustness, Use History, MS Management, People Qualifications) that address quantifying model credibility, and provides guidance to the model developers, analysts, and end users for assessing the MS credibility. Of the eight characteristics, input pedigree, or the quality of the data used to develop model input parameters, governing functions, or initial conditions, can vary significantly. These data quality differences have varying consequences across the range of MS application. NASA-STD-7009 requires that the lowest input data quality be used to represent the entire set of input data when scoring the input pedigree credibility of the model. This requirement provides a conservative assessment of model inputs, and maximizes the communication of the potential level of risk of using model outputs. Unfortunately, in practice, this may result in overly pessimistic communication of the MS output, undermining the credibility of simulation predictions to decision makers. This presentation proposes an alternative assessment mechanism, utilizing results parameter robustness, also known as model input sensitivity, to improve the credibility scoring process for specific simulations.
Generative Representations for Evolving Families of Designs

NASA Technical Reports Server (NTRS)

Hornby, Gregory S.

2003-01-01

Since typical evolutionary design systems encode only a single artifact with each individual, each time the objective changes a new set of individuals must be evolved. When this objective varies in a way that can be parameterized, a more general method is to use a representation in which a single individual encodes an entire class of artifacts. In addition to saving time by preventing the need for multiple evolutionary runs, the evolution of parameter-controlled designs can create families of artifacts with the same style and a reuse of parts between members of the family. In this paper an evolutionary design system is described which uses a generative representation to encode families of designs. Because a generative representation is an algorithmic encoding of a design, its input parameters are a way to control aspects of the design it generates. By evaluating individuals multiple times with different input parameters the evolutionary design system creates individuals in which the input parameter controls specific aspects of a design. This system is demonstrated on two design substrates: neural-networks which solve the 3/5/7-parity problem and three-dimensional tables of varying heights.
Sculpt test problem analysis.

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

Sweetser, John David

2013-10-01

This report details Sculpt's implementation from a user's perspective. Sculpt is an automatic hexahedral mesh generation tool developed at Sandia National Labs by Steve Owen. 54 predetermined test cases are studied while varying the input parameters (Laplace iterations, optimization iterations, optimization threshold, number of processors) and measuring the quality of the resultant mesh. This information is used to determine the optimal input parameters to use for an unknown input geometry. The overall characteristics are covered in Chapter 1. The speci c details of every case are then given in Appendix A. Finally, example Sculpt inputs are given in B.1 andmore » B.2.« less
Adaptive fuzzy dynamic surface control of nonlinear systems with input saturation and time-varying output constraints

NASA Astrophysics Data System (ADS)

Edalati, L.; Khaki Sedigh, A.; Aliyari Shooredeli, M.; Moarefianpour, A.

2018-02-01

This paper deals with the design of adaptive fuzzy dynamic surface control for uncertain strict-feedback nonlinear systems with asymmetric time-varying output constraints in the presence of input saturation. To approximate the unknown nonlinear functions and overcome the problem of explosion of complexity, a Fuzzy logic system is combined with the dynamic surface control in the backstepping design technique. To ensure the output constraints satisfaction, an asymmetric time-varying Barrier Lyapunov Function (BLF) is used. Moreover, by applying the minimal learning parameter technique, the number of the online parameters update for each subsystem is reduced to 2. Hence, the semi-globally uniformly ultimately boundedness (SGUUB) of all the closed-loop signals with appropriate tracking error convergence is guaranteed. The effectiveness of the proposed control is demonstrated by two simulation examples.
Using global sensitivity analysis of demographic models for ecological impact assessment.

PubMed

Aiello-Lammens, Matthew E; Akçakaya, H Resit

2017-02-01

Population viability analysis (PVA) is widely used to assess population-level impacts of environmental changes on species. When combined with sensitivity analysis, PVA yields insights into the effects of parameter and model structure uncertainty. This helps researchers prioritize efforts for further data collection so that model improvements are efficient and helps managers prioritize conservation and management actions. Usually, sensitivity is analyzed by varying one input parameter at a time and observing the influence that variation has over model outcomes. This approach does not account for interactions among parameters. Global sensitivity analysis (GSA) overcomes this limitation by varying several model inputs simultaneously. Then, regression techniques allow measuring the importance of input-parameter uncertainties. In many conservation applications, the goal of demographic modeling is to assess how different scenarios of impact or management cause changes in a population. This is challenging because the uncertainty of input-parameter values can be confounded with the effect of impacts and management actions. We developed a GSA method that separates model outcome uncertainty resulting from parameter uncertainty from that resulting from projected ecological impacts or simulated management actions, effectively separating the 2 main questions that sensitivity analysis asks. We applied this method to assess the effects of predicted sea-level rise on Snowy Plover (Charadrius nivosus). A relatively small number of replicate models (approximately 100) resulted in consistent measures of variable importance when not trying to separate the effects of ecological impacts from parameter uncertainty. However, many more replicate models (approximately 500) were required to separate these effects. These differences are important to consider when using demographic models to estimate ecological impacts of management actions. © 2016 Society for Conservation Biology.
A neural network-based input shaping for swing suppression of an overhead crane under payload hoisting and mass variations

NASA Astrophysics Data System (ADS)

Ramli, Liyana; Mohamed, Z.; Jaafar, H. I.

2018-07-01

This paper proposes an improved input shaping for minimising payload swing of an overhead crane with payload hoisting and payload mass variations. A real time unity magnitude zero vibration (UMZV) shaper is designed by using an artificial neural network trained by particle swarm optimisation. The proposed technique could predict and directly update the shaper's parameters in real time to handle the effects of time-varying parameters during the crane operation with hoisting. To evaluate the performances of the proposed method, experiments are conducted on a laboratory overhead crane with a payload hoisting, different payload masses and two different crane motions. The superiority of the proposed method is confirmed by reductions of at least 38.9% and 91.3% in the overall and residual swing responses, respectively over a UMZV shaper designed using an average operating frequency and a robust shaper namely Zero Vibration Derivative-Derivative (ZVDD). The proposed method also demonstrates a significant residual swing suppression as compared to a ZVDD shaper designed based on varying frequency. In addition, the significant reductions are achieved with a less shaper duration resulting in a satisfactory speed of response. It is envisaged that the proposed method can be used for designing effective input shapers for payload swing suppression of a crane with time-varying parameters and for a crane that employ finite actuation states.
Program document for Energy Systems Optimization Program 2 (ESOP2). Volume 1: Engineering manual

NASA Technical Reports Server (NTRS)

Hamil, R. G.; Ferden, S. L.

1977-01-01

The Energy Systems Optimization Program, which is used to provide analyses of Modular Integrated Utility Systems (MIUS), is discussed. Modifications to the input format to allow modular inputs in specified blocks of data are described. An optimization feature which enables the program to search automatically for the minimum value of one parameter while varying the value of other parameters is reported. New program option flags for prime mover analyses and solar energy for space heating and domestic hot water are also covered.
Estimating A Reference Standard Segmentation With Spatially Varying Performance Parameters: Local MAP STAPLE

PubMed Central

Commowick, Olivier; Akhondi-Asl, Alireza; Warfield, Simon K.

2012-01-01

We present a new algorithm, called local MAP STAPLE, to estimate from a set of multi-label segmentations both a reference standard segmentation and spatially varying performance parameters. It is based on a sliding window technique to estimate the segmentation and the segmentation performance parameters for each input segmentation. In order to allow for optimal fusion from the small amount of data in each local region, and to account for the possibility of labels not being observed in a local region of some (or all) input segmentations, we introduce prior probabilities for the local performance parameters through a new Maximum A Posteriori formulation of STAPLE. Further, we propose an expression to compute confidence intervals in the estimated local performance parameters. We carried out several experiments with local MAP STAPLE to characterize its performance and value for local segmentation evaluation. First, with simulated segmentations with known reference standard segmentation and spatially varying performance, we show that local MAP STAPLE performs better than both STAPLE and majority voting. Then we present evaluations with data sets from clinical applications. These experiments demonstrate that spatial adaptivity in segmentation performance is an important property to capture. We compared the local MAP STAPLE segmentations to STAPLE, and to previously published fusion techniques and demonstrate the superiority of local MAP STAPLE over other state-of-the- art algorithms. PMID:22562727
Numerical simulations of flares on M dwarf stars. I - Hydrodynamics and coronal X-ray emission

NASA Technical Reports Server (NTRS)

Cheng, Chung-Chieh; Pallavicini, Roberto

1991-01-01

Flare-loop models are utilized to simulate the time evolution and physical characteristics of stellar X-ray flares by varying the values of flare-energy input and loop parameters. The hydrodynamic evolution is studied in terms of changes in the parameters of the mass, energy, and momentum equations within an area bounded by the chromosphere and the corona. The zone supports a magnetically confined loop for which processes are described including the expansion of heated coronal gas, chromospheric evaporation, and plasma compression at loop footpoints. The intensities, time profiles, and average coronal temperatures of X-ray flares are derived from the simulations and compared to observational evidence. Because the amount of evaporated material does not vary linearly with flare-energy input, large loops are required to produce the energy measured from stellar flares.
A Deterministic Interfacial Cyclic Oxidation Spalling Model. Part 2; Algebraic Approximation, Descriptive Parameters, and Normalized Universal Curve

NASA Technical Reports Server (NTRS)

Smialek, James L.

2002-01-01

A cyclic oxidation interfacial spalling model has been developed in Part 1. The governing equations have been simplified here by substituting a new algebraic expression for the series (Good-Smialek approximation). This produced a direct relationship between cyclic oxidation weight change and model input parameters. It also allowed for the mathematical derivation of various descriptive parameters as a function of the inputs. It is shown that the maximum in weight change varies directly with the parabolic rate constant and cycle duration and inversely with the spall fraction, all to the 1/2 power. The number of cycles to reach maximum and zero weight change vary inversely with the spall fraction, and the ratio of these cycles is exactly 1:3 for most oxides. By suitably normalizing the weight change and cycle number, it is shown that all cyclic oxidation weight change model curves can be represented by one universal expression for a given oxide scale.
Possibility of controlling nonregulated prices in the electricity market by means of varying the parameters of a power system

NASA Astrophysics Data System (ADS)

Vaskovskaya, T. A.

2014-12-01

This paper offers a new approach to the analysis of price signals from the wholesale electricity and capacity market that is based on the analysis of the influence exerted by input data used in the problem of optimization of the power system operating conditions, namely: parameters of a power grid and power-receiving equipment that might vary under the effect of control devices. It is shown that it would be possible to control nonregulated prices for electricity in the wholesale electricity market by varying the parameters of control devices and energy-receiving equipment. An increase in the effectiveness of power transmission and the cost-effective use of fuel-and-energy resources (energy saving) can become an additional effect of controlling the nonregulated prices.
Conditional parametric models for storm sewer runoff

NASA Astrophysics Data System (ADS)

Jonsdottir, H.; Nielsen, H. Aa; Madsen, H.; Eliasson, J.; Palsson, O. P.; Nielsen, M. K.

2007-05-01

The method of conditional parametric modeling is introduced for flow prediction in a sewage system. It is a well-known fact that in hydrological modeling the response (runoff) to input (precipitation) varies depending on soil moisture and several other factors. Consequently, nonlinear input-output models are needed. The model formulation described in this paper is similar to the traditional linear models like final impulse response (FIR) and autoregressive exogenous (ARX) except that the parameters vary as a function of some external variables. The parameter variation is modeled by local lines, using kernels for local linear regression. As such, the method might be referred to as a nearest neighbor method. The results achieved in this study were compared to results from the conventional linear methods, FIR and ARX. The increase in the coefficient of determination is substantial. Furthermore, the new approach conserves the mass balance better. Hence this new approach looks promising for various hydrological models and analysis.
From Spiking Neuron Models to Linear-Nonlinear Models

PubMed Central

Ostojic, Srdjan; Brunel, Nicolas

2011-01-01

Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates. PMID:21283777
From spiking neuron models to linear-nonlinear models.

PubMed

Ostojic, Srdjan; Brunel, Nicolas

2011-01-20

Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates.
Combined non-parametric and parametric approach for identification of time-variant systems

NASA Astrophysics Data System (ADS)

Dziedziech, Kajetan; Czop, Piotr; Staszewski, Wieslaw J.; Uhl, Tadeusz

2018-03-01

Identification of systems, structures and machines with variable physical parameters is a challenging task especially when time-varying vibration modes are involved. The paper proposes a new combined, two-step - i.e. non-parametric and parametric - modelling approach in order to determine time-varying vibration modes based on input-output measurements. Single-degree-of-freedom (SDOF) vibration modes from multi-degree-of-freedom (MDOF) non-parametric system representation are extracted in the first step with the use of time-frequency wavelet-based filters. The second step involves time-varying parametric representation of extracted modes with the use of recursive linear autoregressive-moving-average with exogenous inputs (ARMAX) models. The combined approach is demonstrated using system identification analysis based on the experimental mass-varying MDOF frame-like structure subjected to random excitation. The results show that the proposed combined method correctly captures the dynamics of the analysed structure, using minimum a priori information on the model.
Sparse Polynomial Chaos Surrogate for ACME Land Model via Iterative Bayesian Compressive Sensing

NASA Astrophysics Data System (ADS)

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

2015-12-01

For computationally expensive climate models, Monte-Carlo approaches of exploring the input parameter space are often prohibitive due to slow convergence with respect to ensemble size. To alleviate this, we build inexpensive surrogates using uncertainty quantification (UQ) methods employing Polynomial Chaos (PC) expansions that approximate the input-output relationships using as few model evaluations as possible. However, when many uncertain input parameters are present, such UQ studies suffer from the curse of dimensionality. In particular, for 50-100 input parameters non-adaptive PC representations have infeasible numbers of basis terms. To this end, we develop and employ Weighted Iterative Bayesian Compressive Sensing to learn the most important input parameter relationships for efficient, sparse PC surrogate construction with posterior uncertainty quantified due to insufficient data. Besides drastic dimensionality reduction, the uncertain surrogate can efficiently replace the model in computationally intensive studies such as forward uncertainty propagation and variance-based sensitivity analysis, as well as design optimization and parameter estimation using observational data. We applied the surrogate construction and variance-based uncertainty decomposition to Accelerated Climate Model for Energy (ACME) Land Model for several output QoIs at nearly 100 FLUXNET sites covering multiple plant functional types and climates, varying 65 input parameters over broad ranges of possible values. This work is supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research, Accelerated Climate Modeling for Energy (ACME) project. 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.

Stability, Consistency and Performance of Distribution Entropy in Analysing Short Length Heart Rate Variability (HRV) Signal.

PubMed

Karmakar, Chandan; Udhayakumar, Radhagayathri K; Li, Peng; Venkatesh, Svetha; Palaniswami, Marimuthu

2017-01-01

Distribution entropy ( DistEn ) is a recently developed measure of complexity that is used to analyse heart rate variability (HRV) data. Its calculation requires two input parameters-the embedding dimension m , and the number of bins M which replaces the tolerance parameter r that is used by the existing approximation entropy ( ApEn ) and sample entropy ( SampEn ) measures. The performance of DistEn can also be affected by the data length N . In our previous studies, we have analyzed stability and performance of DistEn with respect to one parameter ( m or M ) or combination of two parameters ( N and M ). However, impact of varying all the three input parameters on DistEn is not yet studied. Since DistEn is predominantly aimed at analysing short length heart rate variability (HRV) signal, it is important to comprehensively study the stability, consistency and performance of the measure using multiple case studies. In this study, we examined the impact of changing input parameters on DistEn for synthetic and physiological signals. We also compared the variations of DistEn and performance in distinguishing physiological (Elderly from Young) and pathological (Healthy from Arrhythmia) conditions with ApEn and SampEn . The results showed that DistEn values are minimally affected by the variations of input parameters compared to ApEn and SampEn. DistEn also showed the most consistent and the best performance in differentiating physiological and pathological conditions with various of input parameters among reported complexity measures. In conclusion, DistEn is found to be the best measure for analysing short length HRV time series.
Sensitivity of Rainfall-runoff Model Parametrization and Performance to Potential Evaporation Inputs

NASA Astrophysics Data System (ADS)

Jayathilake, D. I.; Smith, T. J.

2017-12-01

Many watersheds of interest are confronted with insufficient data and poor process understanding. Therefore, understanding the relative importance of input data types and the impact of different qualities on model performance, parameterization, and fidelity is critically important to improving hydrologic models. In this paper, the change in model parameterization and performance are explored with respect to four different potential evapotranspiration (PET) products of varying quality. For each PET product, two widely used, conceptual rainfall-runoff models are calibrated with multiple objective functions to a sample of 20 basins included in the MOPEX data set and analyzed to understand how model behavior varied. Model results are further analyzed by classifying catchments as energy- or water-limited using the Budyko framework. The results demonstrated that model fit was largely unaffected by the quality of the PET inputs. However, model parameterizations were clearly sensitive to PET inputs, as their production parameters adjusted to counterbalance input errors. Despite this, changes in model robustness were not observed for either model across the four PET products, although robustness was affected by model structure.
Nonstationary multivariate modeling of cerebral autoregulation during hypercapnia.

PubMed

Kostoglou, Kyriaki; Debert, Chantel T; Poulin, Marc J; Mitsis, Georgios D

2014-05-01

We examined the time-varying characteristics of cerebral autoregulation and hemodynamics during a step hypercapnic stimulus by using recursively estimated multivariate (two-input) models which quantify the dynamic effects of mean arterial blood pressure (ABP) and end-tidal CO2 tension (PETCO2) on middle cerebral artery blood flow velocity (CBFV). Beat-to-beat values of ABP and CBFV, as well as breath-to-breath values of PETCO2 during baseline and sustained euoxic hypercapnia were obtained in 8 female subjects. The multiple-input, single-output models used were based on the Laguerre expansion technique, and their parameters were updated using recursive least squares with multiple forgetting factors. The results reveal the presence of nonstationarities that confirm previously reported effects of hypercapnia on autoregulation, i.e. a decrease in the MABP phase lead, and suggest that the incorporation of PETCO2 as an additional model input yields less time-varying estimates of dynamic pressure autoregulation obtained from single-input (ABP-CBFV) models. Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.
Stability, Consistency and Performance of Distribution Entropy in Analysing Short Length Heart Rate Variability (HRV) Signal

PubMed Central

Karmakar, Chandan; Udhayakumar, Radhagayathri K.; Li, Peng; Venkatesh, Svetha; Palaniswami, Marimuthu

2017-01-01

Distribution entropy (DistEn) is a recently developed measure of complexity that is used to analyse heart rate variability (HRV) data. Its calculation requires two input parameters—the embedding dimension m, and the number of bins M which replaces the tolerance parameter r that is used by the existing approximation entropy (ApEn) and sample entropy (SampEn) measures. The performance of DistEn can also be affected by the data length N. In our previous studies, we have analyzed stability and performance of DistEn with respect to one parameter (m or M) or combination of two parameters (N and M). However, impact of varying all the three input parameters on DistEn is not yet studied. Since DistEn is predominantly aimed at analysing short length heart rate variability (HRV) signal, it is important to comprehensively study the stability, consistency and performance of the measure using multiple case studies. In this study, we examined the impact of changing input parameters on DistEn for synthetic and physiological signals. We also compared the variations of DistEn and performance in distinguishing physiological (Elderly from Young) and pathological (Healthy from Arrhythmia) conditions with ApEn and SampEn. The results showed that DistEn values are minimally affected by the variations of input parameters compared to ApEn and SampEn. DistEn also showed the most consistent and the best performance in differentiating physiological and pathological conditions with various of input parameters among reported complexity measures. In conclusion, DistEn is found to be the best measure for analysing short length HRV time series. PMID:28979215
WE-D-BRE-07: Variance-Based Sensitivity Analysis to Quantify the Impact of Biological Uncertainties in Particle Therapy

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

Kamp, F.; Brueningk, S.C.; Wilkens, J.J.

Purpose: In particle therapy, treatment planning and evaluation are frequently based on biological models to estimate the relative biological effectiveness (RBE) or the equivalent dose in 2 Gy fractions (EQD2). In the context of the linear-quadratic model, these quantities depend on biological parameters (α, β) for ions as well as for the reference radiation and on the dose per fraction. The needed biological parameters as well as their dependency on ion species and ion energy typically are subject to large (relative) uncertainties of up to 20–40% or even more. Therefore it is necessary to estimate the resulting uncertainties in e.g.more » RBE or EQD2 caused by the uncertainties of the relevant input parameters. Methods: We use a variance-based sensitivity analysis (SA) approach, in which uncertainties in input parameters are modeled by random number distributions. The evaluated function is executed 10{sup 4} to 10{sup 6} times, each run with a different set of input parameters, randomly varied according to their assigned distribution. The sensitivity S is a variance-based ranking (from S = 0, no impact, to S = 1, only influential part) of the impact of input uncertainties. The SA approach is implemented for carbon ion treatment plans on 3D patient data, providing information about variations (and their origin) in RBE and EQD2. Results: The quantification enables 3D sensitivity maps, showing dependencies of RBE and EQD2 on different input uncertainties. The high number of runs allows displaying the interplay between different input uncertainties. The SA identifies input parameter combinations which result in extreme deviations of the result and the input parameter for which an uncertainty reduction is the most rewarding. Conclusion: The presented variance-based SA provides advantageous properties in terms of visualization and quantification of (biological) uncertainties and their impact. The method is very flexible, model independent, and enables a broad assessment of uncertainties. Supported by DFG grant WI 3745/1-1 and DFG cluster of excellence: Munich-Centre for Advanced Photonics.« less
Dynamic control modification techniques in teleoperation of a flexible manipulator. M.S. Thesis

NASA Technical Reports Server (NTRS)

Magee, David Patrick

1991-01-01

The objective of this research is to reduce the end-point vibration of a large, teleoperated manipulator while preserving the usefulness of the system motion. A master arm is designed to measure desired joint angles as the user specifies a desired tip motion. The desired joint angles from the master arm are the inputs to an adaptive PD control algorithm that positions the end-point of the manipulator. As the user moves the tip of the master, the robot will vibrate at its natural frequencies which makes it difficult to position the end-point. To eliminate the tip vibration during teleoperated motions, an input shaping method is presented. The input shaping method transforms each sample of the desired input into a new set of impulses that do not excite the system resonances. The method is explained using the equation of motion for a simple, second-order system. The impulse response of such a system is derived and the constraint equations for vibrationless motion are presented. To evaluate the robustness of the method, a different residual vibration equation from Singer's is derived that more accurately represents the input shaping technique. The input shaping method is shown to actually increase the residual vibration in certain situations when the system parameters are not accurately specified. Finally, the implementation of the input shaping method to a system with varying parameters is shown to induce a vibration into the system. To eliminate this vibration, a modified command shaping technique is developed. The ability of the modified command shaping method to reduce vibration at the system resonances is tested by varying input perturbations to trajectories in a range of possible user inputs. By comparing the frequency responses of the transverse acceleration at the end-point of the manipulator, the modified method is compared to the original PD routine. The control scheme that produces the smaller magnitude of resonant vibration at the first natural frequency is considered the more effective control method.
Prediction of Welded Joint Strength in Plasma Arc Welding: A Comparative Study Using Back-Propagation and Radial Basis Neural Networks

NASA Astrophysics Data System (ADS)

Srinivas, Kadivendi; Vundavilli, Pandu R.; Manzoor Hussain, M.; Saiteja, M.

2016-09-01

Welding input parameters such as current, gas flow rate and torch angle play a significant role in determination of qualitative mechanical properties of weld joint. Traditionally, it is necessary to determine the weld input parameters for every new welded product to obtain a quality weld joint which is time consuming. In the present work, the effect of plasma arc welding parameters on mild steel was studied using a neural network approach. To obtain a response equation that governs the input-output relationships, conventional regression analysis was also performed. The experimental data was constructed based on Taguchi design and the training data required for neural networks were randomly generated, by varying the input variables within their respective ranges. The responses were calculated for each combination of input variables by using the response equations obtained through the conventional regression analysis. The performances in Levenberg-Marquardt back propagation neural network and radial basis neural network (RBNN) were compared on various randomly generated test cases, which are different from the training cases. From the results, it is interesting to note that for the above said test cases RBNN analysis gave improved training results compared to that of feed forward back propagation neural network analysis. Also, RBNN analysis proved a pattern of increasing performance as the data points moved away from the initial input values.
Use of Rare Earth Elements in investigations of aeolian processes

USDA-ARS?s Scientific Manuscript database

The representation of the dust cycle in atmospheric circulation models hinges on an accurate parameterization of the vertical dust flux at emission. However, existing parameterizations of the vertical dust flux vary substantially in their scaling with wind friction velocity, require input parameters...
Markets, Herding and Response to External Information.

PubMed

Carro, Adrián; Toral, Raúl; San Miguel, Maxi

2015-01-01

We focus on the influence of external sources of information upon financial markets. In particular, we develop a stochastic agent-based market model characterized by a certain herding behavior as well as allowing traders to be influenced by an external dynamic signal of information. This signal can be interpreted as a time-varying advertising, public perception or rumor, in favor or against one of two possible trading behaviors, thus breaking the symmetry of the system and acting as a continuously varying exogenous shock. As an illustration, we use a well-known German Indicator of Economic Sentiment as information input and compare our results with Germany's leading stock market index, the DAX, in order to calibrate some of the model parameters. We study the conditions for the ensemble of agents to more accurately follow the information input signal. The response of the system to the external information is maximal for an intermediate range of values of a market parameter, suggesting the existence of three different market regimes: amplification, precise assimilation and undervaluation of incoming information.
Effect of Weld Tool Geometry on Friction Stir Welded AA2219-T87 Properties

NASA Technical Reports Server (NTRS)

Querin, Joseph A.; Schneider, Judy A.

2008-01-01

In this study, flat panels of AA2219-T87 were friction stir welded (FSWed) using weld tools with tapered pins The three pin geometries of the weld tools included: 0 (straight cylinder), 30 , and 60 angles on the frustum. For each weld tool geometry, the FSW process parameters were optimized to eliminate defects. A constant heat input was maintained while varying the process parameters of spindle rpm and travel speed. This provided a constant heat input for each FSW weld panel while altering the hot working conditions imparted to the workpiece. The resulting mechanical properties were evaluated from tensile test results of the FSW joint.
Influence of tool geometry and processing parameters on welding defects and mechanical properties for friction stir welding of 6061 Aluminium alloy

NASA Astrophysics Data System (ADS)

Daneji, A.; Ali, M.; Pervaiz, S.

2018-04-01

Friction stir welding (FSW) is a form of solid state welding process for joining metals, alloys, and selective composites. Over the years, FSW development has provided an improved way of producing welding joints, and consequently got accepted in numerous industries such as aerospace, automotive, rail and marine etc. In FSW, the base metal properties control the material’s plastic flow under the influence of a rotating tool whereas, the process and tool parameters play a vital role in the quality of weld. In the current investigation, an array of square butt joints of 6061 Aluminum alloy was to be welded under varying FSW process and tool geometry related parameters, after which the resulting weld was evaluated for the corresponding mechanical properties and welding defects. The study incorporates FSW process and tool parameters such as welding speed, pin height and pin thread pitch as input parameters. However, the weld quality related defects and mechanical properties were treated as output parameters. The experimentation paves way to investigate the correlation between the inputs and the outputs. The correlation between inputs and outputs were used as tool to predict the optimized FSW process and tool parameters for a desired weld output of the base metals under investigation. The study also provides reflection on the effect of said parameters on a welding defect such as wormhole.
Fusion of Asynchronous, Parallel, Unreliable Data Streams

DTIC Science & Technology

2010-09-01

channels that might be used. The two channels chosen for this study, galvanic skin response (GSR) and pulse rate, are convenient and reasonably well...vector as NA. The MDS software tool, PERMAP, uses this same abbreviation. The impact of the lack of information may vary depending on the situation...of how PERMAP (and MDS in general) functions when the input parameters are varied. That is outlined in this section; the impact of those choices is
VAX-11 Programs for Computing Available Potential Energy from CTD Data.

DTIC Science & Technology

1981-08-01

the plots can be plotted as many times as desired. The use of the translators is described at the end of section 3. The multiple branch structure of...are listed later in this section, and short * versions of them may be obtained on the terminal any time the program prompts the user for branch number...input, by typing 0/. Within each branch there may be options which are accessible by varying parameters input by the user at the time the branch
Translating landfill methane generation parameters among first-order decay models.

PubMed

Krause, Max J; Chickering, Giles W; Townsend, Timothy G

2016-11-01

Landfill gas (LFG) generation is predicted by a first-order decay (FOD) equation that incorporates two parameters: a methane generation potential (L 0 ) and a methane generation rate (k). Because non-hazardous waste landfills may accept many types of waste streams, multiphase models have been developed in an attempt to more accurately predict methane generation from heterogeneous waste streams. The ability of a single-phase FOD model to predict methane generation using weighted-average methane generation parameters and tonnages translated from multiphase models was assessed in two exercises. In the first exercise, waste composition from four Danish landfills represented by low-biodegradable waste streams was modeled in the Afvalzorg Multiphase Model and methane generation was compared to the single-phase Intergovernmental Panel on Climate Change (IPCC) Waste Model and LandGEM. In the second exercise, waste composition represented by IPCC waste components was modeled in the multiphase IPCC and compared to single-phase LandGEM and Australia's Solid Waste Calculator (SWC). In both cases, weight-averaging of methane generation parameters from waste composition data in single-phase models was effective in predicting cumulative methane generation from -7% to +6% of the multiphase models. The results underscore the understanding that multiphase models will not necessarily improve LFG generation prediction because the uncertainty of the method rests largely within the input parameters. A unique method of calculating the methane generation rate constant by mass of anaerobically degradable carbon was presented (k c ) and compared to existing methods, providing a better fit in 3 of 8 scenarios. Generally, single phase models with weighted-average inputs can accurately predict methane generation from multiple waste streams with varied characteristics; weighted averages should therefore be used instead of regional default values when comparing models. Translating multiphase first-order decay model input parameters by weighted average shows that single-phase models can predict cumulative methane generation within the level of uncertainty of many of the input parameters as defined by the Intergovernmental Panel on Climate Change (IPCC), which indicates that decreasing the uncertainty of the input parameters will make the model more accurate rather than adding multiple phases or input parameters.
Optimization of light quality from color mixing light-emitting diode systems for general lighting

NASA Astrophysics Data System (ADS)

Thorseth, Anders

2012-03-01

Given the problem of metamerisms inherent in color mixing in light-emitting diode (LED) systems with more than three distinct colors, a method for optimizing the spectral output of multicolor LED system with regards to standardized light quality parameters has been developed. The composite spectral power distribution from the LEDs are simulated using spectral radiometric measurements of single commercially available LEDs for varying input power, to account for the efficiency droop and other non-linear effects in electrical power vs. light output. The method uses electrical input powers as input parameters in a randomized steepest decent optimization. The resulting spectral power distributions are evaluated with regard to the light quality using the standard characteristics: CIE color rendering index, correlated color temperature and chromaticity distance. The results indicate Pareto optimal boundaries for each system, mapping the capabilities of the simulated lighting systems with regard to the light quality characteristics.
A Predictor Analysis Framework for Surface Radiation Budget Reprocessing Using Design of Experiments

NASA Astrophysics Data System (ADS)

Quigley, Patricia Allison

Earth's Radiation Budget (ERB) is an accounting of all incoming energy from the sun and outgoing energy reflected and radiated to space by earth's surface and atmosphere. The National Aeronautics and Space Administration (NASA)/Global Energy and Water Cycle Experiment (GEWEX) Surface Radiation Budget (SRB) project produces and archives long-term datasets representative of this energy exchange system on a global scale. The data are comprised of the longwave and shortwave radiative components of the system and is algorithmically derived from satellite and atmospheric assimilation products, and acquired atmospheric data. It is stored as 3-hourly, daily, monthly/3-hourly, and monthly averages of 1° x 1° grid cells. Input parameters used by the algorithms are a key source of variability in the resulting output data sets. Sensitivity studies have been conducted to estimate the effects this variability has on the output data sets using linear techniques. This entails varying one input parameter at a time while keeping all others constant or by increasing all input parameters by equal random percentages, in effect changing input values for every cell for every three hour period and for every day in each month. This equates to almost 11 million independent changes without ever taking into consideration the interactions or dependencies among the input parameters. A more comprehensive method is proposed here for the evaluating the shortwave algorithm to identify both the input parameters and parameter interactions that most significantly affect the output data. This research utilized designed experiments that systematically and simultaneously varied all of the input parameters of the shortwave algorithm. A D-Optimal design of experiments (DOE) was chosen to accommodate the 14 types of atmospheric properties computed by the algorithm and to reduce the number of trials required by a full factorial study from millions to 128. A modified version of the algorithm was made available for testing such that global calculations of the algorithm were tuned to accept information for a single temporal and spatial point and for one month of averaged data. The points were from each of four atmospherically distinct regions to include the Amazon Rainforest, Sahara Desert, Indian Ocean and Mt. Everest. The same design was used for all of the regions. Least squares multiple regression analysis of the results of the modified algorithm identified those parameters and parameter interactions that most significantly affected the output products. It was found that Cosine solar zenith angle was the strongest influence on the output data in all four regions. The interaction of Cosine Solar Zenith Angle and Cloud Fraction had the strongest influence on the output data in the Amazon, Sahara Desert and Mt. Everest Regions, while the interaction of Cloud Fraction and Cloudy Shortwave Radiance most significantly affected output data in the Indian Ocean region. Second order response models were built using the resulting regression coefficients. A Monte Carlo simulation of each model extended the probability distribution beyond the initial design trials to quantify variability in the modeled output data.
Local Sensitivity of Predicted CO 2 Injectivity and Plume Extent to Model Inputs for the FutureGen 2.0 site

DOE PAGES

Zhang, Z. Fred; White, Signe K.; Bonneville, Alain; ...

2014-12-31

Numerical simulations have been used for estimating CO2 injectivity, CO2 plume extent, pressure distribution, and Area of Review (AoR), and for the design of CO2 injection operations and monitoring network for the FutureGen project. The simulation results are affected by uncertainties associated with numerous input parameters, the conceptual model, initial and boundary conditions, and factors related to injection operations. Furthermore, the uncertainties in the simulation results also vary in space and time. The key need is to identify those uncertainties that critically impact the simulation results and quantify their impacts. We introduce an approach to determine the local sensitivity coefficientmore » (LSC), defined as the response of the output in percent, to rank the importance of model inputs on outputs. The uncertainty of an input with higher sensitivity has larger impacts on the output. The LSC is scalable by the error of an input parameter. The composite sensitivity of an output to a subset of inputs can be calculated by summing the individual LSC values. We propose a local sensitivity coefficient method and applied it to the FutureGen 2.0 Site in Morgan County, Illinois, USA, to investigate the sensitivity of input parameters and initial conditions. The conceptual model for the site consists of 31 layers, each of which has a unique set of input parameters. The sensitivity of 11 parameters for each layer and 7 inputs as initial conditions is then investigated. For CO2 injectivity and plume size, about half of the uncertainty is due to only 4 or 5 of the 348 inputs and 3/4 of the uncertainty is due to about 15 of the inputs. The initial conditions and the properties of the injection layer and its neighbour layers contribute to most of the sensitivity. Overall, the simulation outputs are very sensitive to only a small fraction of the inputs. However, the parameters that are important for controlling CO2 injectivity are not the same as those controlling the plume size. The three most sensitive inputs for injectivity were the horizontal permeability of Mt Simon 11 (the injection layer), the initial fracture-pressure gradient, and the residual aqueous saturation of Mt Simon 11, while those for the plume area were the initial salt concentration, the initial pressure, and the initial fracture-pressure gradient. The advantages of requiring only a single set of simulation results, scalability to the proper parameter errors, and easy calculation of the composite sensitivities make this approach very cost-effective for estimating AoR uncertainty and guiding cost-effective site characterization, injection well design, and monitoring network design for CO2 storage projects.« less
Improved parameterization for the vertical flux of dust aerosols emitted by an eroding soil

USDA-ARS?s Scientific Manuscript database

The representation of the dust cycle in atmospheric circulation models hinges on an accurate parameterization of the vertical dust flux at emission. However, existing parameterizations of the vertical dust flux vary substantially in their scaling with wind friction velocity, require input parameters...
The effect of welding parameters on high-strength SMAW all-weld-metal. Part 1: AWS E11018-M

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

Vercesi, J.; Surian, E.

Three AWS A5.5-81 all-weld-metal test assemblies were welded with an E110180-M electrode from a standard production batch, varying the welding parameters in such a way as to obtain three energy inputs: high heat input and high interpass temperature (hot), medium heat input and medium interpass temperature (medium) and low heat input and low interpass temperature (cold). Mechanical properties and metallographic studies were performed in the as-welded condition, and it was found that only the tensile properties obtained with the test specimen made with the intermediate energy input satisfied the AWS E11018-M requirements. With the cold specimen, the maximal yield strengthmore » was exceeded, and with the hot one, neither the yield nor the tensile minimum strengths were achieved. The elongation and the impact properties were high enough to fulfill the minimal requirements, but the best Charpy-V notch values were obtained with the intermediate energy input. Metallographic studies showed that as the energy input increased the percentage of the columnar zones decreased, the grain size became larger, and in the as-welded zone, there was a little increment of both acicular ferrite and ferrite with second phase, with a consequent decrease of primary ferrite. These results showed that this type of alloy is very sensitive to the welding parameters and that very precise instructions must be given to secure the desired tensile properties in the all-weld-metal test specimens and under actual working conditions.« less
Combining in silico evolution and nonlinear dimensionality reduction to redesign responses of signaling networks

NASA Astrophysics Data System (ADS)

Prescott, Aaron M.; Abel, Steven M.

2016-12-01

The rational design of network behavior is a central goal of synthetic biology. Here, we combine in silico evolution with nonlinear dimensionality reduction to redesign the responses of fixed-topology signaling networks and to characterize sets of kinetic parameters that underlie various input-output relations. We first consider the earliest part of the T cell receptor (TCR) signaling network and demonstrate that it can produce a variety of input-output relations (quantified as the level of TCR phosphorylation as a function of the characteristic TCR binding time). We utilize an evolutionary algorithm (EA) to identify sets of kinetic parameters that give rise to: (i) sigmoidal responses with the activation threshold varied over 6 orders of magnitude, (ii) a graded response, and (iii) an inverted response in which short TCR binding times lead to activation. We also consider a network with both positive and negative feedback and use the EA to evolve oscillatory responses with different periods in response to a change in input. For each targeted input-output relation, we conduct many independent runs of the EA and use nonlinear dimensionality reduction to embed the resulting data for each network in two dimensions. We then partition the results into groups and characterize constraints placed on the parameters by the different targeted response curves. Our approach provides a way (i) to guide the design of kinetic parameters of fixed-topology networks to generate novel input-output relations and (ii) to constrain ranges of biological parameters using experimental data. In the cases considered, the network topologies exhibit significant flexibility in generating alternative responses, with distinct patterns of kinetic rates emerging for different targeted responses.

Processing Oscillatory Signals by Incoherent Feedforward Loops

PubMed Central

Zhang, Carolyn; You, Lingchong

2016-01-01

From the timing of amoeba development to the maintenance of stem cell pluripotency, many biological signaling pathways exhibit the ability to differentiate between pulsatile and sustained signals in the regulation of downstream gene expression. While the networks underlying this signal decoding are diverse, many are built around a common motif, the incoherent feedforward loop (IFFL), where an input simultaneously activates an output and an inhibitor of the output. With appropriate parameters, this motif can exhibit temporal adaptation, where the system is desensitized to a sustained input. This property serves as the foundation for distinguishing input signals with varying temporal profiles. Here, we use quantitative modeling to examine another property of IFFLs—the ability to process oscillatory signals. Our results indicate that the system’s ability to translate pulsatile dynamics is limited by two constraints. The kinetics of the IFFL components dictate the input range for which the network is able to decode pulsatile dynamics. In addition, a match between the network parameters and input signal characteristics is required for optimal “counting”. We elucidate one potential mechanism by which information processing occurs in natural networks, and our work has implications in the design of synthetic gene circuits for this purpose. PMID:27623175
Assessing the importance of rainfall uncertainty on hydrological models with different spatial and temporal scale

NASA Astrophysics Data System (ADS)

Nossent, Jiri; Pereira, Fernando; Bauwens, Willy

2015-04-01

Precipitation is one of the key inputs for hydrological models. As long as the values of the hydrological model parameters are fixed, a variation of the rainfall input is expected to induce a change in the model output. Given the increased awareness of uncertainty on rainfall records, it becomes more important to understand the impact of this input - output dynamic. Yet, modellers often still have the intention to mimic the observed flow, whatever the deviation of the employed records from the actual rainfall might be, by recklessly adapting the model parameter values. But is it actually possible to vary the model parameter values in such a way that a certain (observed) model output can be generated based on inaccurate rainfall inputs? Thus, how important is the rainfall uncertainty for the model output with respect to the model parameter importance? To address this question, we apply the Sobol' sensitivity analysis method to assess and compare the importance of the rainfall uncertainty and the model parameters on the output of the hydrological model. In order to be able to treat the regular model parameters and input uncertainty in the same way, and to allow a comparison of their influence, a possible approach is to represent the rainfall uncertainty by a parameter. To tackle the latter issue, we apply so called rainfall multipliers on hydrological independent storm events, as a probabilistic parameter representation of the possible rainfall variation. As available rainfall records are very often point measurements at a discrete time step (hourly, daily, monthly,…), they contain uncertainty due to a latent lack of spatial and temporal variability. The influence of the latter variability can also be different for hydrological models with different spatial and temporal scale. Therefore, we perform the sensitivity analyses on a semi-distributed model (SWAT) and a lumped model (NAM). The assessment and comparison of the importance of the rainfall uncertainty and the model parameters is achieved by considering different scenarios for the included parameters and the state of the models.
Processor design optimization methodology for synthetic vision systems

NASA Astrophysics Data System (ADS)

Wren, Bill; Tarleton, Norman G.; Symosek, Peter F.

1997-06-01

Architecture optimization requires numerous inputs from hardware to software specifications. The task of varying these input parameters to obtain an optimal system architecture with regard to cost, specified performance and method of upgrade considerably increases the development cost due to the infinitude of events, most of which cannot even be defined by any simple enumeration or set of inequalities. We shall address the use of a PC-based tool using genetic algorithms to optimize the architecture for an avionics synthetic vision system, specifically passive millimeter wave system implementation.
Logarithmic amplifiers.

PubMed

Gandler, W; Shapiro, H

1990-01-01

Logarithmic amplifiers (log amps), which produce an output signal proportional to the logarithm of the input signal, are widely used in cytometry for measurements of parameters that vary over a wide dynamic range, e.g., cell surface immunofluorescence. Existing log amp circuits all deviate to some extent from ideal performance with respect to dynamic range and fidelity to the logarithmic curve; accuracy in quantitative analysis using log amps therefore requires that log amps be individually calibrated. However, accuracy and precision may be limited by photon statistics and system noise when very low level input signals are encountered.
Pesticide Environmental Fate Research for the 21st Century: Building Bridges Between Laboratory and Field Studies at Varying Scales

USDA-ARS?s Scientific Manuscript database

Accurate determination of predicted environmental concentrations (PECs) is a continuing and often elusive goal of pesticide risk assessment. PECs are typically derived using simulation models that depend on laboratory generated data for key input parameters (t1/2, Koc, etc.). Model flexibility in ...
Pesticide Environmental Fate Research for the 21st Century: Building Bridges Between Laboratory and Field Studies at Varying Scales

USDA-ARS?s Scientific Manuscript database

Accurate determination of predicted environmental concentrations (PECs) is a continuing and often elusive goal of pesticide risk assessment. PECs are typically derived using simulation models that depend on laboratory generated data for key input parameters (t1/2, Koc, etc.). Model flexibility in ev...
Estimating fire behavior with FIRECAST: user's manual

Treesearch

Jack D. Cohen

1986-01-01

FIRECAST is a computer program that estimates fire behavior in terms of six fire parameters. Required inputs vary depending on the outputs desired by the fire manager. Fuel model options available to users are these: Northern Forest Fire Laboratory (NFFL), National Fire Danger Rating System (NFDRS), and southern California brushland (SCAL). The program has been...
Markets, Herding and Response to External Information

PubMed Central

Carro, Adrián; Toral, Raúl; San Miguel, Maxi

2015-01-01

We focus on the influence of external sources of information upon financial markets. In particular, we develop a stochastic agent-based market model characterized by a certain herding behavior as well as allowing traders to be influenced by an external dynamic signal of information. This signal can be interpreted as a time-varying advertising, public perception or rumor, in favor or against one of two possible trading behaviors, thus breaking the symmetry of the system and acting as a continuously varying exogenous shock. As an illustration, we use a well-known German Indicator of Economic Sentiment as information input and compare our results with Germany’s leading stock market index, the DAX, in order to calibrate some of the model parameters. We study the conditions for the ensemble of agents to more accurately follow the information input signal. The response of the system to the external information is maximal for an intermediate range of values of a market parameter, suggesting the existence of three different market regimes: amplification, precise assimilation and undervaluation of incoming information. PMID:26204451
Method and apparatus for analog signal conditioner for high speed, digital x-ray spectrometer

DOEpatents

Warburton, William K.; Hubbard, Bradley

1999-01-01

A signal processing system which accepts input from an x-ray detector-preamplifier and produces a signal of reduced dynamic range for subsequent analog-to-digital conversion. The system conditions the input signal to reduce the number of bits required in the analog-to-digital converter by removing that part of the input signal which varies only slowly in time and retaining the amplitude of the pulses which carry information about the x-rays absorbed by the detector. The parameters controlling the signal conditioner's operation can be readily supplied in digital form, allowing it to be integrated into a feedback loop as part of a larger digital x-ray spectroscopy system.
Input Forces Estimation for Nonlinear Systems by Applying a Square-Root Cubature Kalman Filter.

PubMed

Song, Xuegang; Zhang, Yuexin; Liang, Dakai

2017-10-10

This work presents a novel inverse algorithm to estimate time-varying input forces in nonlinear beam systems. With the system parameters determined, the input forces can be estimated in real-time from dynamic responses, which can be used for structural health monitoring. In the process of input forces estimation, the Runge-Kutta fourth-order algorithm was employed to discretize the state equations; a square-root cubature Kalman filter (SRCKF) was employed to suppress white noise; the residual innovation sequences, a priori state estimate, gain matrix, and innovation covariance generated by SRCKF were employed to estimate the magnitude and location of input forces by using a nonlinear estimator. The nonlinear estimator was based on the least squares method. Numerical simulations of a large deflection beam and an experiment of a linear beam constrained by a nonlinear spring were employed. The results demonstrated accuracy of the nonlinear algorithm.
Polynomic nonlinear dynamical systems - A residual sensitivity method for model reduction

NASA Technical Reports Server (NTRS)

Yurkovich, S.; Bugajski, D.; Sain, M.

1985-01-01

The motivation for using polynomic combinations of system states and inputs to model nonlinear dynamics systems is founded upon the classical theories of analysis and function representation. A feature of such representations is the need to make available all possible monomials in these variables, up to the degree specified, so as to provide for the description of widely varying functions within a broad class. For a particular application, however, certain monomials may be quite superfluous. This paper examines the possibility of removing monomials from the model in accordance with the level of sensitivity displayed by the residuals to their absence. Critical in these studies is the effect of system input excitation, and the effect of discarding monomial terms, upon the model parameter set. Therefore, model reduction is approached iteratively, with inputs redesigned at each iteration to ensure sufficient excitation of remaining monomials for parameter approximation. Examples are reported to illustrate the performance of such model reduction approaches.
Nonlinear control of linear parameter varying systems with applications to hypersonic vehicles

NASA Astrophysics Data System (ADS)

Wilcox, Zachary Donald

The focus of this dissertation is to design a controller for linear parameter varying (LPV) systems, apply it specifically to air-breathing hypersonic vehicles, and examine the interplay between control performance and the structural dynamics design. Specifically a Lyapunov-based continuous robust controller is developed that yields exponential tracking of a reference model, despite the presence of bounded, nonvanishing disturbances. The hypersonic vehicle has time varying parameters, specifically temperature profiles, and its dynamics can be reduced to an LPV system with additive disturbances. Since the HSV can be modeled as an LPV system the proposed control design is directly applicable. The control performance is directly examined through simulations. A wide variety of applications exist that can be effectively modeled as LPV systems. In particular, flight systems have historically been modeled as LPV systems and associated control tools have been applied such as gain-scheduling, linear matrix inequalities (LMIs), linear fractional transformations (LFT), and mu-types. However, as the type of flight environments and trajectories become more demanding, the traditional LPV controllers may no longer be sufficient. In particular, hypersonic flight vehicles (HSVs) present an inherently difficult problem because of the nonlinear aerothermoelastic coupling effects in the dynamics. HSV flight conditions produce temperature variations that can alter both the structural dynamics and flight dynamics. Starting with the full nonlinear dynamics, the aerothermoelastic effects are modeled by a temperature dependent, parameter varying state-space representation with added disturbances. The model includes an uncertain parameter varying state matrix, an uncertain parameter varying non-square (column deficient) input matrix, and an additive bounded disturbance. In this dissertation, a robust dynamic controller is formulated for a uncertain and disturbed LPV system. The developed controller is then applied to a HSV model, and a Lyapunov analysis is used to prove global exponential reference model tracking in the presence of uncertainty in the state and input matrices and exogenous disturbances. Simulations with a spectrum of gains and temperature profiles on the full nonlinear dynamic model of the HSV is used to illustrate the performance and robustness of the developed controller. In addition, this work considers how the performance of the developed controller varies over a wide variety of control gains and temperature profiles and are optimized with respect to different performance metrics. Specifically, various temperature profile models and related nonlinear temperature dependent disturbances are used to characterize the relative control performance and effort for each model. Examining such metrics as a function of temperature provides a potential inroad to examine the interplay between structural/thermal protection design and control development and has application for future HSV design and control implementation.
Method and apparatus for analog signal conditioner for high speed, digital x-ray spectrometer

DOEpatents

Warburton, W.K.; Hubbard, B.

1999-02-09

A signal processing system which accepts input from an x-ray detector-preamplifier and produces a signal of reduced dynamic range for subsequent analog-to-digital conversion is disclosed. The system conditions the input signal to reduce the number of bits required in the analog-to-digital converter by removing that part of the input signal which varies only slowly in time and retaining the amplitude of the pulses which carry information about the x-rays absorbed by the detector. The parameters controlling the signal conditioner`s operation can be readily supplied in digital form, allowing it to be integrated into a feedback loop as part of a larger digital x-ray spectroscopy system. 13 figs.
Constant-Elasticity-of-Substitution Simulation

NASA Technical Reports Server (NTRS)

Reiter, G.

1986-01-01

Program simulates constant elasticity-of-substitution (CES) production function. CES function used by economic analysts to examine production costs as well as uncertainties in production. User provides such input parameters as price of labor, price of capital, and dispersion levels. CES minimizes expected cost to produce capital-uncertainty pair. By varying capital-value input, one obtains series of capital-uncertainty pairs. Capital-uncertainty pairs then used to generate several cost curves. CES program menu driven and features specific print menu for examining selected output curves. Program written in BASIC for interactive execution and implemented on IBM PC-series computer.
Synthesis procedure for linear time-varying feedback systems with large parameter ignorance

NASA Technical Reports Server (NTRS)

Mcdonald, T. E., Jr.

1972-01-01

The development of synthesis procedures for linear time-varying feedback systems is considered. It is assumed that the plant can be described by linear differential equations with time-varying coefficients; however, ignorance is associated with the plant in that only the range of the time-variations are known instead of exact functional relationships. As a result of this plant ignorance the use of time-varying compensation is ineffective so that only time-invariant compensation is employed. In addition, there is a noise source at the plant output which feeds noise through the feedback elements to the plant input. Because of this noise source the gain of the feedback elements must be as small as possible. No attempt is made to develop a stability criterion for time-varying systems in this work.
WE-FG-206-06: Dual-Input Tracer Kinetic Modeling and Its Analog Implementation for Dynamic Contrast-Enhanced (DCE-) MRI of Malignant Mesothelioma (MPM)

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

Lee, S; Rimner, A; Hayes, S

Purpose: To use dual-input tracer kinetic modeling of the lung for mapping spatial heterogeneity of various kinetic parameters in malignant MPM Methods: Six MPM patients received DCE-MRI as part of their radiation therapy simulation scan. 5 patients had the epitheloid subtype of MPM, while one was biphasic. A 3D fast-field echo sequence with TR/TE/Flip angle of 3.62ms/1.69ms/15° was used for DCE-MRI acquisition. The scan was collected for 5 minutes with a temporal resolution of 5-9 seconds depending on the spatial extent of the tumor. A principal component analysis-based groupwise deformable registration was used to co-register all the DCE-MRI series formore » motion compensation. All the images were analyzed using five different dual-input tracer kinetic models implemented in analog continuous-time formalism: the Tofts-Kety (TK), extended TK (ETK), two compartment exchange (2CX), adiabatic approximation to the tissue homogeneity (AATH), and distributed parameter (DP) models. The following parameters were computed for each model: total blood flow (BF), pulmonary flow fraction (γ), pulmonary blood flow (BF-pa), systemic blood flow (BF-a), blood volume (BV), mean transit time (MTT), permeability-surface area product (PS), fractional interstitial volume (vi), extraction fraction (E), volume transfer constant (Ktrans) and efflux rate constant (kep). Results: Although the majority of patients had epitheloid histologies, kinetic parameter values varied across different models. One patient showed a higher total BF value in all models among the epitheloid histologies, although the γ value was varying among these different models. In one tumor with a large area of necrosis, the TK and ETK models showed higher E, Ktrans, and kep values and lower interstitial volume as compared to AATH and DP and 2CX models. Kinetic parameters such as BF-pa, BF-a, PS, Ktrans values were higher in surviving group compared to non-surviving group across most models. Conclusion: Dual-input tracer kinetic modeling is feasible in determining micro-vascular characteristics of MPM. This project was supported from Cycle for Survival and MSK Imaging and radiation science (IMRAS) grants.« less
Similarity-transformed dyson mapping and SDG-interacting boson hamiltonian

NASA Astrophysics Data System (ADS)

Navrátil, P.; Dobeš, J.

1991-10-01

The sdg-interacting boson hamiltonian is constructed from the fermion shell-model input. The seniority boson mapping as given by the similarity-transformed Dyson boson mapping is used. The s, d, and g collective boson amplitudes are determined consistently from the mapped hamiltonian. Influence of the starting shell-model parameters is discussed. Calculations for the Sm isotopic chain and for the 148Sm, 150Nd, and 196Pt nuclei are presented. Calculated energy levels as well as E2 and E4 properties agree rather well with experimental ones. To obtain such agreement, the input shell-model parameters cannot be fixed at a constant set for several nuclei but have to be somewhat varied, especially in the deformed region. Possible reasons for this variation are discussed. Effects of the explicit g-boson consideration are shown.
Aircraft Fault Detection Using Real-Time Frequency Response Estimation

NASA Technical Reports Server (NTRS)

Grauer, Jared A.

2016-01-01

A real-time method for estimating time-varying aircraft frequency responses from input and output measurements was demonstrated. The Bat-4 subscale airplane was used with NASA Langley Research Center's AirSTAR unmanned aerial flight test facility to conduct flight tests and collect data for dynamic modeling. Orthogonal phase-optimized multisine inputs, summed with pilot stick and pedal inputs, were used to excite the responses. The aircraft was tested in its normal configuration and with emulated failures, which included a stuck left ruddervator and an increased command path latency. No prior knowledge of a dynamic model was used or available for the estimation. The longitudinal short period dynamics were investigated in this work. Time-varying frequency responses and stability margins were tracked well using a 20 second sliding window of data, as compared to a post-flight analysis using output error parameter estimation and a low-order equivalent system model. This method could be used in a real-time fault detection system, or for other applications of dynamic modeling such as real-time verification of stability margins during envelope expansion tests.
Performance Optimizing Adaptive Control with Time-Varying Reference Model Modification

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.; Hashemi, Kelley E.

2017-01-01

This paper presents a new adaptive control approach that involves a performance optimization objective. The control synthesis involves the design of a performance optimizing adaptive controller from a subset of control inputs. The resulting effect of the performance optimizing adaptive controller is to modify the initial reference model into a time-varying reference model which satisfies the performance optimization requirement obtained from an optimal control problem. The time-varying reference model modification is accomplished by the real-time solutions of the time-varying Riccati and Sylvester equations coupled with the least-squares parameter estimation of the sensitivities of the performance metric. The effectiveness of the proposed method is demonstrated by an application of maneuver load alleviation control for a flexible aircraft.
Testing the robustness of management decisions to uncertainty: Everglades restoration scenarios.

PubMed

Fuller, Michael M; Gross, Louis J; Duke-Sylvester, Scott M; Palmer, Mark

2008-04-01

To effectively manage large natural reserves, resource managers must prepare for future contingencies while balancing the often conflicting priorities of different stakeholders. To deal with these issues, managers routinely employ models to project the response of ecosystems to different scenarios that represent alternative management plans or environmental forecasts. Scenario analysis is often used to rank such alternatives to aid the decision making process. However, model projections are subject to uncertainty in assumptions about model structure, parameter values, environmental inputs, and subcomponent interactions. We introduce an approach for testing the robustness of model-based management decisions to the uncertainty inherent in complex ecological models and their inputs. We use relative assessment to quantify the relative impacts of uncertainty on scenario ranking. To illustrate our approach we consider uncertainty in parameter values and uncertainty in input data, with specific examples drawn from the Florida Everglades restoration project. Our examples focus on two alternative 30-year hydrologic management plans that were ranked according to their overall impacts on wildlife habitat potential. We tested the assumption that varying the parameter settings and inputs of habitat index models does not change the rank order of the hydrologic plans. We compared the average projected index of habitat potential for four endemic species and two wading-bird guilds to rank the plans, accounting for variations in parameter settings and water level inputs associated with hypothetical future climates. Indices of habitat potential were based on projections from spatially explicit models that are closely tied to hydrology. For the American alligator, the rank order of the hydrologic plans was unaffected by substantial variation in model parameters. By contrast, simulated major shifts in water levels led to reversals in the ranks of the hydrologic plans in 24.1-30.6% of the projections for the wading bird guilds and several individual species. By exposing the differential effects of uncertainty, relative assessment can help resource managers assess the robustness of scenario choice in model-based policy decisions.

Risk-assessment of post-wildfire hydrological response in semi-arid basins: The effects of varying rainfall representations in the KINEROS2/AGWA model

USDA-ARS?s Scientific Manuscript database

Representation of precipitation is one of the most difficult aspects of modeling post-fire runoff and erosion and also one of the most sensitive input parameters to rainfall-runoff models. The impact of post-fire convective rainstorms, especially in semi-arid watersheds, depends on the overlap betwe...
Description and availability of the SMARTS spectral model for photovoltaic applications

NASA Astrophysics Data System (ADS)

Myers, Daryl R.; Gueymard, Christian A.

2004-11-01

Limited spectral response range of photocoltaic (PV) devices requires device performance be characterized with respect to widely varying terrestrial solar spectra. The FORTRAN code "Simple Model for Atmospheric Transmission of Sunshine" (SMARTS) was developed for various clear-sky solar renewable energy applications. The model is partly based on parameterizations of transmittance functions in the MODTRAN/LOWTRAN band model family of radiative transfer codes. SMARTS computes spectra with a resolution of 0.5 nanometers (nm) below 400 nm, 1.0 nm from 400 nm to 1700 nm, and 5 nm from 1700 nm to 4000 nm. Fewer than 20 input parameters are required to compute spectral irradiance distributions including spectral direct beam, total, and diffuse hemispherical radiation, and up to 30 other spectral parameters. A spreadsheet-based graphical user interface can be used to simplify the construction of input files for the model. The model is the basis for new terrestrial reference spectra developed by the American Society for Testing and Materials (ASTM) for photovoltaic and materials degradation applications. We describe the model accuracy, functionality, and the availability of source and executable code. Applications to PV rating and efficiency and the combined effects of spectral selectivity and varying atmospheric conditions are briefly discussed.
Nonlinear frequency compression: Influence of start frequency and input bandwidth on consonant and vowel recognitiona)

PubMed Central

Alexander, Joshua M.

2016-01-01

By varying parameters that control nonlinear frequency compression (NFC), this study examined how different ways of compressing inaudible mid- and/or high-frequency information at lower frequencies influences perception of consonants and vowels. Twenty-eight listeners with mild to moderately severe hearing loss identified consonants and vowels from nonsense syllables in noise following amplification via a hearing aid simulator. Low-pass filtering and the selection of NFC parameters fixed the output bandwidth at a frequency representing a moderately severe (3.3 kHz, group MS) or a mild-to-moderate (5.0 kHz, group MM) high-frequency loss. For each group (n = 14), effects of six combinations of NFC start frequency (SF) and input bandwidth [by varying the compression ratio (CR)] were examined. For both groups, the 1.6 kHz SF significantly reduced vowel and consonant recognition, especially as CR increased; whereas, recognition was generally unaffected if SF increased at the expense of a higher CR. Vowel recognition detriments for group MS were moderately correlated with the size of the second formant frequency shift following NFC. For both groups, significant improvement (33%–50%) with NFC was confined to final /s/ and /z/ and to some VCV tokens, perhaps because of listeners' limited exposure to each setting. No set of parameters simultaneously maximized recognition across all tokens. PMID:26936574
Dynamic model inversion techniques for breath-by-breath measurement of carbon dioxide from low bandwidth sensors.

PubMed

Sivaramakrishnan, Shyam; Rajamani, Rajesh; Johnson, Bruce D

2009-01-01

Respiratory CO(2) measurement (capnography) is an important diagnosis tool that lacks inexpensive and wearable sensors. This paper develops techniques to enable use of inexpensive but slow CO(2) sensors for breath-by-breath tracking of CO(2) concentration. This is achieved by mathematically modeling the dynamic response and using model-inversion techniques to predict input CO(2) concentration from the slow-varying output. Experiments are designed to identify model-dynamics and extract relevant model-parameters for a solidstate room monitoring CO(2) sensor. A second-order model that accounts for flow through the sensor's filter and casing is found to be accurate in describing the sensor's slow response. The resulting estimate is compared with a standard-of-care respiratory CO(2) analyzer and shown to effectively track variation in breath-by-breath CO(2) concentration. This methodology is potentially useful for measuring fast-varying inputs to any slow sensor.
Simulating the x-ray image contrast to setup techniques with desired flaw detectability

NASA Astrophysics Data System (ADS)

Koshti, Ajay M.

2015-04-01

The paper provides simulation data of previous work by the author in developing a model for estimating detectability of crack-like flaws in radiography. The methodology is developed to help in implementation of NASA Special x-ray radiography qualification, but is generically applicable to radiography. The paper describes a method for characterizing the detector resolution. Applicability of ASTM E 2737 resolution requirements to the model are also discussed. The paper describes a model for simulating the detector resolution. A computer calculator application, discussed here, also performs predicted contrast and signal-to-noise ratio calculations. Results of various simulation runs in calculating x-ray flaw size parameter and image contrast for varying input parameters such as crack depth, crack width, part thickness, x-ray angle, part-to-detector distance, part-to-source distance, source sizes, and detector sensitivity and resolution are given as 3D surfaces. These results demonstrate effect of the input parameters on the flaw size parameter and the simulated image contrast of the crack. These simulations demonstrate utility of the flaw size parameter model in setting up x-ray techniques that provide desired flaw detectability in radiography. The method is applicable to film radiography, computed radiography, and digital radiography.
Neural Network Machine Learning and Dimension Reduction for Data Visualization

NASA Technical Reports Server (NTRS)

Liles, Charles A.

2014-01-01

Neural network machine learning in computer science is a continuously developing field of study. Although neural network models have been developed which can accurately predict a numeric value or nominal classification, a general purpose method for constructing neural network architecture has yet to be developed. Computer scientists are often forced to rely on a trial-and-error process of developing and improving accurate neural network models. In many cases, models are constructed from a large number of input parameters. Understanding which input parameters have the greatest impact on the prediction of the model is often difficult to surmise, especially when the number of input variables is very high. This challenge is often labeled the "curse of dimensionality" in scientific fields. However, techniques exist for reducing the dimensionality of problems to just two dimensions. Once a problem's dimensions have been mapped to two dimensions, it can be easily plotted and understood by humans. The ability to visualize a multi-dimensional dataset can provide a means of identifying which input variables have the highest effect on determining a nominal or numeric output. Identifying these variables can provide a better means of training neural network models; models can be more easily and quickly trained using only input variables which appear to affect the outcome variable. The purpose of this project is to explore varying means of training neural networks and to utilize dimensional reduction for visualizing and understanding complex datasets.
Investigation of Multi-Input Multi-Output Robust Control Methods to Handle Parametric Uncertainties in Autopilot Design.

PubMed

Kasnakoğlu, Coşku

2016-01-01

Some level of uncertainty is unavoidable in acquiring the mass, geometry parameters and stability derivatives of an aerial vehicle. In certain instances tiny perturbations of these could potentially cause considerable variations in flight characteristics. This research considers the impact of varying these parameters altogether. This is a generalization of examining the effects of particular parameters on selected modes present in existing literature. Conventional autopilot designs commonly assume that each flight channel is independent and develop single-input single-output (SISO) controllers for every one, that are utilized in parallel for actual flight. It is demonstrated that an attitude controller built like this can function flawlessly on separate nominal cases, but can become unstable with a perturbation no more than 2%. Two robust multi-input multi-output (MIMO) design strategies, specifically loop-shaping and μ-synthesis are outlined as potential substitutes and are observed to handle large parametric changes of 30% while preserving decent performance. Duplicating the loop-shaping procedure for the outer loop, a complete flight control system is formed. It is confirmed through software-in-the-loop (SIL) verifications utilizing blade element theory (BET) that the autopilot is capable of navigation and landing exposed to high parametric variations and powerful winds.
Investigation of Multi-Input Multi-Output Robust Control Methods to Handle Parametric Uncertainties in Autopilot Design

PubMed Central

Kasnakoğlu, Coşku

2016-01-01

Some level of uncertainty is unavoidable in acquiring the mass, geometry parameters and stability derivatives of an aerial vehicle. In certain instances tiny perturbations of these could potentially cause considerable variations in flight characteristics. This research considers the impact of varying these parameters altogether. This is a generalization of examining the effects of particular parameters on selected modes present in existing literature. Conventional autopilot designs commonly assume that each flight channel is independent and develop single-input single-output (SISO) controllers for every one, that are utilized in parallel for actual flight. It is demonstrated that an attitude controller built like this can function flawlessly on separate nominal cases, but can become unstable with a perturbation no more than 2%. Two robust multi-input multi-output (MIMO) design strategies, specifically loop-shaping and μ-synthesis are outlined as potential substitutes and are observed to handle large parametric changes of 30% while preserving decent performance. Duplicating the loop-shaping procedure for the outer loop, a complete flight control system is formed. It is confirmed through software-in-the-loop (SIL) verifications utilizing blade element theory (BET) that the autopilot is capable of navigation and landing exposed to high parametric variations and powerful winds. PMID:27783706
Asymptotic Eigenstructures

NASA Technical Reports Server (NTRS)

Thompson, P. M.; Stein, G.

1980-01-01

The behavior of the closed loop eigenstructure of a linear system with output feedback is analyzed as a single parameter multiplying the feedback gain is varied. An algorithm is presented that computes the asymptotically infinite eigenstructure, and it is shown how a system with high gain, feedback decouples into single input, single output systems. Then a synthesis algorithm is presented which uses full state feedback to achieve a desired asymptotic eigenstructure.
Recommended Parameter Values for GENII Modeling of Radionuclides in Routine Air and Water Releases

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

Snyder, Sandra F.; Arimescu, Carmen; Napier, Bruce A.

The GENII v2 code is used to estimate dose to individuals or populations from the release of radioactive materials into air or water. Numerous parameter values are required for input into this code. User-defined parameters cover the spectrum from chemical data, meteorological data, agricultural data, and behavioral data. This document is a summary of parameter values that reflect conditions in the United States. Reasonable regional and age-dependent data is summarized. Data availability and quality varies. The set of parameters described address scenarios for chronic air emissions or chronic releases to public waterways. Considerations for the special tritium and carbon-14 modelsmore » are briefly addressed. GENIIv2.10.0 is the current software version that this document supports.« less
Input Uncertainty and its Implications on Parameter Assessment in Hydrologic and Hydroclimatic Modelling Studies

NASA Astrophysics Data System (ADS)

Chowdhury, S.; Sharma, A.

2005-12-01

Hydrological model inputs are often derived from measurements at point locations taken at discrete time steps. The nature of uncertainty associated with such inputs is thus a function of the quality and number of measurements available in time. A change in these characteristics (such as a change in the number of rain-gauge inputs used to derive spatially averaged rainfall) results in inhomogeneity in the associated distributional profile. Ignoring such uncertainty can lead to models that aim to simulate based on the observed input variable instead of the true measurement, resulting in a biased representation of the underlying system dynamics as well as an increase in both bias and the predictive uncertainty in simulations. This is especially true of cases where the nature of uncertainty likely in the future is significantly different to that in the past. Possible examples include situations where the accuracy of the catchment averaged rainfall has increased substantially due to an increase in the rain-gauge density, or accuracy of climatic observations (such as sea surface temperatures) increased due to the use of more accurate remote sensing technologies. We introduce here a method to ascertain the true value of parameters in the presence of additive uncertainty in model inputs. This method, known as SIMulation EXtrapolation (SIMEX, [Cook, 1994]) operates on the basis of an empirical relationship between parameters and the level of additive input noise (or uncertainty). The method starts with generating a series of alternate realisations of model inputs by artificially adding white noise in increasing multiples of the known error variance. The alternate realisations lead to alternate sets of parameters that are increasingly biased with respect to the truth due to the increased variability in the inputs. Once several such realisations have been drawn, one is able to formulate an empirical relationship between the parameter values and the level of additive noise present. SIMEX is based on theory that the trend in alternate parameters can be extrapolated back to the notional error free zone. We illustrate the utility of SIMEX in a synthetic rainfall-runoff modelling scenario and an application to study the dependence of uncertain distributed sea surface temperature anomalies with an indicator of the El Nino Southern Oscillation, the Southern Oscillation Index (SOI). The errors in rainfall data and its affect is explored using Sacramento rainfall runoff model. The rainfall uncertainty is assumed to be multiplicative and temporally invariant. The model used to relate the sea surface temperature anomalies (SSTA) to the SOI is assumed to be of a linear form. The nature of uncertainty in the SSTA is additive and varies with time. The SIMEX framework allows assessment of the relationship between the error free inputs and response. Cook, J.R., Stefanski, L. A., Simulation-Extrapolation Estimation in Parametric Measurement Error Models, Journal of the American Statistical Association, 89 (428), 1314-1328, 1994.
Random vs. Combinatorial Methods for Discrete Event Simulation of a Grid Computer Network

NASA Technical Reports Server (NTRS)

Kuhn, D. Richard; Kacker, Raghu; Lei, Yu

2010-01-01

This study compared random and t-way combinatorial inputs of a network simulator, to determine if these two approaches produce significantly different deadlock detection for varying network configurations. Modeling deadlock detection is important for analyzing configuration changes that could inadvertently degrade network operations, or to determine modifications that could be made by attackers to deliberately induce deadlock. Discrete event simulation of a network may be conducted using random generation, of inputs. In this study, we compare random with combinatorial generation of inputs. Combinatorial (or t-way) testing requires every combination of any t parameter values to be covered by at least one test. Combinatorial methods can be highly effective because empirical data suggest that nearly all failures involve the interaction of a small number of parameters (1 to 6). Thus, for example, if all deadlocks involve at most 5-way interactions between n parameters, then exhaustive testing of all n-way interactions adds no additional information that would not be obtained by testing all 5-way interactions. While the maximum degree of interaction between parameters involved in the deadlocks clearly cannot be known in advance, covering all t-way interactions may be more efficient than using random generation of inputs. In this study we tested this hypothesis for t = 2, 3, and 4 for deadlock detection in a network simulation. Achieving the same degree of coverage provided by 4-way tests would have required approximately 3.2 times as many random tests; thus combinatorial methods were more efficient for detecting deadlocks involving a higher degree of interactions. The paper reviews explanations for these results and implications for modeling and simulation.
Wing optimization for space shuttle orbiter vehicles

NASA Technical Reports Server (NTRS)

Surber, T. E.; Bornemann, W. E.; Miller, W. D.

1972-01-01

The results were presented of a parametric study performed to determine the optimum wing geometry for a proposed space shuttle orbiter. The results of the study establish the minimum weight wing for a series of wing-fuselage combinations subject to constraints on aerodynamic heating, wing trailing edge sweep, and wing over-hang. The study consists of a generalized design evaluation which has the flexibility of arbitrarily varying those wing parameters which influence the vehicle system design and its performance. The study is structured to allow inputs of aerodynamic, weight, aerothermal, structural and material data in a general form so that the influence of these parameters on the design optimization process can be isolated and identified. This procedure displays the sensitivity of the system design of variations in wing geometry. The parameters of interest are varied in a prescribed fashion on a selected fuselage and the effect on the total vehicle weight is determined. The primary variables investigated are: wing loading, aspect ratio, leading edge sweep, thickness ratio, and taper ratio.
Simulation of synaptic short-term plasticity using Ba(CF3SO3)2-doped polyethylene oxide electrolyte film.

PubMed

Chang, C T; Zeng, F; Li, X J; Dong, W S; Lu, S H; Gao, S; Pan, F

2016-01-07

The simulation of synaptic plasticity using new materials is critical in the study of brain-inspired computing. Devices composed of Ba(CF3SO3)2-doped polyethylene oxide (PEO) electrolyte film were fabricated and with pulse responses found to resemble the synaptic short-term plasticity (STP) of both short-term depression (STD) and short-term facilitation (STF) synapses. The values of the charge and discharge peaks of the pulse responses did not vary with input number when the pulse frequency was sufficiently low(~1 Hz). However, when the frequency was increased, the charge and discharge peaks decreased and increased, respectively, in gradual trends and approached stable values with respect to the input number. These stable values varied with the input frequency, which resulted in the depressed and potentiated weight modifications of the charge and discharge peaks, respectively. These electrical properties simulated the high and low band-pass filtering effects of STD and STF, respectively. The simulations were consistent with biological results and the corresponding biological parameters were successfully extracted. The study verified the feasibility of using organic electrolytes to mimic STP.
Simulation of synaptic short-term plasticity using Ba(CF3SO3)2-doped polyethylene oxide electrolyte film

PubMed Central

Chang, C. T.; Zeng, F.; Li, X. J.; Dong, W. S.; Lu, S. H.; Gao, S.; Pan, F.

2016-01-01

The simulation of synaptic plasticity using new materials is critical in the study of brain-inspired computing. Devices composed of Ba(CF3SO3)2-doped polyethylene oxide (PEO) electrolyte film were fabricated and with pulse responses found to resemble the synaptic short-term plasticity (STP) of both short-term depression (STD) and short-term facilitation (STF) synapses. The values of the charge and discharge peaks of the pulse responses did not vary with input number when the pulse frequency was sufficiently low(~1 Hz). However, when the frequency was increased, the charge and discharge peaks decreased and increased, respectively, in gradual trends and approached stable values with respect to the input number. These stable values varied with the input frequency, which resulted in the depressed and potentiated weight modifications of the charge and discharge peaks, respectively. These electrical properties simulated the high and low band-pass filtering effects of STD and STF, respectively. The simulations were consistent with biological results and the corresponding biological parameters were successfully extracted. The study verified the feasibility of using organic electrolytes to mimic STP. PMID:26739613
Simulation of synaptic short-term plasticity using Ba(CF3SO3)2-doped polyethylene oxide electrolyte film

NASA Astrophysics Data System (ADS)

Chang, C. T.; Zeng, F.; Li, X. J.; Dong, W. S.; Lu, S. H.; Gao, S.; Pan, F.

2016-01-01

The simulation of synaptic plasticity using new materials is critical in the study of brain-inspired computing. Devices composed of Ba(CF3SO3)2-doped polyethylene oxide (PEO) electrolyte film were fabricated and with pulse responses found to resemble the synaptic short-term plasticity (STP) of both short-term depression (STD) and short-term facilitation (STF) synapses. The values of the charge and discharge peaks of the pulse responses did not vary with input number when the pulse frequency was sufficiently low(~1 Hz). However, when the frequency was increased, the charge and discharge peaks decreased and increased, respectively, in gradual trends and approached stable values with respect to the input number. These stable values varied with the input frequency, which resulted in the depressed and potentiated weight modifications of the charge and discharge peaks, respectively. These electrical properties simulated the high and low band-pass filtering effects of STD and STF, respectively. The simulations were consistent with biological results and the corresponding biological parameters were successfully extracted. The study verified the feasibility of using organic electrolytes to mimic STP.
Extension of the Vane Pump-Grinder Technology to Manufacture Finely Dispersed Meat Batters.

PubMed

Irmscher, Stefan B; Gibis, Monika; Herrmann, Kurt; Oechsle, Anja Maria; Kohlus, Reinhard; Weiss, Jochen

2016-03-01

A vane pump-grinder system was extended to enable the manufacture of finely dispersed emulsion-type sausages by constructing and attaching a high-shear homogenizer at the outlet. We hypothesized that the dispersing capabilities of the extended system may be improved to the point of facilitating meat-fat emulsification due to an overall increased volumetric energy input EV . Coarsely ground raw material mixtures were processed to yield meat batters at varying volume flow rates (10 to 60 L/min) and rotational rotor speeds of the homogenizer nrotor (1000 to 3400 rpm). The normalized torques acting on pump, grinder, and homogenizer motors were recorded and unit power consumptions were calculated. The structure of the manufactured meat batters and sausages were analyzed via image analysis. Key physicochemical properties of unheated and heated batters, that is, texture, water-binding, color, and solubilized protein were determined. The mean diameter d10 of the visible lean meat particles varied between 352 and 406 μm whereas the mean volume-surface diameter d32 varied between 603 and 796 μm. The lightness L* ranged from 66.2 to 70.7 and correlated with the volumetric energy input and product structure. By contrast, varying process parameters did not impact color values a* (approximately 11) and b* (approximately 8). Interestingly, water-binding and protein solubilization were not affected. An exponential process-structure relationship was identified allowing manufacturers to predict product properties as a function of applied process parameters. Raw material mixtures can be continuously comminuted, emulsified, and subsequently filled into casings using an extended vane pump-grinder. © 2016 Institute of Food Technologists®
Diffusion from a line source

NASA Technical Reports Server (NTRS)

Burns, R. E.

1973-01-01

The problem with predicting pollutant diffusion from a line source of arbitrary geometry is treated. The concentration at the line source may be arbitrarily varied with time. Special attention is given to the meteorological inputs which act as boundary conditions for the problem, and a mixing layer of arbitrary depth is assumed. Numerical application of the derived theory indicates the combinations of meteorological parameters that may be expected to result in high pollution concentrations.
Simulating the X-Ray Image Contrast to Set-Up Techniques with Desired Flaw Detectability

NASA Technical Reports Server (NTRS)

Koshti, Ajay M.

2015-01-01

The paper provides simulation data of previous work by the author in developing a model for estimating detectability of crack-like flaws in radiography. The methodology is being developed to help in implementation of NASA Special x-ray radiography qualification, but is generically applicable to radiography. The paper describes a method for characterizing X-ray detector resolution for crack detection. Applicability of ASTM E 2737 resolution requirements to the model are also discussed. The paper describes a model for simulating the detector resolution. A computer calculator application, discussed here, also performs predicted contrast and signal-to-noise ratio calculations. Results of various simulation runs in calculating x-ray flaw size parameter and image contrast for varying input parameters such as crack depth, crack width, part thickness, x-ray angle, part-to-detector distance, part-to-source distance, source sizes, and detector sensitivity and resolution are given as 3D surfaces. These results demonstrate effect of the input parameters on the flaw size parameter and the simulated image contrast of the crack. These simulations demonstrate utility of the flaw size parameter model in setting up x-ray techniques that provide desired flaw detectability in radiography. The method is applicable to film radiography, computed radiography, and digital radiography.
Processing oscillatory signals by incoherent feedforward loops

NASA Astrophysics Data System (ADS)

Zhang, Carolyn; Wu, Feilun; Tsoi, Ryan; Shats, Igor; You, Lingchong

From the timing of amoeba development to the maintenance of stem cell pluripotency,many biological signaling pathways exhibit the ability to differentiate between pulsatile and sustained signals in the regulation of downstream gene expression.While networks underlying this signal decoding are diverse,many are built around a common motif, the incoherent feedforward loop (IFFL),where an input simultaneously activates an output and an inhibitor of the output.With appropriate parameters,this motif can generate temporal adaptation,where the system is desensitized to a sustained input.This property serves as the foundation for distinguishing signals with varying temporal profiles.Here,we use quantitative modeling to examine another property of IFFLs,the ability to process oscillatory signals.Our results indicate that the system's ability to translate pulsatile dynamics is limited by two constraints.The kinetics of IFFL components dictate the input range for which the network can decode pulsatile dynamics.In addition,a match between the network parameters and signal characteristics is required for optimal ``counting''.We elucidate one potential mechanism by which information processing occurs in natural networks with implications in the design of synthetic gene circuits for this purpose. This work was partially supported by the National Science Foundation Graduate Research Fellowship (CZ).

Validation for Global Solar Wind Prediction Using Ulysses Comparison: Multiple Coronal and Heliospheric Models Installed at the Community Coordinated Modeling Center

NASA Technical Reports Server (NTRS)

Jian, L. K.; MacNeice, P. J.; Mays, M. L.; Taktakishvili, A.; Odstrcil, D.; Jackson, B.; Yu, H.-S.; Riley, P.; Sokolov, I. V.

2016-01-01

The prediction of the background global solar wind is a necessary part of space weather forecasting. Several coronal and heliospheric models have been installed and/or recently upgraded at the Community Coordinated Modeling Center (CCMC), including the Wang-Sheely-Arge (WSA)-Enlil model, MHD-Around-a-Sphere (MAS)-Enlil model, Space Weather Modeling Framework (SWMF), and Heliospheric tomography using interplanetary scintillation data. Ulysses recorded the last fast latitudinal scan from southern to northern poles in 2007. By comparing the modeling results with Ulysses observations over seven Carrington rotations, we have extended our third-party validation from the previous near-Earth solar wind to middle to high latitudes, in the same late declining phase of solar cycle 23. Besides visual comparison, wehave quantitatively assessed the models capabilities in reproducing the time series, statistics, and latitudinal variations of solar wind parameters for a specific range of model parameter settings, inputs, and grid configurations available at CCMC. The WSA-Enlil model results vary with three different magnetogram inputs.The MAS-Enlil model captures the solar wind parameters well, despite its underestimation of the speed at middle to high latitudes. The new version of SWMF misses many solar wind variations probably because it uses lower grid resolution than other models. The interplanetary scintillation-tomography cannot capture the latitudinal variations of solar wind well yet. Because the model performance varies with parameter settings which are optimized for different epochs or flow states, the performance metric study provided here can serve as a template that researchers can use to validate the models for the time periods and conditions of interest to them.
Validation for global solar wind prediction using Ulysses comparison: Multiple coronal and heliospheric models installed at the Community Coordinated Modeling Center

NASA Astrophysics Data System (ADS)

Jian, L. K.; MacNeice, P. J.; Mays, M. L.; Taktakishvili, A.; Odstrcil, D.; Jackson, B.; Yu, H.-S.; Riley, P.; Sokolov, I. V.

2016-08-01

The prediction of the background global solar wind is a necessary part of space weather forecasting. Several coronal and heliospheric models have been installed and/or recently upgraded at the Community Coordinated Modeling Center (CCMC), including the Wang-Sheely-Arge (WSA)-Enlil model, MHD-Around-a-Sphere (MAS)-Enlil model, Space Weather Modeling Framework (SWMF), and heliospheric tomography using interplanetary scintillation data. Ulysses recorded the last fast latitudinal scan from southern to northern poles in 2007. By comparing the modeling results with Ulysses observations over seven Carrington rotations, we have extended our third-party validation from the previous near-Earth solar wind to middle to high latitudes, in the same late declining phase of solar cycle 23. Besides visual comparison, we have quantitatively assessed the models' capabilities in reproducing the time series, statistics, and latitudinal variations of solar wind parameters for a specific range of model parameter settings, inputs, and grid configurations available at CCMC. The WSA-Enlil model results vary with three different magnetogram inputs. The MAS-Enlil model captures the solar wind parameters well, despite its underestimation of the speed at middle to high latitudes. The new version of SWMF misses many solar wind variations probably because it uses lower grid resolution than other models. The interplanetary scintillation-tomography cannot capture the latitudinal variations of solar wind well yet. Because the model performance varies with parameter settings which are optimized for different epochs or flow states, the performance metric study provided here can serve as a template that researchers can use to validate the models for the time periods and conditions of interest to them.
Effects of control inputs on the estimation of stability and control parameters of a light airplane

NASA Technical Reports Server (NTRS)

Cannaday, R. L.; Suit, W. T.

1977-01-01

The maximum likelihood parameter estimation technique was used to determine the values of stability and control derivatives from flight test data for a low-wing, single-engine, light airplane. Several input forms were used during the tests to investigate the consistency of parameter estimates as it relates to inputs. These consistencies were compared by using the ensemble variance and estimated Cramer-Rao lower bound. In addition, the relationship between inputs and parameter correlations was investigated. Results from the stabilator inputs are inconclusive but the sequence of rudder input followed by aileron input or aileron followed by rudder gave more consistent estimates than did rudder or ailerons individually. Also, square-wave inputs appeared to provide slightly improved consistency in the parameter estimates when compared to sine-wave inputs.
Control and optimization system

DOEpatents

Xinsheng, Lou

2013-02-12

A system for optimizing a power plant includes a chemical loop having an input for receiving an input parameter (270) and an output for outputting an output parameter (280), a control system operably connected to the chemical loop and having a multiple controller part (230) comprising a model-free controller. The control system receives the output parameter (280), optimizes the input parameter (270) based on the received output parameter (280), and outputs an optimized input parameter (270) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.
System and method for motor parameter estimation

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

Luhrs, Bin; Yan, Ting

2014-03-18

A system and method for determining unknown values of certain motor parameters includes a motor input device connectable to an electric motor having associated therewith values for known motor parameters and an unknown value of at least one motor parameter. The motor input device includes a processing unit that receives a first input from the electric motor comprising values for the known motor parameters for the electric motor and receive a second input comprising motor data on a plurality of reference motors, including values for motor parameters corresponding to the known motor parameters of the electric motor and values formore » motor parameters corresponding to the at least one unknown motor parameter value of the electric motor. The processor determines the unknown value of the at least one motor parameter from the first input and the second input and determines a motor management strategy for the electric motor based thereon.« less
Field mapping for heat capacity mapping determinations: Ground support for airborne thermal surveys

NASA Technical Reports Server (NTRS)

Lyon, R. J. P.

1976-01-01

Thermal models independently derived by Watson, Outcalt, and Rosema were compared using similar input data and found to yield very different results. Each model has a varying degree of sensitivity to any specified parameter. Data collected at Pisgah Crater-Lavic Lake was re-examined to indicate serious discrepancy in results for thermal inertia from Jet Lab Propulsion Laboratory calculations, when made using the same orginal data sets.
Flight Test Validation of Optimal Input Design and Comparison to Conventional Inputs

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

1997-01-01

A technique for designing optimal inputs for aerodynamic parameter estimation was flight tested on the F-18 High Angle of Attack Research Vehicle (HARV). Model parameter accuracies calculated from flight test data were compared on an equal basis for optimal input designs and conventional inputs at the same flight condition. In spite of errors in the a priori input design models and distortions of the input form by the feedback control system, the optimal inputs increased estimated parameter accuracies compared to conventional 3-2-1-1 and doublet inputs. In addition, the tests using optimal input designs demonstrated enhanced design flexibility, allowing the optimal input design technique to use a larger input amplitude to achieve further increases in estimated parameter accuracy without departing from the desired flight test condition. This work validated the analysis used to develop the optimal input designs, and demonstrated the feasibility and practical utility of the optimal input design technique.
Analytically-derived sensitivities in one-dimensional models of solute transport in porous media

USGS Publications Warehouse

Knopman, D.S.

1987-01-01

Analytically-derived sensitivities are presented for parameters in one-dimensional models of solute transport in porous media. Sensitivities were derived by direct differentiation of closed form solutions for each of the odel, and by a time integral method for two of the models. Models are based on the advection-dispersion equation and include adsorption and first-order chemical decay. Boundary conditions considered are: a constant step input of solute, constant flux input of solute, and exponentially decaying input of solute at the upstream boundary. A zero flux is assumed at the downstream boundary. Initial conditions include a constant and spatially varying distribution of solute. One model simulates the mixing of solute in an observation well from individual layers in a multilayer aquifer system. Computer programs produce output files compatible with graphics software in which sensitivities are plotted as a function of either time or space. (USGS)
A study of deoxyribonucleotide metabolism and its relation to DNA synthesis. Supercomputer simulation and model-system analysis.

PubMed

Heinmets, F; Leary, R H

1991-06-01

A model system (1) was established to analyze purine and pyrimidine metabolism. This system has been expanded to include macrosimulation of DNA synthesis and the study of its regulation by terminal deoxynucleoside triphosphates (dNTPs) via a complex set of interactions. Computer experiments reveal that our model exhibits adequate and reasonable sensitivity in terms of dNTP pool levels and rates of DNA synthesis when inputs to the system are varied. These simulation experiments reveal that in order to achieve maximum DNA synthesis (in terms of purine metabolism), a proper balance is required in guanine and adenine input into this metabolic system. Excessive inputs will become inhibitory to DNA synthesis. In addition, studies are carried out on rates of DNA synthesis when various parameters are changed quantitatively. The current system is formulated by 110 differential equations.
Analysis of Discontinuity Induced Bifurcations in a Dual Input DC-DC Converter

NASA Astrophysics Data System (ADS)

Giaouris, Damian; Banerjee, Soumitro; Mandal, Kuntal; Al-Hindawi, Mohammed M.; Abusorrah, Abdullah; Al-Turki, Yusuf; El Aroudi, Abdelali

DC-DC power converters with multiple inputs and a single output are used in numerous applications where multiple sources, e.g. two or more renewable energy sources and/or a battery, feed a single load. In this work, a classical boost converter topology with two input branches connected to two different sources is chosen, with each branch independently being controlled by a separate peak current mode controller. We demonstrate for the first time that even though this converter is similar to other well known topologies that have been studied before, it exhibits many complex nonlinear behaviors that are not found in any other standard PWM controlled power converter. The system undergoes period incrementing cascade as a parameter is varied, with discontinuous hard transitions between consecutive periodicities. We show that the system can be described by a discontinuous map, which explains the observed bifurcation phenomena. The results have been experimentally validated.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Roberts, Jesse D.; Grace Chang; Jason Magalen

A n indust ry standard wave modeling tool was utilized to investigate model sensitivity to input parameters and wave energy converter ( WEC ) array deploym ent scenarios. Wave propagation was investigated d ownstream of the WECs to evaluate overall near - and far - field effects of WEC arrays. The sensitivity study illustrate d that b oth wave height and near - bottom orbital velocity we re subject to the largest pote ntial variations, each decreas ed in sensitivity as transmission coefficient increase d , as number and spacing of WEC devices decrease d , and as the deploymentmore » location move d offshore. Wave direction wa s affected consistently for all parameters and wave perio d was not affected (or negligibly affected) by varying model parameters or WEC configuration .« less
Exemplifying the Effects of Parameterization Shortcomings in the Numerical Simulation of Geological Energy and Mass Storage

NASA Astrophysics Data System (ADS)

Dethlefsen, Frank; Tilmann Pfeiffer, Wolf; Schäfer, Dirk

2016-04-01

Numerical simulations of hydraulic, thermal, geomechanical, or geochemical (THMC-) processes in the subsurface have been conducted for decades. Often, such simulations are commenced by applying a parameter set that is as realistic as possible. Then, a base scenario is calibrated on field observations. Finally, scenario simulations can be performed, for instance to forecast the system behavior after varying input data. In the context of subsurface energy and mass storage, however, these model calibrations based on field data are often not available, as these storage actions have not been carried out so far. Consequently, the numerical models merely rely on the parameter set initially selected, and uncertainties as a consequence of a lack of parameter values or process understanding may not be perceivable, not mentioning quantifiable. Therefore, conducting THMC simulations in the context of energy and mass storage deserves a particular review of the model parameterization with its input data, and such a review so far hardly exists to the required extent. Variability or aleatory uncertainty exists for geoscientific parameter values in general, and parameters for that numerous data points are available, such as aquifer permeabilities, may be described statistically thereby exhibiting statistical uncertainty. In this case, sensitivity analyses for quantifying the uncertainty in the simulation resulting from varying this parameter can be conducted. There are other parameters, where the lack of data quantity and quality implies a fundamental changing of ongoing processes when such a parameter value is varied in numerical scenario simulations. As an example for such a scenario uncertainty, varying the capillary entry pressure as one of the multiphase flow parameters can either allow or completely inhibit the penetration of an aquitard by gas. As the last example, the uncertainty of cap-rock fault permeabilities and consequently potential leakage rates of stored gases into shallow compartments are regarded as recognized ignorance by the authors of this study, as no realistic approach exists to determine this parameter and values are best guesses only. In addition to these aleatory uncertainties, an equivalent classification is possible for rating epistemic uncertainties describing the degree of understanding processes such as the geochemical and hydraulic effects following potential gas intrusions from deeper reservoirs into shallow aquifers. As an outcome of this grouping of uncertainties, prediction errors of scenario simulations can be calculated by sensitivity analyses, if the uncertainties are identified as statistical. However, if scenario uncertainties exist or even recognized ignorance has to be attested to a parameter or a process in question, the outcomes of simulations mainly depend on the decision of the modeler by choosing parameter values or by interpreting the occurring of processes. In that case, the informative value of numerical simulations is limited by ambiguous simulation results, which cannot be refined without improving the geoscientific database through laboratory or field studies on a longer term basis, so that the effects of the subsurface use may be predicted realistically. This discussion, amended by a compilation of available geoscientific data to parameterize such simulations, will be presented in this study.
Dependence of rates of breakage on fines content in wet ball mill grinding

NASA Astrophysics Data System (ADS)

Bhattacharyya, Anirban

The following research fundamentally deals with the cause and implications of nonlinearities in breakage rates of materials in wet grinding systems. The innate dependence of such nonlinearities on fines content and the milling environment during wet grinding operations is also tested and observed. Preferential breakage of coarser size fractions as compared to the finer size fractions in a particle population were observed and discussed. The classification action of the pulp was deemed to be the probable cause for such a peculiarity. Ores with varying degrees of hardness and brittleness were used for wet grinding experiments, primarily to test the variations in specific breakage rates as a function of varying hardness. For this research, limestone, quartzite, and gold ore were used. The degree of hardness is of the order of: limestone, quartzite, gold ore. Selection and breakage function parameters were determined in the course of this research. Functional forms of these expressions were used to compare experimentally derived parameter estimates. Force-fitting of parameters was not done in order to examine the realtime behavior of particle populations in wet grinding systems. Breakage functions were established as being invariant with respect to such operating variables like ball load, mill speed, particle load, and particle size distribution of the mill. It was also determined that specific selection functions were inherently dependent on the particle size distribution in wet grinding systems. Also, they were consistent with inputs of specific energy, according to grind time. Nonlinearity trends were observed for 1st order specific selection functions which illustrated variations in breakage rates with incremental inputs of grind time and specific energy. A mean particle size called the fulcrum was noted below which the nonlinearities in the breakage trends were observed. This magnitude of the fulcrum value varied with percent solids and slurry filling, indicating that breakage rates were being influenced by the milling environment as a whole. Primarily, there was always an increase in the breakage rates of coarser fractions with an increase in the amount of fines in the particle population. Consequently, the breakage rates of the finer size fractions were observed to decrease with an increase in grind time. Similar trends were noticed for 2nd order specific selection functions, where incremental inputs of specific energy were provided to observe realtime trends in the nonlinearity of breakage rates closely. Although the breakage rates for coarser size fractions increase with an increase in the amount of fines, the nature of nonlinearities varied with extended grind times. 1st order and 2nd order energy-specific breakage rates were observed to notice the variation in trends with extended grind times. Implications of such nonlinearities in specific breakage rates of various materials were tested on predictive simulation techniques, using the normalized linear population balance model and compared with an incremental methodology of specific energy input.
Development and Applications of Benchmark Examples for Static Delamination Propagation Predictions

NASA Technical Reports Server (NTRS)

Krueger, Ronald

2013-01-01

The development and application of benchmark examples for the assessment of quasistatic delamination propagation capabilities was demonstrated for ANSYS (TradeMark) and Abaqus/Standard (TradeMark). The examples selected were based on finite element models of Double Cantilever Beam (DCB) and Mixed-Mode Bending (MMB) specimens. First, quasi-static benchmark results were created based on an approach developed previously. Second, the delamination was allowed to propagate under quasi-static loading from its initial location using the automated procedure implemented in ANSYS (TradeMark) and Abaqus/Standard (TradeMark). Input control parameters were varied to study the effect on the computed delamination propagation. Overall, the benchmarking procedure proved valuable by highlighting the issues associated with choosing the appropriate input parameters for the VCCT implementations in ANSYS® and Abaqus/Standard®. However, further assessment for mixed-mode delamination fatigue onset and growth is required. Additionally studies should include the assessment of the propagation capabilities in more complex specimens and on a structural level.
Open-source LCA tool for estimating greenhouse gas emissions from crude oil production using field characteristics.

PubMed

El-Houjeiri, Hassan M; Brandt, Adam R; Duffy, James E

2013-06-04

Existing transportation fuel cycle emissions models are either general and calculate nonspecific values of greenhouse gas (GHG) emissions from crude oil production, or are not available for public review and auditing. We have developed the Oil Production Greenhouse Gas Emissions Estimator (OPGEE) to provide open-source, transparent, rigorous GHG assessments for use in scientific assessment, regulatory processes, and analysis of GHG mitigation options by producers. OPGEE uses petroleum engineering fundamentals to model emissions from oil and gas production operations. We introduce OPGEE and explain the methods and assumptions used in its construction. We run OPGEE on a small set of fictional oil fields and explore model sensitivity to selected input parameters. Results show that upstream emissions from petroleum production operations can vary from 3 gCO2/MJ to over 30 gCO2/MJ using realistic ranges of input parameters. Significant drivers of emissions variation are steam injection rates, water handling requirements, and rates of flaring of associated gas.
Bridging the gap between theoretical ecology and real ecosystems: modeling invertebrate community composition in streams.

PubMed

Schuwirth, Nele; Reichert, Peter

2013-02-01

For the first time, we combine concepts of theoretical food web modeling, the metabolic theory of ecology, and ecological stoichiometry with the use of functional trait databases to predict the coexistence of invertebrate taxa in streams. We developed a mechanistic model that describes growth, death, and respiration of different taxa dependent on various environmental influence factors to estimate survival or extinction. Parameter and input uncertainty is propagated to model results. Such a model is needed to test our current quantitative understanding of ecosystem structure and function and to predict effects of anthropogenic impacts and restoration efforts. The model was tested using macroinvertebrate monitoring data from a catchment of the Swiss Plateau. Even without fitting model parameters, the model is able to represent key patterns of the coexistence structure of invertebrates at sites varying in external conditions (litter input, shading, water quality). This confirms the suitability of the model concept. More comprehensive testing and resulting model adaptations will further increase the predictive accuracy of the model.
Application of neural network to remote sensing of soil moisture using theoretical polarimetric backscattering coefficients

NASA Technical Reports Server (NTRS)

Wang, L.; Shin, R. T.; Kong, J. A.; Yueh, S. H.

1993-01-01

This paper investigates the potential application of neural network to inversion of soil moisture using polarimetric remote sensing data. The neural network used for the inversion of soil parameters is multi-layer perceptron trained with the back-propagation algorithm. The training data include the polarimetric backscattering coefficients obtained from theoretical surface scattering models together with an assumed nominal range of soil parameters which are comprised of the soil permittivity and surface roughness parameters. Soil permittivity is calculated from the soil moisture and the assumed soil texture based on an empirical formula at C-, L-, and P-bands. The rough surface parameters for the soil surface, which is described by the Gaussian random process, are the root-mean-square (rms) height and correlation length. For the rough surface scattering, small perturbation method is used for the L-band frequency, and Kirchhoff approximation is used for the C-band frequency to obtain the corresponding backscattering coefficients. During the training, the backscattering coefficients are the inputs to the neural net and the output from the net are compared with the desired soil parameters to adjust the interconnecting weights. The process is repeated for each input-output data entry and then for the entire training data until convergence is reached. After training, the backscattering coefficients are applied to the trained neural net to retrieve the soil parameters which are compared with the desired soil parameters to verify the effectiveness of this technique. Several cases are examined. First, for simplicity, the correlation length and rms height of the soil surface are fixed while soil moisture is varied. Soil moisture obtained using the neural networks with either L-band or C-band backscattering coefficients for the HH and VV polarizations as inputs is in good agreement with the desired soil moisture. The neural net output matches the desired output for the soil moisture range of 16 to 60 percent for the C-band case. The next case investigated is to vary both soil moisture and rms height while keeping the correlation length fixed. For this case, C-band backscattering coefficients are not sufficient for retrieving two parameters because the Kirchhoff approximation gives the same HH and VV backscattering coefficients. Therefore, the backscattering coefficients at two different frequency bands are necessary to find both the soil moisture and rms height. Finally, the neural nets are also applied to simultaneously invert soil moisture, rms height, and correlation length. Overall, the soil moisture retrieved from the neural network agrees very well with the desired soil moisture. This suggests that the neural network shows potential for retrieval of soil parameters from remote sensing data.
Dynamics of mechanical feedback-type hydraulic servomotors under inertia loads

NASA Technical Reports Server (NTRS)

Gold, Harold; Otto, Edward W; Ransom, Victor L

1953-01-01

An analysis of the dynamics of mechanical feedback-type hydraulic servomotors under inertia loads is developed and experimental verification is presented. The analysis, which is developed in terms of two physical parameters, yields direct expressions for the following dynamic responses: (1) the transient response to a step input and the maximum cylinder pressure during the transient and (2) the variation of amplitude attenuation and phase shift with the frequency of a sinusoidally varying input. The validity of the analysis is demonstrated by means of recorded transient and frequency responses obtained on two servomotors. The calculated responses are in close agreement with the measured responses. The relations presented are readily applicable to the design as well as to the analysis of hydraulic servomotors.
Finite element modelling of squirrel, guinea pig and rat skulls: using geometric morphometrics to assess sensitivity.

PubMed

Cox, P G; Fagan, M J; Rayfield, E J; Jeffery, N

2011-12-01

Rodents are defined by a uniquely specialized dentition and a highly complex arrangement of jaw-closing muscles. Finite element analysis (FEA) is an ideal technique to investigate the biomechanical implications of these specializations, but it is essential to understand fully the degree of influence of the different input parameters of the FE model to have confidence in the model's predictions. This study evaluates the sensitivity of FE models of rodent crania to elastic properties of the materials, loading direction, and the location and orientation of the models' constraints. Three FE models were constructed of squirrel, guinea pig and rat skulls. Each was loaded to simulate biting on the incisors, and the first and the third molars, with the angle of the incisal bite varied over a range of 45°. The Young's moduli of the bone and teeth components were varied between limits defined by findings from our own and previously published tests of material properties. Geometric morphometrics (GMM) was used to analyse the resulting skull deformations. Bone stiffness was found to have the strongest influence on the results in all three rodents, followed by bite position, and then bite angle and muscle orientation. Tooth material properties were shown to have little effect on the deformation of the skull. The effect of bite position varied between species, with the mesiodistal position of the biting tooth being most important in squirrels and guinea pigs, whereas bilateral vs. unilateral biting had the greatest influence in rats. A GMM analysis of isolated incisor deformations showed that, for all rodents, bite angle is the most important parameter, followed by elastic properties of the tooth. The results here elucidate which input parameters are most important when defining the FE models, but also provide interesting glimpses of the biomechanical differences between the three skulls, which will be fully explored in future publications. © 2011 The Authors. Journal of Anatomy © 2011 Anatomical Society of Great Britain and Ireland.
Pulsed Nd:YAG laser welding of cardiac pacemaker batteries with reduced heat input

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

Fuerschbach, P.W.; Hinkley, D.A.

1997-03-01

The effects of Nd:YAG laser beam welding process parameters on the resulting heat input in 304L stainless steel cardiac pacemaker batteries have been studied. By careful selection of process parameters, the results can be used to reduce temperatures near glass-to-metal seals and assure hermeticity in laser beam welding of high reliability components. Three designed response surface experiments were used to compare welding performance with lenses of varying focal lengths. The measured peak temperatures at the glass-to-metal seals varied from 65 to 140 C (149 to 284 F) and depended strongly on the levels of the experimental factors. It was foundmore » that welds of equivalent size can be made with significantly reduced temperatures. The reduction in battery temperatures has been attributed to an increase in the melting efficiency. This increase is thought to be due primarily to increased travel speeds, which were facilitated by high peak powers and low pulse energies. For longer focal length lenses, weld fusion zone widths were found to be greater even without a corresponding increase in the size of the weld. It was also found that increases in laser beam irradiance either by higher peak powers or smaller spot sizes created deeper and larger welds. These gains were attributed to an increase in the laser energy transfer efficiency.« less

Evaluation of habitat suitability index models by global sensitivity and uncertainty analyses: a case study for submerged aquatic vegetation

USGS Publications Warehouse

Zajac, Zuzanna; Stith, Bradley M.; Bowling, Andrea C.; Langtimm, Catherine A.; Swain, Eric D.

2015-01-01

Habitat suitability index (HSI) models are commonly used to predict habitat quality and species distributions and are used to develop biological surveys, assess reserve and management priorities, and anticipate possible change under different management or climate change scenarios. Important management decisions may be based on model results, often without a clear understanding of the level of uncertainty associated with model outputs. We present an integrated methodology to assess the propagation of uncertainty from both inputs and structure of the HSI models on model outputs (uncertainty analysis: UA) and relative importance of uncertain model inputs and their interactions on the model output uncertainty (global sensitivity analysis: GSA). We illustrate the GSA/UA framework using simulated hydrology input data from a hydrodynamic model representing sea level changes and HSI models for two species of submerged aquatic vegetation (SAV) in southwest Everglades National Park: Vallisneria americana (tape grass) and Halodule wrightii (shoal grass). We found considerable spatial variation in uncertainty for both species, but distributions of HSI scores still allowed discrimination of sites with good versus poor conditions. Ranking of input parameter sensitivities also varied spatially for both species, with high habitat quality sites showing higher sensitivity to different parameters than low-quality sites. HSI models may be especially useful when species distribution data are unavailable, providing means of exploiting widely available environmental datasets to model past, current, and future habitat conditions. The GSA/UA approach provides a general method for better understanding HSI model dynamics, the spatial and temporal variation in uncertainties, and the parameters that contribute most to model uncertainty. Including an uncertainty and sensitivity analysis in modeling efforts as part of the decision-making framework will result in better-informed, more robust decisions.
Balancing the stochastic description of uncertainties as a function of hydrologic model complexity

NASA Astrophysics Data System (ADS)

Del Giudice, D.; Reichert, P.; Albert, C.; Kalcic, M.; Logsdon Muenich, R.; Scavia, D.; Bosch, N. S.; Michalak, A. M.

2016-12-01

Uncertainty analysis is becoming an important component of forecasting water and pollutant fluxes in urban and rural environments. Properly accounting for errors in the modeling process can help to robustly assess the uncertainties associated with the inputs (e.g. precipitation) and outputs (e.g. runoff) of hydrological models. In recent years we have investigated several Bayesian methods to infer the parameters of a mechanistic hydrological model along with those of the stochastic error component. The latter describes the uncertainties of model outputs and possibly inputs. We have adapted our framework to a variety of applications, ranging from predicting floods in small stormwater systems to nutrient loads in large agricultural watersheds. Given practical constraints, we discuss how in general the number of quantities to infer probabilistically varies inversely with the complexity of the mechanistic model. Most often, when evaluating a hydrological model of intermediate complexity, we can infer the parameters of the model as well as of the output error model. Describing the output errors as a first order autoregressive process can realistically capture the "downstream" effect of inaccurate inputs and structure. With simpler runoff models we can additionally quantify input uncertainty by using a stochastic rainfall process. For complex hydrologic transport models, instead, we show that keeping model parameters fixed and just estimating time-dependent output uncertainties could be a viable option. The common goal across all these applications is to create time-dependent prediction intervals which are both reliable (cover the nominal amount of validation data) and precise (are as narrow as possible). In conclusion, we recommend focusing both on the choice of the hydrological model and of the probabilistic error description. The latter can include output uncertainty only, if the model is computationally-expensive, or, with simpler models, it can separately account for different sources of errors like in the inputs and the structure of the model.
Robust fixed-time synchronization of delayed Cohen-Grossberg neural networks.

PubMed

Wan, Ying; Cao, Jinde; Wen, Guanghui; Yu, Wenwu

2016-01-01

The fixed-time master-slave synchronization of Cohen-Grossberg neural networks with parameter uncertainties and time-varying delays is investigated. Compared with finite-time synchronization where the convergence time relies on the initial synchronization errors, the settling time of fixed-time synchronization can be adjusted to desired values regardless of initial conditions. Novel synchronization control strategy for the slave neural network is proposed. By utilizing the Filippov discontinuous theory and Lyapunov stability theory, some sufficient schemes are provided for selecting the control parameters to ensure synchronization with required convergence time and in the presence of parameter uncertainties. Corresponding criteria for tuning control inputs are also derived for the finite-time synchronization. Finally, two numerical examples are given to illustrate the validity of the theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.
Characterization of the electrical output of flat-plate photovoltaic arrays

NASA Technical Reports Server (NTRS)

Gonzalez, C. C.; Hill, G. M.; Ross, R. G., Jr.

1982-01-01

The electric output of flat-plate photovoltaic arrays changes constantly, due primarily to changes in cell temperature and irradiance level. As a result, array loads such as direct-current to alternating-current power conditioners must be able to accommodate widely varying input levels, while maintaining operation at or near the array maximum power point.The results of an extensive computer simulation study that was used to define the parameters necessary for the systematic design of array/power-conditioner interfaces are presented as normalized ratios of power-conditioner parameters to array parameters, to make the results universally applicable to a wide variety of system sizes, sites, and operating modes. The advantages of maximum power tracking and a technique for computing average annual power-conditioner efficiency are discussed.
Analyzing Power Supply and Demand on the ISS

NASA Technical Reports Server (NTRS)

Thomas, Justin; Pham, Tho; Halyard, Raymond; Conwell, Steve

2006-01-01

Station Power and Energy Evaluation Determiner (SPEED) is a Java application program for analyzing the supply and demand aspects of the electrical power system of the International Space Station (ISS). SPEED can be executed on any computer that supports version 1.4 or a subsequent version of the Java Runtime Environment. SPEED includes an analysis module, denoted the Simplified Battery Solar Array Model, which is a simplified engineering model of the ISS primary power system. This simplified model makes it possible to perform analyses quickly. SPEED also includes a user-friendly graphical-interface module, an input file system, a parameter-configuration module, an analysis-configuration-management subsystem, and an output subsystem. SPEED responds to input information on trajectory, shadowing, attitude, and pointing in either a state-of-charge mode or a power-availability mode. In the state-of-charge mode, SPEED calculates battery state-of-charge profiles, given a time-varying power-load profile. In the power-availability mode, SPEED determines the time-varying total available solar array and/or battery power output, given a minimum allowable battery state of charge.
Development of a Phasor Diagram Creator to Visualize the Piston and Displacer Forces in an Advanced Stirling Convertor

NASA Technical Reports Server (NTRS)

Saha, Dipanjan; Lewandowski, Edward J.

2013-01-01

The steady-state, nearly sinusoidal behavior of the components in a free-piston Stirling engine allows for visualization of the forces in the system using phasor diagrams. Based on Newton's second law, F = ma, any phasor diagrams modeling a given component in a system should close if all of the acting forces have been considered. Since the Advanced Stirling Radioisotope Generator (ASRG), currently being developed for future NASA deep space missions, is made up of such nearly sinusoidally oscillating components, its phasor diagrams would also be expected to close. A graphical user interface (GUI) has been written in MATLAB (MathWorks), which takes user input data, passes it to Sage (Gedeon Associates), a one-dimensional thermodynamic modeling program used to model the Stirling convertor, runs Sage, and then automatically plots the phasor diagrams. Using this software tool, the effect of varying different Sage inputs on the phasor diagrams was determined. The parameters varied were piston amplitude, hot-end temperature, cold-end temperature, operating frequency, and displacer spring constant. These phasor diagrams offer useful insight into convertor operation and performance.
Simulating the Gradually Deteriorating Performance of an RTG

NASA Technical Reports Server (NTRS)

Wood, Eric G.; Ewell, Richard C.; Patel, Jagdish; Hanks, David R.; Lozano, Juan A.; Snyder, G. Jeffrey; Noon, Larry

2008-01-01

Degra (now in version 3) is a computer program that simulates the performance of a radioisotope thermoelectric generator (RTG) over its lifetime. Degra is provided with a graphical user interface that is used to edit input parameters that describe the initial state of the RTG and the time-varying loads and environment to which it will be exposed. Performance is computed by modeling the flows of heat from the radioactive source and through the thermocouples, also allowing for losses, to determine the temperature drop across the thermocouples. This temperature drop is used to determine the open-circuit voltage, electrical resistance, and thermal conductance of the thermocouples. Output power can then be computed by relating the open-circuit voltage and the electrical resistance of the thermocouples to a specified time-varying load voltage. Degra accounts for the gradual deterioration of performance attributable primarily to decay of the radioactive source and secondarily to gradual deterioration of the thermoelectric material. To provide guidance to an RTG designer, given a minimum of input, Degra computes the dimensions, masses, and thermal conductances of important internal structures as well as the overall external dimensions and total mass.
Petroleum pollution in surface sediments of Daya Bay, South China, revealed by chemical fingerprinting of aliphatic and alicyclic hydrocarbons

NASA Astrophysics Data System (ADS)

Gao, Xuelu; Chen, Shaoyong

2008-10-01

Nine surface sediments collected from Daya Bay have been Soxhlet-extracted with 2:1 (v/v) dichloromethane-methanol. The non-aromatic hydrocarbon (NAH) fraction of solvent extractable organic matter (EOM) and some bulk geochemical parameters have been analyzed to determine petroleum pollution of the bay. The NAH content varies from 32 to 276 μg g -1 (average 104 μg g -1) dry sediment and accounts for 5.8-64.1% (average 41.6%) of the EOM. n-Alkanes with carbon number ranging from 15 to 35 are identified to be derived from both biogenic and petrogenic sources in varying proportions. The contribution of marine authigenic input to the sedimentary n-alkanes is lower than the allochthonous input based on the average n-C 31/ n-C 19 alkane ratio. 25.6-46.5% of the n-alkanes, with a mean of 35.6%, are contributed by vascular plant wax. Results of unresolved complex mixture, isoprenoid hydrocarbons, hopanes and steranes also suggest possible petroleum contamination. There is strong evidence of a common petroleum contamination source in the bay.
Experimental benchmark of the NINJA code for application to the Linac4 H- ion source plasma

NASA Astrophysics Data System (ADS)

Briefi, S.; Mattei, S.; Rauner, D.; Lettry, J.; Tran, M. Q.; Fantz, U.

2017-10-01

For a dedicated performance optimization of negative hydrogen ion sources applied at particle accelerators, a detailed assessment of the plasma processes is required. Due to the compact design of these sources, diagnostic access is typically limited to optical emission spectroscopy yielding only line-of-sight integrated results. In order to allow for a spatially resolved investigation, the electromagnetic particle-in-cell Monte Carlo collision code NINJA has been developed for the Linac4 ion source at CERN. This code considers the RF field generated by the ICP coil as well as the external static magnetic fields and calculates self-consistently the resulting discharge properties. NINJA is benchmarked at the diagnostically well accessible lab experiment CHARLIE (Concept studies for Helicon Assisted RF Low pressure Ion sourcEs) at varying RF power and gas pressure. A good general agreement is observed between experiment and simulation although the simulated electron density trends for varying pressure and power as well as the absolute electron temperature values deviate slightly from the measured ones. This can be explained by the assumption of strong inductive coupling in NINJA, whereas the CHARLIE discharges show the characteristics of loosely coupled plasmas. For the Linac4 plasma, this assumption is valid. Accordingly, both the absolute values of the accessible plasma parameters and their trends for varying RF power agree well in measurement and simulation. At varying RF power, the H- current extracted from the Linac4 source peaks at 40 kW. For volume operation, this is perfectly reflected by assessing the processes in front of the extraction aperture based on the simulation results where the highest H- density is obtained for the same power level. In surface operation, the production of negative hydrogen ions at the converter surface can only be considered by specialized beam formation codes, which require plasma parameters as input. It has been demonstrated that this input can be provided reliably by the NINJA code.
Material appearance acquisition from a single image

NASA Astrophysics Data System (ADS)

Zhang, Xu; Cui, Shulin; Cui, Hanwen; Yang, Lin; Wu, Tao

2017-01-01

The scope of this paper is to present a method of material appearance acquisition(MAA) from a single image. In this paper, material appearance is represented by spatially varying bidirectional reflectance distribution function(SVBRDF). Therefore, MAA can be reduced to the problem of recovery of each pixel's BRDF parameters from an original input image, which include diffuse coefficient, specular coefficient, normal and glossiness based on the Blinn-Phone model. In our method, the workflow of MAA includes five main phases: highlight removal, estimation of intrinsic images, shape from shading(SFS), initialization of glossiness and refining SVBRDF parameters based on IPOPT. The results indicate that the proposed technique can effectively extract the material appearance from a single image.
MRAC Control with Prior Model Knowledge for Asymmetric Damaged Aircraft

PubMed Central

Zhang, Jing

2015-01-01

This paper develops a novel state-tracking multivariable model reference adaptive control (MRAC) technique utilizing prior knowledge of plant models to recover control performance of an asymmetric structural damaged aircraft. A modification of linear model representation is given. With prior knowledge on structural damage, a polytope linear parameter varying (LPV) model is derived to cover all concerned damage conditions. An MRAC method is developed for the polytope model, of which the stability and asymptotic error convergence are theoretically proved. The proposed technique reduces the number of parameters to be adapted and thus decreases computational cost and requires less input information. The method is validated by simulations on NASA generic transport model (GTM) with damage. PMID:26180839
DOE Office of Scientific and Technical Information (OSTI.GOV)

Vaysset, Adrien; Manfrini, Mauricio; Pourtois, Geoffrey

The functionality of a cross-shaped Spin Torque Majority Gate is explored by means of micromagnetic simulations. The different input combinations are simulated varying material parameters, current density and size. The main failure mode is identified: above a critical size, a domain wall can be pinned at the center of the cross, preventing further propagation of the information. By simulating several phase diagrams, the key parameters are obtained and the operating condition is deduced. A simple relation between the domain wall width and the size of the Spin Torque Majority Gate determines the working range. Finally, a correlation is found betweenmore » the energy landscape and the main failure mode. We demonstrate that a macrospin behavior ensures a reliable majority gate operation.« less
Potential efficiencies of open- and closed-cycle CO, supersonic, electric-discharge lasers

NASA Technical Reports Server (NTRS)

Monson, D. J.

1976-01-01

Computed open- and closed-cycle system efficiencies (laser power output divided by electrical power input) are presented for a CW carbon monoxide, supersonic, electric-discharge laser. Closed-system results include the compressor power required to overcome stagnation pressure losses due to supersonic heat addition and a supersonic diffuser. The paper shows the effect on the system efficiencies of varying several important parameters. These parameters include: gas mixture, gas temperature, gas total temperature, gas density, total discharge energy loading, discharge efficiency, saturated gain coefficient, optical cavity size and location with respect to the discharge, and supersonic diffuser efficiency. Maximum open-cycle efficiency of 80-90% is predicted; the best closed-cycle result is 60-70%.
Modeling clear-sky solar radiation across a range of elevations in Hawai‘i: Comparing the use of input parameters at different temporal resolutions

NASA Astrophysics Data System (ADS)

Longman, Ryan J.; Giambelluca, Thomas W.; Frazier, Abby G.

2012-01-01

Estimates of clear sky global solar irradiance using the parametric model SPCTRAL2 were tested against clear sky radiation observations at four sites in Hawai`i using daily, mean monthly, and 1 year mean model parameter settings. Atmospheric parameters in SPCTRAL2 and similar models are usually set at site-specific values and are not varied to represent the effects of fluctuating humidity, aerosol amount and type, or ozone concentration, because time-dependent atmospheric parameter estimates are not available at most sites of interest. In this study, we sought to determine the added value of using time dependent as opposed to fixed model input parameter settings. At the AERONET site, Mauna Loa Observatory (MLO) on the island of Hawai`i, where daily measurements of atmospheric optical properties and hourly solar radiation observations are available, use of daily rather than 1 year mean aerosol parameter values reduced mean bias error (MBE) from 18 to 10 W m-2 and root mean square error from 25 to 17 W m-2. At three stations in the HaleNet climate network, located at elevations of 960, 1640, and 2590 m on the island of Maui, where aerosol-related parameter settings were interpolated from observed values for AERONET sites at MLO (3397 m) and Lāna`i (20 m), and precipitable water was estimated using radiosonde-derived humidity profiles from nearby Hilo, the model performed best when using constant 1 year mean parameter values. At HaleNet Station 152, for example, MBE was 18, 10, and 8 W m-2 for daily, monthly, and 1 year mean parameters, respectively.
Aerodynamic parameter estimation via Fourier modulating function techniques

NASA Technical Reports Server (NTRS)

Pearson, A. E.

1995-01-01

Parameter estimation algorithms are developed in the frequency domain for systems modeled by input/output ordinary differential equations. The approach is based on Shinbrot's method of moment functionals utilizing Fourier based modulating functions. Assuming white measurement noises for linear multivariable system models, an adaptive weighted least squares algorithm is developed which approximates a maximum likelihood estimate and cannot be biased by unknown initial or boundary conditions in the data owing to a special property attending Shinbrot-type modulating functions. Application is made to perturbation equation modeling of the longitudinal and lateral dynamics of a high performance aircraft using flight-test data. Comparative studies are included which demonstrate potential advantages of the algorithm relative to some well established techniques for parameter identification. Deterministic least squares extensions of the approach are made to the frequency transfer function identification problem for linear systems and to the parameter identification problem for a class of nonlinear-time-varying differential system models.
Simulations of a FIR Oscillator with Large Slippage parameter at Jefferson Lab for FIR/UV pump-probe experiments

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

Benson, Stephen V.; Campbell, L. T.; McNeil, B.W.T.

We previously proposed a dual FEL configuration on the UV Demo FEL at Jefferson Lab that would allow simultaneous lasing at FIR and UV wavelengths. The FIR source would be an FEL oscillator with a short wiggler providing diffraction-limited pulses with pulse energy exceeding 50 microJoules, using the exhaust beam from a UVFEL as the input electron beam. Since the UV FEL requires very short pulses, the input to the FIR FEL is extremely short compared to a slippage length and the usual Slowly Varying Envelope Approximation (SVEA) does not apply. We use a non-SVEA code to simulate this systemmore » both with a small energy spread (UV laser off) and with large energy spread (UV laser on).« less
Optimization Under Uncertainty for Electronics Cooling Design

NASA Astrophysics Data System (ADS)

Bodla, Karthik K.; Murthy, Jayathi Y.; Garimella, Suresh V.

Optimization under uncertainty is a powerful methodology used in design and optimization to produce robust, reliable designs. Such an optimization methodology, employed when the input quantities of interest are uncertain, produces output uncertainties, helping the designer choose input parameters that would result in satisfactory thermal solutions. Apart from providing basic statistical information such as mean and standard deviation in the output quantities, auxiliary data from an uncertainty based optimization, such as local and global sensitivities, help the designer decide the input parameter(s) to which the output quantity of interest is most sensitive. This helps the design of experiments based on the most sensitive input parameter(s). A further crucial output of such a methodology is the solution to the inverse problem - finding the allowable uncertainty range in the input parameter(s), given an acceptable uncertainty range in the output quantity of interest...
Synthesis of laughter by modifying excitation characteristics.

PubMed

Thati, Sathya Adithya; Kumar K, Sudheer; Yegnanarayana, B

2013-05-01

In this paper, a method to synthesize laughter by modifying the excitation source information is presented. The excitation source information is derived by extracting epoch locations and instantaneous fundamental frequency using zero frequency filtering approach. The zero frequency filtering approach is modified to capture the rapidly varying instantaneous fundamental frequency in natural laugh signals. The nature of variation of excitation features in natural laughter is examined to determine the features to be incorporated in the synthesis of a laugh signal. Features such as pitch period and strength of excitation are modified in the utterance of vowel /a/ or /i/ to generate the laughter signal. Frication is also incorporated wherever appropriate. Laugh signal is generated by varying parameters at both call level and bout level. Experiments are conducted to determine the significance of different features in the perception of laughter. Subjective evaluation is performed to determine the level of acceptance and quality of synthesis of the synthesized laughter signal for different choices of parameter values and for different input types.
Back Propagation Neural Network Model for Predicting the Performance of Immobilized Cell Biofilters Handling Gas-Phase Hydrogen Sulphide and Ammonia

PubMed Central

Rene, Eldon R.; López, M. Estefanía; Kim, Jung Hoon; Park, Hung Suck

2013-01-01

Lab scale studies were conducted to evaluate the performance of two simultaneously operated immobilized cell biofilters (ICBs) for removing hydrogen sulphide (H2S) and ammonia (NH3) from gas phase. The removal efficiencies (REs) of the biofilter treating H2S varied from 50 to 100% at inlet loading rates (ILRs) varying up to 13 g H2S/m3 ·h, while the NH3 biofilter showed REs ranging from 60 to 100% at ILRs varying between 0.5 and 5.5 g NH3/m3 ·h. An application of the back propagation neural network (BPNN) to predict the performance parameter, namely, RE (%) using this experimental data is presented in this paper. The input parameters to the network were unit flow (per min) and inlet concentrations (ppmv), respectively. The accuracy of BPNN-based model predictions were evaluated by providing the trained network topology with a test dataset and also by calculating the regression coefficient (R 2) values. The results from this predictive modeling work showed that BPNNs were able to predict the RE of both the ICBs efficiently. PMID:24307999
Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring

NASA Technical Reports Server (NTRS)

Peck, Charles C.; Dhawan, Atam P.; Meyer, Claudia M.

1991-01-01

A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the space shuttle main engine (SSME), the functional relationship between measured parameters is unknown and complex. Furthermore, the number of possible input parameters is quite large. Many approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms they were employed to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.

Analyzing Spacecraft Telecommunication Systems

NASA Technical Reports Server (NTRS)

Kordon, Mark; Hanks, David; Gladden, Roy; Wood, Eric

2004-01-01

Multi-Mission Telecom Analysis Tool (MMTAT) is a C-language computer program for analyzing proposed spacecraft telecommunication systems. MMTAT utilizes parameterized input and computational models that can be run on standard desktop computers to perform fast and accurate analyses of telecommunication links. MMTAT is easy to use and can easily be integrated with other software applications and run as part of almost any computational simulation. It is distributed as either a stand-alone application program with a graphical user interface or a linkable library with a well-defined set of application programming interface (API) calls. As a stand-alone program, MMTAT provides both textual and graphical output. The graphs make it possible to understand, quickly and easily, how telecommunication performance varies with variations in input parameters. A delimited text file that can be read by any spreadsheet program is generated at the end of each run. The API in the linkable-library form of MMTAT enables the user to control simulation software and to change parameters during a simulation run. Results can be retrieved either at the end of a run or by use of a function call at any time step.
Relative sensitivities of DCE-MRI pharmacokinetic parameters to arterial input function (AIF) scaling

NASA Astrophysics Data System (ADS)

Li, Xin; Cai, Yu; Moloney, Brendan; Chen, Yiyi; Huang, Wei; Woods, Mark; Coakley, Fergus V.; Rooney, William D.; Garzotto, Mark G.; Springer, Charles S.

2016-08-01

Dynamic-Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been used widely for clinical applications. Pharmacokinetic modeling of DCE-MRI data that extracts quantitative contrast reagent/tissue-specific model parameters is the most investigated method. One of the primary challenges in pharmacokinetic analysis of DCE-MRI data is accurate and reliable measurement of the arterial input function (AIF), which is the driving force behind all pharmacokinetics. Because of effects such as inflow and partial volume averaging, AIF measured from individual arteries sometimes require amplitude scaling for better representation of the blood contrast reagent (CR) concentration time-courses. Empirical approaches like blinded AIF estimation or reference tissue AIF derivation can be useful and practical, especially when there is no clearly visible blood vessel within the imaging field-of-view (FOV). Similarly, these approaches generally also require magnitude scaling of the derived AIF time-courses. Since the AIF varies among individuals even with the same CR injection protocol and the perfect scaling factor for reconstructing the ground truth AIF often remains unknown, variations in estimated pharmacokinetic parameters due to varying AIF scaling factors are of special interest. In this work, using simulated and real prostate cancer DCE-MRI data, we examined parameter variations associated with AIF scaling. Our results show that, for both the fast-exchange-limit (FXL) Tofts model and the water exchange sensitized fast-exchange-regime (FXR) model, the commonly fitted CR transfer constant (Ktrans) and the extravascular, extracellular volume fraction (ve) scale nearly proportionally with the AIF, whereas the FXR-specific unidirectional cellular water efflux rate constant, kio, and the CR intravasation rate constant, kep, are both AIF scaling insensitive. This indicates that, for DCE-MRI of prostate cancer and possibly other cancers, kio and kep may be more suitable imaging biomarkers for cross-platform, multicenter applications. Data from our limited study cohort show that kio correlates with Gleason scores, suggesting that it may be a useful biomarker for prostate cancer disease progression monitoring.
A tuning algorithm for model predictive controllers based on genetic algorithms and fuzzy decision making.

PubMed

van der Lee, J H; Svrcek, W Y; Young, B R

2008-01-01

Model Predictive Control is a valuable tool for the process control engineer in a wide variety of applications. Because of this the structure of an MPC can vary dramatically from application to application. There have been a number of works dedicated to MPC tuning for specific cases. Since MPCs can differ significantly, this means that these tuning methods become inapplicable and a trial and error tuning approach must be used. This can be quite time consuming and can result in non-optimum tuning. In an attempt to resolve this, a generalized automated tuning algorithm for MPCs was developed. This approach is numerically based and combines a genetic algorithm with multi-objective fuzzy decision-making. The key advantages to this approach are that genetic algorithms are not problem specific and only need to be adapted to account for the number and ranges of tuning parameters for a given MPC. As well, multi-objective fuzzy decision-making can handle qualitative statements of what optimum control is, in addition to being able to use multiple inputs to determine tuning parameters that best match the desired results. This is particularly useful for multi-input, multi-output (MIMO) cases where the definition of "optimum" control is subject to the opinion of the control engineer tuning the system. A case study will be presented in order to illustrate the use of the tuning algorithm. This will include how different definitions of "optimum" control can arise, and how they are accounted for in the multi-objective decision making algorithm. The resulting tuning parameters from each of the definition sets will be compared, and in doing so show that the tuning parameters vary in order to meet each definition of optimum control, thus showing the generalized automated tuning algorithm approach for tuning MPCs is feasible.
Visual Predictive Check in Models with Time-Varying Input Function.

PubMed

Largajolli, Anna; Bertoldo, Alessandra; Campioni, Marco; Cobelli, Claudio

2015-11-01

The nonlinear mixed effects models are commonly used modeling techniques in the pharmaceutical research as they enable the characterization of the individual profiles together with the population to which the individuals belong. To ensure a correct use of them is fundamental to provide powerful diagnostic tools that are able to evaluate the predictive performance of the models. The visual predictive check (VPC) is a commonly used tool that helps the user to check by visual inspection if the model is able to reproduce the variability and the main trend of the observed data. However, the simulation from the model is not always trivial, for example, when using models with time-varying input function (IF). In this class of models, there is a potential mismatch between each set of simulated parameters and the associated individual IF which can cause an incorrect profile simulation. We introduce a refinement of the VPC by taking in consideration a correlation term (the Mahalanobis or normalized Euclidean distance) that helps the association of the correct IF with the individual set of simulated parameters. We investigate and compare its performance with the standard VPC in models of the glucose and insulin system applied on real and simulated data and in a simulated pharmacokinetic/pharmacodynamic (PK/PD) example. The newly proposed VPC performance appears to be better with respect to the standard VPC especially for the models with big variability in the IF where the probability of simulating incorrect profiles is higher.
Space shuttle SRM plume expansion sensitivity analysis. [flow characteristics of exhaust gases from solid propellant rocket engines

NASA Technical Reports Server (NTRS)

Smith, S. D.; Tevepaugh, J. A.; Penny, M. M.

1975-01-01

The exhaust plumes of the space shuttle solid rocket motors can have a significant effect on the base pressure and base drag of the shuttle vehicle. A parametric analysis was conducted to assess the sensitivity of the initial plume expansion angle of analytical solid rocket motor flow fields to various analytical input parameters and operating conditions. The results of the analysis are presented and conclusions reached regarding the sensitivity of the initial plume expansion angle to each parameter investigated. Operating conditions parametrically varied were chamber pressure, nozzle inlet angle, nozzle throat radius of curvature ratio and propellant particle loading. Empirical particle parameters investigated were mean size, local drag coefficient and local heat transfer coefficient. Sensitivity of the initial plume expansion angle to gas thermochemistry model and local drag coefficient model assumptions were determined.
The system controlling the composition of clastic sediments

USGS Publications Warehouse

Johnsson, Mark J.

1993-01-01

The composition of clastic sediments and rocks is controlled by a complex suite of parameters operating during pedogenesis, erosion, transport, deposition, and burial. The principal first-order parameters include source rock composition, modification by chemical weathering, mechanical disaggregation and abrasion, authigenic inputs, hydrodynamic sorting, and diagenesis. Each of these first-order parameters is influenced to varying degrees by such factors as the tectonic settings of the source region, transportational system and depositional environment, climate, vegetation, relief, slope, and the nature and energy of transportational and depositional systems. These factors are not independent; rather a complicated web of interrelationships and feedback mechanisms causes many factors to be modulated by others. Accordingly, processes controlling the composition of clastic sediments are best viewed as constituting a system, and in evaluating compositional information the dynamics of the system must be considered as whole.
Practical input optimization for aircraft parameter estimation experiments. Ph.D. Thesis, 1990

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

1993-01-01

The object of this research was to develop an algorithm for the design of practical, optimal flight test inputs for aircraft parameter estimation experiments. A general, single pass technique was developed which allows global optimization of the flight test input design for parameter estimation using the principles of dynamic programming with the input forms limited to square waves only. Provision was made for practical constraints on the input, including amplitude constraints, control system dynamics, and selected input frequency range exclusions. In addition, the input design was accomplished while imposing output amplitude constraints required by model validity and considerations of safety during the flight test. The algorithm has multiple input design capability, with optional inclusion of a constraint that only one control move at a time, so that a human pilot can implement the inputs. It is shown that the technique can be used to design experiments for estimation of open loop model parameters from closed loop flight test data. The report includes a new formulation of the optimal input design problem, a description of a new approach to the solution, and a summary of the characteristics of the algorithm, followed by three example applications of the new technique which demonstrate the quality and expanded capabilities of the input designs produced by the new technique. In all cases, the new input design approach showed significant improvement over previous input design methods in terms of achievable parameter accuracies.
ParamAP: Standardized Parameterization of Sinoatrial Node Myocyte Action Potentials.

PubMed

Rickert, Christian; Proenza, Catherine

2017-08-22

Sinoatrial node myocytes act as cardiac pacemaker cells by generating spontaneous action potentials (APs). Much information is encoded in sinoatrial AP waveforms, but both the analysis and the comparison of AP parameters between studies is hindered by the lack of standardized parameter definitions and the absence of automated analysis tools. Here we introduce ParamAP, a standalone cross-platform computational tool that uses a template-free detection algorithm to automatically identify and parameterize APs from text input files. ParamAP employs a graphic user interface with automatic and user-customizable input modes, and it outputs data files in text and PDF formats. ParamAP returns a total of 16 AP waveform parameters including time intervals such as the AP duration, membrane potentials such as the maximum diastolic potential, and rates of change of the membrane potential such as the diastolic depolarization rate. ParamAP provides a robust AP detection algorithm in combination with a standardized AP parameter analysis over a wide range of AP waveforms and firing rates, owing in part to the use of an iterative algorithm for the determination of the threshold potential and the diastolic depolarization rate that is independent of the maximum upstroke velocity, a parameter that can vary significantly among sinoatrial APs. Because ParamAP is implemented in Python 3, it is also highly customizable and extensible. In conclusion, ParamAP is a powerful computational tool that facilitates quantitative analysis and enables comparison of sinoatrial APs by standardizing parameter definitions and providing an automated work flow. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.
A hierarchical stress release model for synthetic seismicity

NASA Astrophysics Data System (ADS)

Bebbington, Mark

1997-06-01

We construct a stochastic dynamic model for synthetic seismicity involving stochastic stress input, release, and transfer in an environment of heterogeneous strength and interacting segments. The model is not fault-specific, having a number of adjustable parameters with physical interpretation, namely, stress relaxation, stress transfer, stress dissipation, segment structure, strength, and strength heterogeneity, which affect the seismicity in various ways. Local parameters are chosen to be consistent with large historical events, other parameters to reproduce bulk seismicity statistics for the fault as a whole. The one-dimensional fault is divided into a number of segments, each comprising a varying number of nodes. Stress input occurs at each node in a simple random process, representing the slow buildup due to tectonic plate movements. Events are initiated, subject to a stochastic hazard function, when the stress on a node exceeds the local strength. An event begins with the transfer of excess stress to neighboring nodes, which may in turn transfer their excess stress to the next neighbor. If the event grows to include the entire segment, then most of the stress on the segment is transferred to neighboring segments (or dissipated) in a characteristic event. These large events may themselves spread to other segments. We use the Middle America Trench to demonstrate that this model, using simple stochastic stress input and triggering mechanisms, can produce behavior consistent with the historical record over five units of magnitude. We also investigate the effects of perturbing various parameters in order to show how the model might be tailored to a specific fault structure. The strength of the model lies in this ability to reproduce the behavior of a general linear fault system through the choice of a relatively small number of parameters. It remains to develop a procedure for estimating the internal state of the model from the historical observations in order to use the model for forward prediction.
Reactor performance of a 750 m(3) anaerobic digestion plant: varied substrate input conditions impacting methanogenic community.

PubMed

Wagner, Andreas Otto; Malin, Cornelia; Lins, Philipp; Gstraunthaler, Gudrun; Illmer, Paul

2014-10-01

A 750 m(3) anaerobic digester was studied over a half year period including a shift from good reactor performance to a reduced one. Various abiotic parameters like volatile fatty acids (VFA) (formic-, acetic-, propionic-, (iso-)butyric-, (iso-)valeric-, lactic acid), total C, total N, NH4 -N, and total proteins, as well as the organic matter content and dry mass were determined. In addition several process parameters such as temperature, pH, retention time and input of substrate and the concentrations of CH4, H2, CO2 and H2S within the reactor were monitored continuously. The present study aimed at the investigation of the abundance of acetogens and total cell numbers and the microbial methanogenic community as derived from PCR-dHPLC analysis in order to put it into context with the determined abiotic parameters. An influence of substrate quantity on the efficiency of the anaerobic digestion process was found as well as a shift from a hydrogenotrophic in times of good reactor performance towards an acetoclastic dominated methanogenic community in times of reduced reactor performance. After the change in substrate conditions it took the methano-archaeal community about 5-6 weeks to be affected but then changes occurred quickly. Copyright © 2014 Elsevier Ltd. All rights reserved.
Preserving information in neural transmission.

PubMed

Sincich, Lawrence C; Horton, Jonathan C; Sharpee, Tatyana O

2009-05-13

Along most neural pathways, the spike trains transmitted from one neuron to the next are altered. In the process, neurons can either achieve a more efficient stimulus representation, or extract some biologically important stimulus parameter, or succeed at both. We recorded the inputs from single retinal ganglion cells and the outputs from connected lateral geniculate neurons in the macaque to examine how visual signals are relayed from retina to cortex. We found that geniculate neurons re-encoded multiple temporal stimulus features to yield output spikes that carried more information about stimuli than was available in each input spike. The coding transformation of some relay neurons occurred with no decrement in information rate, despite output spike rates that averaged half the input spike rates. This preservation of transmitted information was achieved by the short-term summation of inputs that geniculate neurons require to spike. A reduced model of the retinal and geniculate visual responses, based on two stimulus features and their associated nonlinearities, could account for >85% of the total information available in the spike trains and the preserved information transmission. These results apply to neurons operating on a single time-varying input, suggesting that synaptic temporal integration can alter the temporal receptive field properties to create a more efficient representation of visual signals in the thalamus than the retina.
Plasma property and performance prediction for mercury ion thrusters

NASA Technical Reports Server (NTRS)

Longhurst, G. R.; Wilbur, P. J.

1979-01-01

The discharge chambers of mercury ion thrusters are modelled so the principal effects and processes which govern discharge plasma properties and thruster performance are described. The conservation relations for mass, charge and energy when applied to the Maxwellian electron population in the ion production region yield equations which may be made one-dimensional by the proper choice of coordinates. Solutions to these equations with the appropriate boundary conditions give electron density and temperature profiles which agree reasonably well with measurements. It is then possible to estimate plasma properties from thruster design data and those operating parameters which are directly controllable. By varying the operating parameter inputs to the computer code written to solve these equations, perfromance curves are obtained which agree quite well with measurements.
Is there a `universal' dynamic zero-parameter hydrological model? Evaluation of a dynamic Budyko model in US and India

NASA Astrophysics Data System (ADS)

Patnaik, S.; Biswal, B.; Sharma, V. C.

2017-12-01

River flow varies greatly in space and time, and the single biggest challenge for hydrologists and ecologists around the world is the fact that most rivers are either ungauged or poorly gauged. Although it is relatively easier to predict long-term average flow of a river using the `universal' zero-parameter Budyko model, lack of data hinders short-term flow prediction at ungauged locations using traditional hydrological models as they require observed flow data for model calibration. Flow prediction in ungauged basins thus requires a dynamic 'zero-parameter' hydrological model. One way to achieve this is to regionalize a dynamic hydrological model's parameters. However, a regionalization method based zero-parameter dynamic hydrological model is not `universal'. An alternative attempt was made recently to develop a zero-parameter dynamic model by defining an instantaneous dryness index as a function of antecedent rainfall and solar energy inputs with the help of a decay function and using the original Budyko function. The model was tested first in 63 US catchments and later in 50 Indian catchments. The median Nash-Sutcliffe efficiency (NSE) was found to be close to 0.4 in both the cases. Although improvements need to be incorporated in order to use the model for reliable prediction, the main aim of this study was to rather understand hydrological processes. The overall results here seem to suggest that the dynamic zero-parameter Budyko model is `universal.' In other words natural catchments around the world are strikingly similar to each other in the way they respond to hydrologic inputs; we thus need to focus more on utilizing catchment similarities in hydrological modelling instead of over parameterizing our models.
European nitrogen policies, nitrate in rivers and the use of the INCA model

NASA Astrophysics Data System (ADS)

Skeffington, R.

This paper is concerned with nitrogen inputs to European catchments, how they are likely to change in future, and the implications for the INCA model. National N budgets show that the fifteen countries currently in the European Union (the EU-15 countries) probably have positive N balances - that is, N inputs exceed outputs. The major sources are atmospheric deposition, fertilisers and animal feed, the relative importance of which varies between countries. The magnitude of the fluxes which determine the transport and retention of N in catchments is also very variable in both space and time. The most important of these fluxes are parameterised directly or indirectly in the INCA Model, though it is doubtful whether the present version of the model is flexible enough to encompass short-term (daily) variations in inputs or longer-term (decadal) changes in soil parameters. As an aid to predicting future changes in deposition, international legislation relating to atmospheric N inputs and nitrate in rivers is reviewed briefly. Atmospheric N deposition and fertiliser use are likely to decrease over the next 10 years, but probably not sufficiently to balance national N budgets.
Development of a Stochastically-driven, Forward Predictive Performance Model for PEMFCs

NASA Astrophysics Data System (ADS)

Harvey, David Benjamin Paul

A one-dimensional multi-scale coupled, transient, and mechanistic performance model for a PEMFC membrane electrode assembly has been developed. The model explicitly includes each of the 5 layers within a membrane electrode assembly and solves for the transport of charge, heat, mass, species, dissolved water, and liquid water. Key features of the model include the use of a multi-step implementation of the HOR reaction on the anode, agglomerate catalyst sub-models for both the anode and cathode catalyst layers, a unique approach that links the composition of the catalyst layer to key properties within the agglomerate model and the implementation of a stochastic input-based approach for component material properties. The model employs a new methodology for validation using statistically varying input parameters and statistically-based experimental performance data; this model represents the first stochastic input driven unit cell performance model. The stochastic input driven performance model was used to identify optimal ionomer content within the cathode catalyst layer, demonstrate the role of material variation in potential low performing MEA materials, provide explanation for the performance of low-Pt loaded MEAs, and investigate the validity of transient-sweep experimental diagnostic methods.
Metallurgy and mechanical properties variation with heat input,during dissimilar metal welding between stainless and carbon steel

NASA Astrophysics Data System (ADS)

Ramdan, RD; Koswara, AL; Surasno; Wirawan, R.; Faturohman, F.; Widyanto, B.; Suratman, R.

2018-02-01

The present research focus on the metallurgy and mechanical aspect of dissimilar metal welding.One of the common parameters that significantly contribute to the metallurgical aspect on the metal during welding is heat input. Regarding this point, in the present research, voltage, current and the welding speed has been varied in order to observe the effect of heat input on the metallurgical and mechanical aspect of both welded metals. Welding was conducted by Gas Metal Arc Welding (GMAW) on stainless and carbon steel with filler metal of ER 309. After welding, hardness test (micro-Vickers), tensile test, macro and micro-structure characterization and Energy Dispersive Spectroscopy (EDS) characterization were performed. It was observed no brittle martensite observed at HAZ of carbon steel, whereas sensitization was observed at the HAZ of stainless steel for all heat input variation at the present research. Generally, both HAZ at carbon steel and stainless steel did not affect tensile test result, however the formation of chromium carbide at the grain boundary of HAZ structure (sensitization) of stainless steel, indicate that better process and control of welding is required for dissimilar metal welding, especially to overcome this issue.
Propagation of hypergeometric Gaussian beams in strongly nonlocal nonlinear media

NASA Astrophysics Data System (ADS)

Tang, Bin; Bian, Lirong; Zhou, Xin; Chen, Kai

2018-01-01

Optical vortex beams have attracted lots of interest due to its potential application in image processing, optical trapping and optical communications, etc. In this work, we theoretically and numerically investigated the propagation properties of hypergeometric Gaussian (HyGG) beams in strongly nonlocal nonlinear media. Based on the Snyder-Mitchell model, analytical expressions for propagation of the HyGG beams in strongly nonlocal nonlinear media were obtained. The influence of input power and optical parameters on the evolutions of the beam width and radius of curvature is illustrated, respectively. The results show that the beam width and radius of curvature of the HyGG beams remain invariant, like a soliton when the input power is equal to the critical power. Otherwise, it varies periodically like a breather, which is the result of competition between the beam diffraction and nonlinearity of the medium.
Disaggregated seismic hazard and the elastic input energy spectrum: An approach to design earthquake selection

NASA Astrophysics Data System (ADS)

Chapman, Martin Colby

1998-12-01

The design earthquake selection problem is fundamentally probabilistic. Disaggregation of a probabilistic model of the seismic hazard offers a rational and objective approach that can identify the most likely earthquake scenario(s) contributing to hazard. An ensemble of time series can be selected on the basis of the modal earthquakes derived from the disaggregation. This gives a useful time-domain realization of the seismic hazard, to the extent that a single motion parameter captures the important time-domain characteristics. A possible limitation to this approach arises because most currently available motion prediction models for peak ground motion or oscillator response are essentially independent of duration, and modal events derived using the peak motions for the analysis may not represent the optimal characterization of the hazard. The elastic input energy spectrum is an alternative to the elastic response spectrum for these types of analyses. The input energy combines the elements of amplitude and duration into a single parameter description of the ground motion that can be readily incorporated into standard probabilistic seismic hazard analysis methodology. This use of the elastic input energy spectrum is examined. Regression analysis is performed using strong motion data from Western North America and consistent data processing procedures for both the absolute input energy equivalent velocity, (Vsbea), and the elastic pseudo-relative velocity response (PSV) in the frequency range 0.5 to 10 Hz. The results show that the two parameters can be successfully fit with identical functional forms. The dependence of Vsbea and PSV upon (NEHRP) site classification is virtually identical. The variance of Vsbea is uniformly less than that of PSV, indicating that Vsbea can be predicted with slightly less uncertainty as a function of magnitude, distance and site classification. The effects of site class are important at frequencies less than a few Hertz. The regression modeling does not resolve significant effects due to site class at frequencies greater than approximately 5 Hz. Disaggregation of general seismic hazard models using Vsbea indicates that the modal magnitudes for the higher frequency oscillators tend to be larger, and vary less with oscillator frequency, than those derived using PSV. Insofar as the elastic input energy may be a better parameter for quantifying the damage potential of ground motion, its use in probabilistic seismic hazard analysis could provide an improved means for selecting earthquake scenarios and establishing design earthquakes for many types of engineering analyses.
Characterization of a Common-Gate Amplifier Using Ferroelectric Transistors

NASA Technical Reports Server (NTRS)

Hunt, Mitchell; Sayyah, Rana; MacLeod, Todd C.; Ho, Fat D.

2011-01-01

In this paper, the empirical data collected through experiments performed using a FeFET in the common-gate amplifier circuit is presented. The FeFET common-gate amplifier was characterized by varying all parameters in the circuit, such as load resistance, biasing of the transistor, and input voltages. Due to the polarization of the ferroelectric layer, the particular behavior of the FeFET common-gate amplifier presents interesting results. Furthermore, the differences between a FeFET common-gate amplifier and a MOSFET common-gate amplifier are examined.
Static Characteristics of the Ferroelectric Transistor Inverter

NASA Technical Reports Server (NTRS)

Mitchell, Cody; Laws, crystal; MacLeond, Todd C.; Ho, Fat D.

2010-01-01

The inverter is one of the most fundamental building blocks of digital logic, and it can be used as the foundation for understanding more complex logic gates and circuits. This paper presents the characteristics of an inverter circuit using a ferroelectric field-effect transistor. The voltage transfer characteristics are analyzed with respect to varying parameters such as supply voltage, input voltage, and load resistance. The effects of the ferroelectric layer between the gate and semiconductor are examined, and comparisons are made between the inverters using ferroelectric transistors and those using traditional MOSFETs.

Entry dynamics of space shuttle orbiter with longitudinal stability and control uncertainties at supersonic and hypersonic speeds

NASA Technical Reports Server (NTRS)

Stone, H. W.; Powell, R. W.

1977-01-01

A six-degree-of-freedom simulation analysis was conducted to examine the effects of longitudinal static aerodynamic stability and control uncertainties on the performance of the space shuttle orbiter automatic (no manual inputs) entry guidance and control systems. To establish the acceptable boundaries, the static aerodynamic characteristics were varied either by applying a multiplier to the aerodynamic parameter or by adding an increment. With either of two previously identified control system modifications included, the acceptable longitudinal aerodynamic boundaries were determined.
Enhancing PTFs with remotely sensed data for multi-scale soil water retention estimation

NASA Astrophysics Data System (ADS)

Jana, Raghavendra B.; Mohanty, Binayak P.

2011-03-01

SummaryUse of remotely sensed data products in the earth science and water resources fields is growing due to increasingly easy availability of the data. Traditionally, pedotransfer functions (PTFs) employed for soil hydraulic parameter estimation from other easily available data have used basic soil texture and structure information as inputs. Inclusion of surrogate/supplementary data such as topography and vegetation information has shown some improvement in the PTF's ability to estimate more accurate soil hydraulic parameters. Artificial neural networks (ANNs) are a popular tool for PTF development, and are usually applied across matching spatial scales of inputs and outputs. However, different hydrologic, hydro-climatic, and contaminant transport models require input data at different scales, all of which may not be easily available from existing databases. In such a scenario, it becomes necessary to scale the soil hydraulic parameter values estimated by PTFs to suit the model requirements. Also, uncertainties in the predictions need to be quantified to enable users to gauge the suitability of a particular dataset in their applications. Bayesian Neural Networks (BNNs) inherently provide uncertainty estimates for their outputs due to their utilization of Markov Chain Monte Carlo (MCMC) techniques. In this paper, we present a PTF methodology to estimate soil water retention characteristics built on a Bayesian framework for training of neural networks and utilizing several in situ and remotely sensed datasets jointly. The BNN is also applied across spatial scales to provide fine scale outputs when trained with coarse scale data. Our training data inputs include ground/remotely sensed soil texture, bulk density, elevation, and Leaf Area Index (LAI) at 1 km resolutions, while similar properties measured at a point scale are used as fine scale inputs. The methodology was tested at two different hydro-climatic regions. We also tested the effect of varying the support scale of the training data for the BNNs by sequentially aggregating finer resolution training data to coarser resolutions, and the applicability of the technique to upscaling problems. The BNN outputs are corrected for bias using a non-linear CDF-matching technique. Final results show good promise of the suitability of this Bayesian Neural Network approach for soil hydraulic parameter estimation across spatial scales using ground-, air-, or space-based remotely sensed geophysical parameters. Inclusion of remotely sensed data such as elevation and LAI in addition to in situ soil physical properties improved the estimation capabilities of the BNN-based PTF in certain conditions.
Effects of Uncertainties in Hydrological Modelling. A Case Study of a Mountainous Catchment in Southern Norway

NASA Astrophysics Data System (ADS)

Engeland, Kolbjorn; Steinsland, Ingelin

2016-04-01

The aim of this study is to investigate how the inclusion of uncertainties in inputs and observed streamflow influence the parameter estimation, streamflow predictions and model evaluation. In particular we wanted to answer the following research questions: • What is the effect of including a random error in the precipitation and temperature inputs? • What is the effect of decreased information about precipitation by excluding the nearest precipitation station? • What is the effect of the uncertainty in streamflow observations? • What is the effect of reduced information about the true streamflow by using a rating curve where the measurement of the highest and lowest streamflow is excluded when estimating the rating curve? To answer these questions, we designed a set of calibration experiments and evaluation strategies. We used the elevation distributed HBV model operating on daily time steps combined with a Bayesian formulation and the MCMC routine Dream for parameter inference. The uncertainties in inputs was represented by creating ensembles of precipitation and temperature. The precipitation ensemble were created using a meta-gaussian random field approach. The temperature ensembles were created using a 3D Bayesian kriging with random sampling of the temperature laps rate. The streamflow ensembles were generated by a Bayesian multi-segment rating curve model. Precipitation and temperatures were randomly sampled for every day, whereas the streamflow ensembles were generated from rating curve ensembles, and the same rating curve was always used for the whole time series in a calibration or evaluation run. We chose a catchment with a meteorological station measuring precipitation and temperature, and a rating curve of relatively high quality. This allowed us to investigate and further test the effect of having less information on precipitation and streamflow during model calibration, predictions and evaluation. The results showed that including uncertainty in the precipitation and temperature input has a negligible effect on the posterior distribution of parameters and for the Nash-Sutcliffe (NS) efficiency for the predicted flows, while the reliability and the continuous rank probability score (CRPS) improves. Reduced information in precipitation input resulted in a and a shift in the water balance parameter Pcorr, a model producing smoother streamflow predictions giving poorer NS and CRPS, but higher reliability. The effect of calibrating the hydrological model using wrong rating curves is mainly seen as variability in the water balance parameter Pcorr. When evaluating predictions obtained using a wrong rating curve, the evaluation scores varies depending on the true rating curve. Generally, the best evaluation scores were not achieved for the rating curve used for calibration, but for a rating curves giving low variance in streamflow observations. Reduced information in streamflow influenced the water balance parameter Pcorr, and increased the spread in evaluation scores giving both better and worse scores. This case study shows that estimating the water balance is challenging since both precipitation inputs and streamflow observations have pronounced systematic component in their uncertainties.
Option B+ for the prevention of mother-to-child transmission of HIV infection in developing countries: a review of published cost-effectiveness analyses.

PubMed

Karnon, Jonathan; Orji, Nneka

2016-10-01

To review the published literature on the cost effectiveness of Option B+ (lifelong antiretroviral therapy) for preventing mother-to-child transmission (PMTCT) of HIV during pregnancy and breastfeeding to inform decision making in low- and middle-income countries. PubMed, Scopus, Google scholar and Medline were searched to identify studies of the cost effectiveness of the World Health Organization (WHO) treatment guidelines for PMTCT. Study quality was appraised using the consolidated health economic evaluation reporting standards checklist. Eligible studies were reviewed in detail to assess the relevance and impact of alternative evaluation frameworks, assumptions and input parameter values. Five published cost effectiveness analyses of Option B+ for the PMTCT of HIV were identified. The reported cost-effectiveness of Option B+ varies substantially, with the results of different studies implying that Option B+ is dominant (lower costs, greater benefits), cost-effective (additional benefits at acceptable additional costs) or not cost-effective (additional benefits at unacceptable additional costs). This variation is due to significant differences in model structures and input parameter values. Structural differences were observed around the estimation of programme effects on infants, HIV-infected mothers and their HIV negative partners, over multiple pregnancies, as well assumptions regarding routine access to antiretroviral therapies. Significant differences in key input parameters were observed in transmission rates, intervention costs and effects and downstream cost savings. Across five model-based cost-effectiveness analyses of strategies for the PMTCT of HIV, the most comprehensive analysis reported that option B+ is highly likely to be cost-effective. This evaluation may have been overly favourable towards option B+ with respect to some input parameter values, but potentially important additional benefits were omitted. Decision makers might be best advised to review this analysis, with a view to requesting additional analyses of the model to inform local funding decisions around alternative strategies for the PMTCT of HIV. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Feedback topology and XOR-dynamics in Boolean networks with varying input structure

NASA Astrophysics Data System (ADS)

Ciandrini, L.; Maffi, C.; Motta, A.; Bassetti, B.; Cosentino Lagomarsino, M.

2009-08-01

We analyze a model of fixed in-degree random Boolean networks in which the fraction of input-receiving nodes is controlled by the parameter γ . We investigate analytically and numerically the dynamics of graphs under a parallel XOR updating scheme. This scheme is interesting because it is accessible analytically and its phenomenology is at the same time under control and as rich as the one of general Boolean networks. We give analytical formulas for the dynamics on general graphs, showing that with a XOR-type evolution rule, dynamic features are direct consequences of the topological feedback structure, in analogy with the role of relevant components in Kauffman networks. Considering graphs with fixed in-degree, we characterize analytically and numerically the feedback regions using graph decimation algorithms (Leaf Removal). With varying γ , this graph ensemble shows a phase transition that separates a treelike graph region from one in which feedback components emerge. Networks near the transition point have feedback components made of disjoint loops, in which each node has exactly one incoming and one outgoing link. Using this fact, we provide analytical estimates of the maximum period starting from topological considerations.
Feedback topology and XOR-dynamics in Boolean networks with varying input structure.

PubMed

Ciandrini, L; Maffi, C; Motta, A; Bassetti, B; Cosentino Lagomarsino, M

2009-08-01

We analyze a model of fixed in-degree random Boolean networks in which the fraction of input-receiving nodes is controlled by the parameter gamma. We investigate analytically and numerically the dynamics of graphs under a parallel XOR updating scheme. This scheme is interesting because it is accessible analytically and its phenomenology is at the same time under control and as rich as the one of general Boolean networks. We give analytical formulas for the dynamics on general graphs, showing that with a XOR-type evolution rule, dynamic features are direct consequences of the topological feedback structure, in analogy with the role of relevant components in Kauffman networks. Considering graphs with fixed in-degree, we characterize analytically and numerically the feedback regions using graph decimation algorithms (Leaf Removal). With varying gamma , this graph ensemble shows a phase transition that separates a treelike graph region from one in which feedback components emerge. Networks near the transition point have feedback components made of disjoint loops, in which each node has exactly one incoming and one outgoing link. Using this fact, we provide analytical estimates of the maximum period starting from topological considerations.
Development of a Phasor Diagram Creator to Visualize the Piston and Displacer Forces in an Advanced Stirling Convertor

NASA Technical Reports Server (NTRS)

Saha, Dipanjan; Lewandowski, Edward J.

2013-01-01

The steady state, nearly sinusoidal behavior of the components in a Free Piston Stirling Engine allows for visualization of the forces in the system using phasor diagrams. Based on Newton's second law, F=ma, any phasor diagrams modeling a given component in a system should close if all of the acting forces have been considered. Since the Advanced Stirling Radioisotope Generator (ASRG), currently being developed for future NASA deep space missions, is made up of such nearly sinusoidally oscillating components, its phasor diagrams would also be expected to close. A graphical user interface (GUI) has been written in MATLAB by taking user input data, passing it to Sage, a 1-D thermodynamic modeling program used to model the Stirling convertor, running Sage and then automatically plotting the phasor diagrams. Using this software tool, the effect of varying different Sage inputs on the phasor diagrams was determined. The parameters varied were piston amplitude, hot end temperature, cold end temperature, operating frequency, and displacer spring constant. By using these phasor diagrams, better insight can be gained as to why the convertor operates the way that it does.
Sensor fault diagnosis of singular delayed LPV systems with inexact parameters: an uncertain system approach

NASA Astrophysics Data System (ADS)

Hassanabadi, Amir Hossein; Shafiee, Masoud; Puig, Vicenc

2018-01-01

In this paper, sensor fault diagnosis of a singular delayed linear parameter varying (LPV) system is considered. In the considered system, the model matrices are dependent on some parameters which are real-time measurable. The case of inexact parameter measurements is considered which is close to real situations. Fault diagnosis in this system is achieved via fault estimation. For this purpose, an augmented system is created by including sensor faults as additional system states. Then, an unknown input observer (UIO) is designed which estimates both the system states and the faults in the presence of measurement noise, disturbances and uncertainty induced by inexact measured parameters. Error dynamics and the original system constitute an uncertain system due to inconsistencies between real and measured values of the parameters. Then, the robust estimation of the system states and the faults are achieved with H∞ performance and formulated with a set of linear matrix inequalities (LMIs). The designed UIO is also applicable for fault diagnosis of singular delayed LPV systems with unmeasurable scheduling variables. The efficiency of the proposed approach is illustrated with an example.
Dual ant colony operational modal analysis parameter estimation method

NASA Astrophysics Data System (ADS)

Sitarz, Piotr; Powałka, Bartosz

2018-01-01

Operational Modal Analysis (OMA) is a common technique used to examine the dynamic properties of a system. Contrary to experimental modal analysis, the input signal is generated in object ambient environment. Operational modal analysis mainly aims at determining the number of pole pairs and at estimating modal parameters. Many methods are used for parameter identification. Some methods operate in time while others in frequency domain. The former use correlation functions, the latter - spectral density functions. However, while some methods require the user to select poles from a stabilisation diagram, others try to automate the selection process. Dual ant colony operational modal analysis parameter estimation method (DAC-OMA) presents a new approach to the problem, avoiding issues involved in the stabilisation diagram. The presented algorithm is fully automated. It uses deterministic methods to define the interval of estimated parameters, thus reducing the problem to optimisation task which is conducted with dedicated software based on ant colony optimisation algorithm. The combination of deterministic methods restricting parameter intervals and artificial intelligence yields very good results, also for closely spaced modes and significantly varied mode shapes within one measurement point.
Photovoltaic array: Power conditioner interface characteristics

NASA Technical Reports Server (NTRS)

Gonzalez, C. C.; Hill, G. M.; Ross, R. G., Jr.

1982-01-01

The electrical output (power, current, and voltage) of flat plate solar arrays changes constantly, due primarily to changes in cell temperature and irradiance level. As a result, array loads such as dc-to-ac power conditioners must be capable of accommodating widely varying input levels while maintaining operation at or near the maximum power point of the array. The array operating characteristics and extreme output limits necessary for the systematic design of array load interfaces under a wide variety of climatic conditions are studied. A number of interface parameters are examined, including optimum operating voltage, voltage energy, maximum power and current limits, and maximum open circuit voltage. The effect of array degradation and I-V curve fill factor or the array power conditioner interface is also discussed. Results are presented as normalized ratios of power conditioner parameters to array parameters, making the results universally applicable to a wide variety of system sizes, sites, and operating modes.
Efficient Screening of Climate Model Sensitivity to a Large Number of Perturbed Input Parameters [plus supporting information

DOE PAGES

Covey, Curt; Lucas, Donald D.; Tannahill, John; ...

2013-07-01

Modern climate models contain numerous input parameters, each with a range of possible values. Since the volume of parameter space increases exponentially with the number of parameters N, it is generally impossible to directly evaluate a model throughout this space even if just 2-3 values are chosen for each parameter. Sensitivity screening algorithms, however, can identify input parameters having relatively little effect on a variety of output fields, either individually or in nonlinear combination.This can aid both model development and the uncertainty quantification (UQ) process. Here we report results from a parameter sensitivity screening algorithm hitherto untested in climate modeling,more » the Morris one-at-a-time (MOAT) method. This algorithm drastically reduces the computational cost of estimating sensitivities in a high dimensional parameter space because the sample size grows linearly rather than exponentially with N. It nevertheless samples over much of the N-dimensional volume and allows assessment of parameter interactions, unlike traditional elementary one-at-a-time (EOAT) parameter variation. We applied both EOAT and MOAT to the Community Atmosphere Model (CAM), assessing CAM’s behavior as a function of 27 uncertain input parameters related to the boundary layer, clouds, and other subgrid scale processes. For radiation balance at the top of the atmosphere, EOAT and MOAT rank most input parameters similarly, but MOAT identifies a sensitivity that EOAT underplays for two convection parameters that operate nonlinearly in the model. MOAT’s ranking of input parameters is robust to modest algorithmic variations, and it is qualitatively consistent with model development experience. Supporting information is also provided at the end of the full text of the article.« less
Reconstruction of neuronal input through modeling single-neuron dynamics and computations

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

Qin, Qing; Wang, Jiang; Yu, Haitao

Mathematical models provide a mathematical description of neuron activity, which can better understand and quantify neural computations and corresponding biophysical mechanisms evoked by stimulus. In this paper, based on the output spike train evoked by the acupuncture mechanical stimulus, we present two different levels of models to describe the input-output system to achieve the reconstruction of neuronal input. The reconstruction process is divided into two steps: First, considering the neuronal spiking event as a Gamma stochastic process. The scale parameter and the shape parameter of Gamma process are, respectively, defined as two spiking characteristics, which are estimated by a state-spacemore » method. Then, leaky integrate-and-fire (LIF) model is used to mimic the response system and the estimated spiking characteristics are transformed into two temporal input parameters of LIF model, through two conversion formulas. We test this reconstruction method by three different groups of simulation data. All three groups of estimates reconstruct input parameters with fairly high accuracy. We then use this reconstruction method to estimate the non-measurable acupuncture input parameters. Results show that under three different frequencies of acupuncture stimulus conditions, estimated input parameters have an obvious difference. The higher the frequency of the acupuncture stimulus is, the higher the accuracy of reconstruction is.« less
Reconstruction of neuronal input through modeling single-neuron dynamics and computations

NASA Astrophysics Data System (ADS)

Qin, Qing; Wang, Jiang; Yu, Haitao; Deng, Bin; Chan, Wai-lok

2016-06-01

Mathematical models provide a mathematical description of neuron activity, which can better understand and quantify neural computations and corresponding biophysical mechanisms evoked by stimulus. In this paper, based on the output spike train evoked by the acupuncture mechanical stimulus, we present two different levels of models to describe the input-output system to achieve the reconstruction of neuronal input. The reconstruction process is divided into two steps: First, considering the neuronal spiking event as a Gamma stochastic process. The scale parameter and the shape parameter of Gamma process are, respectively, defined as two spiking characteristics, which are estimated by a state-space method. Then, leaky integrate-and-fire (LIF) model is used to mimic the response system and the estimated spiking characteristics are transformed into two temporal input parameters of LIF model, through two conversion formulas. We test this reconstruction method by three different groups of simulation data. All three groups of estimates reconstruct input parameters with fairly high accuracy. We then use this reconstruction method to estimate the non-measurable acupuncture input parameters. Results show that under three different frequencies of acupuncture stimulus conditions, estimated input parameters have an obvious difference. The higher the frequency of the acupuncture stimulus is, the higher the accuracy of reconstruction is.
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.
Integrated controls design optimization

DOEpatents

Lou, Xinsheng; Neuschaefer, Carl H.

2015-09-01

A control system (207) for optimizing a chemical looping process of a power plant includes an optimizer (420), an income algorithm (230) and a cost algorithm (225) and a chemical looping process models. The process models are used to predict the process outputs from process input variables. Some of the process in puts and output variables are related to the income of the plant; and some others are related to the cost of the plant operations. The income algorithm (230) provides an income input to the optimizer (420) based on a plurality of input parameters (215) of the power plant. The cost algorithm (225) provides a cost input to the optimizer (420) based on a plurality of output parameters (220) of the power plant. The optimizer (420) determines an optimized operating parameter solution based on at least one of the income input and the cost input, and supplies the optimized operating parameter solution to the power plant.
Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis

PubMed Central

Casadebaig, Pierre; Zheng, Bangyou; Chapman, Scott; Huth, Neil; Faivre, Robert; Chenu, Karine

2016-01-01

A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites × 125 years), management practices (3 sowing dates × 3 nitrogen fertilization levels) and CO2 (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait × environment × management landscape (∼ 82 million individual simulations in total). The patterns of parameter × environment × management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference cultivar. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identification of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement. PMID:26799483
Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis.

PubMed

Casadebaig, Pierre; Zheng, Bangyou; Chapman, Scott; Huth, Neil; Faivre, Robert; Chenu, Karine

2016-01-01

A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites × 125 years), management practices (3 sowing dates × 3 nitrogen fertilization levels) and CO2 (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait × environment × management landscape (∼ 82 million individual simulations in total). The patterns of parameter × environment × management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference cultivar. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identification of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement.
Prediction and Computation of Corrosion Rates of A36 Mild Steel in Oilfield Seawater

NASA Astrophysics Data System (ADS)

Paul, Subir; Mondal, Rajdeep

2018-04-01

The parameters which primarily control the corrosion rate and life of steel structures are several and they vary across the different ocean and seawater as well as along the depth. While the effect of single parameter on corrosion behavior is known, the conjoint effects of multiple parameters and the interrelationship among the variables are complex. Millions sets of experiments are required to understand the mechanism of corrosion failure. Statistical modeling such as ANN is one solution that can reduce the number of experimentation. ANN model was developed using 170 sets of experimental data of A35 mild steel in simulated seawater, varying the corrosion influencing parameters SO4 2-, Cl-, HCO3 -,CO3 2-, CO2, O2, pH and temperature as input and the corrosion current as output. About 60% of experimental data were used to train the model, 20% for testing and 20% for validation. The model was developed by programming in Matlab. 80% of the validated data could predict the corrosion rate correctly. Corrosion rates predicted by the ANN model are displayed in 3D graphics which show many interesting phenomenon of the conjoint effects of multiple variables that might throw new ideas of mitigation of corrosion by simply modifying the chemistry of the constituents. The model could predict the corrosion rates of some real systems.
Positional glow curve simulation for thermoluminescent detector (TLD) system design

NASA Astrophysics Data System (ADS)

Branch, C. J.; Kearfott, K. J.

1999-02-01

Multi- and thin element dosimeters, variable heating rate schemes, and glow-curve analysis have been employed to improve environmental and personnel dosimetry using thermoluminescent detectors (TLDs). Detailed analysis of the effects of errors and optimization of techniques would be highly desirable. However, an understanding of the relationship between TL light production, light attenuation, and precise heating schemes is made difficult because of experimental challenges involved in measuring positional TL light production and temperature variations as a function of time. This work reports the development of a general-purpose computer code, thermoluminescent detector simulator, TLD-SIM, to simulate the heating of any TLD type using a variety of conventional and experimental heating methods including pulsed focused or unfocused lasers with Gaussian or uniform cross sections, planchet, hot gas, hot finger, optical, infrared, or electrical heating. TLD-SIM has been used to study the impact on the TL light production of varying the input parameters which include: detector composition, heat capacity, heat conductivity, physical size, and density; trapped electron density, the frequency factor of oscillation of electrons in the traps, and trap-conduction band potential energy difference; heating scheme source terms and heat transfer boundary conditions; and TL light scatter and attenuation coefficients. Temperature profiles and glow curves as a function of position time, as well as the corresponding temporally and/or spatially integrated glow values, may be plotted while varying any of the input parameters. Examples illustrating TLD system functions, including glow curve variability, will be presented. The flexible capabilities of TLD-SIM promises to enable improved TLD system design.
Robust LS-SVM-based adaptive constrained control for a class of uncertain nonlinear systems with time-varying predefined performance

NASA Astrophysics Data System (ADS)

Luo, Jianjun; Wei, Caisheng; Dai, Honghua; Yuan, Jianping

2018-03-01

This paper focuses on robust adaptive control for a class of uncertain nonlinear systems subject to input saturation and external disturbance with guaranteed predefined tracking performance. To reduce the limitations of classical predefined performance control method in the presence of unknown initial tracking errors, a novel predefined performance function with time-varying design parameters is first proposed. Then, aiming at reducing the complexity of nonlinear approximations, only two least-square-support-vector-machine-based (LS-SVM-based) approximators with two design parameters are required through norm form transformation of the original system. Further, a novel LS-SVM-based adaptive constrained control scheme is developed under the time-vary predefined performance using backstepping technique. Wherein, to avoid the tedious analysis and repeated differentiations of virtual control laws in the backstepping technique, a simple and robust finite-time-convergent differentiator is devised to only extract its first-order derivative at each step in the presence of external disturbance. In this sense, the inherent demerit of backstepping technique-;explosion of terms; brought by the recursive virtual controller design is conquered. Moreover, an auxiliary system is designed to compensate the control saturation. Finally, three groups of numerical simulations are employed to validate the effectiveness of the newly developed differentiator and the proposed adaptive constrained control scheme.

Human health risk assessment of lead from mining activities at semi-arid locations in the context of total lead exposure.

PubMed

Zheng, Jiajia; Huynh, Trang; Gasparon, Massimo; Ng, Jack; Noller, Barry

2013-12-01

Lead from historical mining and mineral processing activities may pose potential human health risks if materials with high concentrations of bioavailable lead minerals are released to the environment. Since the Joint Expert Committee on Food Additives of Food and Agriculture Organization/World Health Organization withdrew the Provisional Tolerable Weekly Intake of lead in 2011, an alternative method was required for lead exposure assessment. This study evaluated the potential lead hazard to young children (0-7 years) from a historical mining location at a semi-arid area using the U.S. EPA Integrated Exposure Uptake Biokinetic (IEUBK) Model, with selected site-specific input data. This study assessed lead exposure via the inhalation pathway for children living in a location affected by lead mining activities and with specific reference to semi-arid conditions and made comparison with the ingestion pathway by using the physiologically based extraction test for gastro-intestinal simulation. Sensitivity analysis for major IEUBK input parameters was conducted. Three groups of input parameters were classified according to the results of predicted blood concentrations. The modelled lead absorption attributed to the inhalation route was lower than 2 % (mean ± SE, 0.9 % ± 0.1 %) of all lead intake routes and was demonstrated as a less significant exposure pathway to children's blood, compared with ingestion. Whilst dermal exposure was negligible, diet and ingestion of soil and dust were the dominant parameters in terms of children's blood lead prediction. The exposure assessment identified the changing role of dietary intake when house lead loadings varied. Recommendations were also made to conduct comprehensive site-specific human health risk assessment in future studies of lead exposure under a semi-arid climate.
Next-Generation Lightweight Mirror Modeling Software

NASA Technical Reports Server (NTRS)

Arnold, William R., Sr.; Fitzgerald, Mathew; Rosa, Rubin Jaca; Stahl, Phil

2013-01-01

The advances in manufacturing techniques for lightweight mirrors, such as EXELSIS deep core low temperature fusion, Corning's continued improvements in the Frit bonding process and the ability to cast large complex designs, combined with water-jet and conventional diamond machining of glasses and ceramics has created the need for more efficient means of generating finite element models of these structures. Traditional methods of assembling 400,000 + element models can take weeks of effort, severely limiting the range of possible optimization variables. This paper will introduce model generation software developed under NASA sponsorship for the design of both terrestrial and space based mirrors. The software deals with any current mirror manufacturing technique, single substrates, multiple arrays of substrates, as well as the ability to merge submodels into a single large model. The modeler generates both mirror and suspension system elements, suspensions can be created either for each individual petal or the whole mirror. A typical model generation of 250,000 nodes and 450,000 elements only takes 5-10 minutes, much of that time being variable input time. The program can create input decks for ANSYS, ABAQUS and NASTRAN. An archive/retrieval system permits creation of complete trade studies, varying cell size, depth, and petal size, suspension geometry with the ability to recall a particular set of parameters and make small or large changes with ease. The input decks created by the modeler are text files which can be modified by any editor, all the key shell thickness parameters are accessible and comments in deck identify which groups of elements are associated with these parameters. This again makes optimization easier. With ANSYS decks, the nodes representing support attachments are grouped into components; in ABAQUS these are SETS and in NASTRAN as GRIDPOINT SETS, this make integration of these models into large telescope or satellite models possible
Next Generation Lightweight Mirror Modeling Software

NASA Technical Reports Server (NTRS)

Arnold, William; Fitzgerald, Matthew; Stahl, Philip

2013-01-01

The advances in manufacturing techniques for lightweight mirrors, such as EXELSIS deep core low temperature fusion, Corning's continued improvements in the Frit bonding process and the ability to cast large complex designs, combined with water-jet and conventional diamond machining of glasses and ceramics has created the need for more efficient means of generating finite element models of these structures. Traditional methods of assembling 400,000 + element models can take weeks of effort, severely limiting the range of possible optimization variables. This paper will introduce model generation software developed under NASA sponsorship for the design of both terrestrial and space based mirrors. The software deals with any current mirror manufacturing technique, single substrates, multiple arrays of substrates, as well as the ability to merge submodels into a single large model. The modeler generates both mirror and suspension system elements, suspensions can be created either for each individual petal or the whole mirror. A typical model generation of 250,000 nodes and 450,000 elements only takes 5-10 minutes, much of that time being variable input time. The program can create input decks for ANSYS, ABAQUS and NASTRAN. An archive/retrieval system permits creation of complete trade studies, varying cell size, depth, and petal size, suspension geometry with the ability to recall a particular set of parameters and make small or large changes with ease. The input decks created by the modeler are text files which can be modified by any editor, all the key shell thickness parameters are accessible and comments in deck identify which groups of elements are associated with these parameters. This again makes optimization easier. With ANSYS decks, the nodes representing support attachments are grouped into components; in ABAQUS these are SETS and in NASTRAN as GRIDPOINT SETS, this make integration of these models into large telescope or satellite models possible.
Next Generation Lightweight Mirror Modeling Software

NASA Technical Reports Server (NTRS)

Arnold, William R., Sr.; Fitzgerald, Mathew; Rosa, Rubin Jaca; Stahl, H. Philip

2013-01-01

The advances in manufacturing techniques for lightweight mirrors, such as EXELSIS deep core low temperature fusion, Corning's continued improvements in the Frit bonding process and the ability to cast large complex designs, combined with water-jet and conventional diamond machining of glasses and ceramics has created the need for more efficient means of generating finite element models of these structures. Traditional methods of assembling 400,000 + element models can take weeks of effort, severely limiting the range of possible optimization variables. This paper will introduce model generation software developed under NASA sponsorship for the design of both terrestrial and space based mirrors. The software deals with any current mirror manufacturing technique, single substrates, multiple arrays of substrates, as well as the ability to merge submodels into a single large model. The modeler generates both mirror and suspension system elements, suspensions can be created either for each individual petal or the whole mirror. A typical model generation of 250,000 nodes and 450,000 elements only takes 5-10 minutes, much of that time being variable input time. The program can create input decks for ANSYS, ABAQUS and NASTRAN. An archive/retrieval system permits creation of complete trade studies, varying cell size, depth, and petal size, suspension geometry with the ability to recall a particular set of parameters and make small or large changes with ease. The input decks created by the modeler are text files which can be modified by any editor, all the key shell thickness parameters are accessible and comments in deck identify which groups of elements are associated with these parameters. This again makes optimization easier. With ANSYS decks, the nodes representing support attachments are grouped into components; in ABAQUS these are SETS and in NASTRAN as GRIDPOINT SETS, this make integration of these models into large telescope or satellite models easier.
Model parameter uncertainty analysis for an annual field-scale P loss model

NASA Astrophysics Data System (ADS)

Bolster, Carl H.; Vadas, Peter A.; Boykin, Debbie

2016-08-01

Phosphorous (P) fate and transport models are important tools for developing and evaluating conservation practices aimed at reducing P losses from agricultural fields. Because all models are simplifications of complex systems, there will exist an inherent amount of uncertainty associated with their predictions. It is therefore important that efforts be directed at identifying, quantifying, and communicating the different sources of model uncertainties. In this study, we conducted an uncertainty analysis with the Annual P Loss Estimator (APLE) model. Our analysis included calculating parameter uncertainties and confidence and prediction intervals for five internal regression equations in APLE. We also estimated uncertainties of the model input variables based on values reported in the literature. We then predicted P loss for a suite of fields under different management and climatic conditions while accounting for uncertainties in the model parameters and inputs and compared the relative contributions of these two sources of uncertainty to the overall uncertainty associated with predictions of P loss. Both the overall magnitude of the prediction uncertainties and the relative contributions of the two sources of uncertainty varied depending on management practices and field characteristics. This was due to differences in the number of model input variables and the uncertainties in the regression equations associated with each P loss pathway. Inspection of the uncertainties in the five regression equations brought attention to a previously unrecognized limitation with the equation used to partition surface-applied fertilizer P between leaching and runoff losses. As a result, an alternate equation was identified that provided similar predictions with much less uncertainty. Our results demonstrate how a thorough uncertainty and model residual analysis can be used to identify limitations with a model. Such insight can then be used to guide future data collection and model development and evaluation efforts.
Programmable electronic synthesized capacitance

NASA Technical Reports Server (NTRS)

Kleinberg, Leonard L. (Inventor)

1987-01-01

A predetermined and variable synthesized capacitance which may be incorporated into the resonant portion of an electronic oscillator for the purpose of tuning the oscillator comprises a programmable operational amplifier circuit. The operational amplifier circuit has its output connected to its inverting input, in a follower configuration, by a network which is low impedance at the operational frequency of the circuit. The output of the operational amplifier is also connected to the noninverting input by a capacitor. The noninverting input appears as a synthesized capacitance which may be varied with a variation in gain-bandwidth product of the operational amplifier circuit. The gain-bandwidth product may, in turn, be varied with a variation in input set current with a digital to analog converter whose output is varied with a command word. The output impedance of the circuit may also be varied by the output set current. This circuit may provide very small ranges in oscillator frequency with relatively large control voltages unaffected by noise.
Fuzzy logic controller optimization

DOEpatents

Sepe, Jr., Raymond B; Miller, John Michael

2004-03-23

A method is provided for optimizing a rotating induction machine system fuzzy logic controller. The fuzzy logic controller has at least one input and at least one output. Each input accepts a machine system operating parameter. Each output produces at least one machine system control parameter. The fuzzy logic controller generates each output based on at least one input and on fuzzy logic decision parameters. Optimization begins by obtaining a set of data relating each control parameter to at least one operating parameter for each machine operating region. A model is constructed for each machine operating region based on the machine operating region data obtained. The fuzzy logic controller is simulated with at least one created model in a feedback loop from a fuzzy logic output to a fuzzy logic input. Fuzzy logic decision parameters are optimized based on the simulation.
Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT.

PubMed

Bizios, Dimitrios; Heijl, Anders; Hougaard, Jesper Leth; Bengtsson, Boel

2010-02-01

To compare the performance of two machine learning classifiers (MLCs), artificial neural networks (ANNs) and support vector machines (SVMs), with input based on retinal nerve fibre layer thickness (RNFLT) measurements by optical coherence tomography (OCT), on the diagnosis of glaucoma, and to assess the effects of different input parameters. We analysed Stratus OCT data from 90 healthy persons and 62 glaucoma patients. Performance of MLCs was compared using conventional OCT RNFLT parameters plus novel parameters such as minimum RNFLT values, 10th and 90th percentiles of measured RNFLT, and transformations of A-scan measurements. For each input parameter and MLC, the area under the receiver operating characteristic curve (AROC) was calculated. There were no statistically significant differences between ANNs and SVMs. The best AROCs for both ANN (0.982, 95%CI: 0.966-0.999) and SVM (0.989, 95% CI: 0.979-1.0) were based on input of transformed A-scan measurements. Our SVM trained on this input performed better than ANNs or SVMs trained on any of the single RNFLT parameters (p < or = 0.038). The performance of ANNs and SVMs trained on minimum thickness values and the 10th and 90th percentiles were at least as good as ANNs and SVMs with input based on the conventional RNFLT parameters. No differences between ANN and SVM were observed in this study. Both MLCs performed very well, with similar diagnostic performance. Input parameters have a larger impact on diagnostic performance than the type of machine classifier. Our results suggest that parameters based on transformed A-scan thickness measurements of the RNFL processed by machine classifiers can improve OCT-based glaucoma diagnosis.
Systematic comparison of the response properties of protein and RNA mediated gene regulatory motifs.

PubMed

Iyengar, Bharat Ravi; Pillai, Beena; Venkatesh, K V; Gadgil, Chetan J

2017-05-30

We present a framework enabling the dissection of the effects of motif structure (feedback or feedforward), the nature of the controller (RNA or protein), and the regulation mode (transcriptional, post-transcriptional or translational) on the response to a step change in the input. We have used a common model framework for gene expression where both motif structures have an activating input and repressing regulator, with the same set of parameters, to enable a comparison of the responses. We studied the global sensitivity of the system properties, such as steady-state gain, overshoot, peak time, and peak duration, to parameters. We find that, in all motifs, overshoot correlated negatively whereas peak duration varied concavely with peak time. Differences in the other system properties were found to be mainly dependent on the nature of the controller rather than the motif structure. Protein mediated motifs showed a higher degree of adaptation i.e. a tendency to return to baseline levels; in particular, feedforward motifs exhibited perfect adaptation. RNA mediated motifs had a mild regulatory effect; they also exhibited a lower peaking tendency and mean overshoot. Protein mediated feedforward motifs showed higher overshoot and lower peak time compared to the corresponding feedback motifs.
Heat Transfer Model for Hot Air Balloons

NASA Astrophysics Data System (ADS)

Llado-Gambin, Adriana

A heat transfer model and analysis for hot air balloons is presented in this work, backed with a flow simulation using SolidWorks. The objective is to understand the major heat losses in the balloon and to identify the parameters that affect most its flight performance. Results show that more than 70% of the heat losses are due to the emitted radiation from the balloon envelope and that convection losses represent around 20% of the total. A simulated heating source is also included in the modeling based on typical thermal input from a balloon propane burner. The burner duty cycle to keep a constant altitude can vary from 10% to 28% depending on the atmospheric conditions, and the ambient temperature is the parameter that most affects the total thermal input needed. The simulation and analysis also predict that the gas temperature inside the balloon decreases at a rate of -0.25 K/s when there is no burner activity, and it increases at a rate of +1 K/s when the balloon pilot operates the burner. The results were compared to actual flight data and they show very good agreement indicating that the major physical processes responsible for balloon performance aloft are accurately captured in the simulation.
Effect of Common Cryoprotectants on Critical Warming Rates and Ice Formation in Aqueous Solutions

PubMed Central

Hopkins, Jesse B.; Badeau, Ryan; Warkentin, Matthew; Thorne, Robert E.

2012-01-01

Ice formation on warming is of comparable or greater importance to ice formation on cooling in determining survival of cryopreserved samples. Critical warming rates required for ice-free warming of vitrified aqueous solutions of glycerol, dimethyl sulfoxide, ethylene glycol, polyethylene glycol 200 and sucrose have been measured for warming rates of order 10 to 104 K/s. Critical warming rates are typically one to three orders of magnitude larger than critical cooling rates. Warming rates vary strongly with cooling rates, perhaps due to the presence of small ice fractions in nominally vitrified samples. Critical warming and cooling rate data spanning orders of magnitude in rates provide rigorous tests of ice nucleation and growth models and their assumed input parameters. Current models with current best estimates for input parameters provide a reasonable account of critical warming rates for glycerol solutions at high concentrations/low rates, but overestimate both critical warming and cooling rates by orders of magnitude at lower concentrations and larger rates. In vitrification protocols, minimizing concentrations of potentially damaging cryoprotectants while minimizing ice formation will require ultrafast warming rates, as well as fast cooling rates to minimize the required warming rates. PMID:22728046
Microbial risk assessment in heterogeneous aquifers: 1. Pathogen transport

NASA Astrophysics Data System (ADS)

Molin, S.; Cvetkovic, V.

2010-05-01

Pathogen transport in heterogeneous aquifers is investigated for microbial risk assessment. A point source with time-dependent input of pathogens is assumed, exemplified as a simple on-site sanitation installation, intermingled with water supply wells. Any pathogen transmission pathway (realization) to the receptor from a postulated infection hazard is viewed as a random event, with the hydraulic conductivity varying spatially. For aquifers where VAR[lnK] < 1 and the integral scale is finite, we provide relatively simple semianalytical expressions for pathogen transport that incorporate the colloid filtration theory. We test a wide range of Damkohler numbers in order to assess the significance of rate limitations on the aquifer barrier function. Even slow immobile inactivation may notably affect the retention of pathogens. Analytical estimators for microbial peak discharge are evaluated and are shown to be applicable using parameters representative of rotavirus and Hepatitis A with input of 10-20 days duration.
Test and evaluation of a multifunction keyboard and a dedicated keyboard for control of a flight management computer

NASA Technical Reports Server (NTRS)

Crane, J. M.; Boucek, G. P., Jr.; Smith, W. D.

1986-01-01

A flight management computer (FMC) control display unit (CDU) test was conducted to compare two types of input devices: a fixed legend (dedicated) keyboard and a programmable legend (multifunction) keyboard. The task used for comparison was operation of the flight management computer for the Boeing 737-300. The same tasks were performed by twelve pilots on the FMC control display unit configured with a programmable legend keyboard and with the currently used B737-300 dedicated keyboard. Flight simulator work activity levels and input task complexity were varied during each pilot session. Half of the points tested were previously familiar with the B737-300 dedicated keyboard CDU and half had no prior experience with it. The data collected included simulator flight parameters, keystroke time and sequences, and pilot questionnaire responses. A timeline analysis was also used for evaluation of the two keyboard concepts.
Binational digital soils map of the Ambos Nogales watershed, southern Arizona and northern Sonora, Mexico

USGS Publications Warehouse

Norman, Laura

2004-01-01

We have prepared a digital map of soil parameters for the international Ambos Nogales watershed to use as input for selected soils-erosion models. The Ambos Nogales watershed in southern Arizona and northern Sonora, Mexico, contains the Nogales wash, a tributary of the Upper Santa Cruz River. The watershed covers an area of 235 km2, just under half of which is in Mexico. Preliminary investigations of potential erosion revealed a discrepancy in soils data and mapping across the United States-Mexican border due to issues including different mapping resolutions, incompatible formatting, and varying nomenclature and classification systems. To prepare a digital soils map appropriate for input to a soils-erosion model, the historical analog soils maps for Nogales, Ariz., were scanned and merged with the larger-scale digital soils data available for Nogales, Sonora, Mexico using a geographic information system.
Distributed Adaptive Containment Control for a Class of Nonlinear Multiagent Systems With Input Quantization.

PubMed

Wang, Chenliang; Wen, Changyun; Hu, Qinglei; Wang, Wei; Zhang, Xiuyu

2018-06-01

This paper is devoted to distributed adaptive containment control for a class of nonlinear multiagent systems with input quantization. By employing a matrix factorization and a novel matrix normalization technique, some assumptions involving control gain matrices in existing results are relaxed. By fusing the techniques of sliding mode control and backstepping control, a two-step design method is proposed to construct controllers and, with the aid of neural networks, all system nonlinearities are allowed to be unknown. Moreover, a linear time-varying model and a similarity transformation are introduced to circumvent the obstacle brought by quantization, and the controllers need no information about the quantizer parameters. The proposed scheme is able to ensure the boundedness of all closed-loop signals and steer the containment errors into an arbitrarily small residual set. The simulation results illustrate the effectiveness of the scheme.
The influence of anatomical and physiological parameters on the interference voltage at the input of unipolar cardiac pacemakers in low frequency electric fields.

PubMed

Joosten, S; Pammler, K; Silny, J

2009-02-07

The problem of electromagnetic interference of electronic implants such as cardiac pacemakers has been well known for many years. An increasing number of field sources in everyday life and occupational environment leads unavoidably to an increased risk for patients with electronic implants. However, no obligatory national or international safety regulations exist for the protection of this patient group. The aim of this study is to find out the anatomical and physiological worst-case conditions for patients with an implanted pacemaker adjusted to unipolar sensing in external time-varying electric fields. The results of this study with 15 volunteers show that, in electric fields, variation of the interference voltage at the input of a cardiac pacemaker adds up to 200% only because of individual factors. These factors should be considered in human studies and in the setting of safety regulations.
Multiscale stochastic simulations for tensile testing of nanotube-based macroscopic cables.

PubMed

Pugno, Nicola M; Bosia, Federico; Carpinteri, Alberto

2008-08-01

Thousands of multiscale stochastic simulations are carried out in order to perform the first in-silico tensile tests of carbon nanotube (CNT)-based macroscopic cables with varying length. The longest treated cable is the space-elevator megacable but more realistic shorter cables are also considered in this bottom-up investigation. Different sizes, shapes, and concentrations of defects are simulated, resulting in cable macrostrengths not larger than approximately 10 GPa, which is much smaller than the theoretical nanotube strength (approximately 100 GPa). No best-fit parameters are present in the multiscale simulations: the input at level 1 is directly estimated from nanotensile tests of CNTs, whereas its output is considered as the input for the level 2, and so on up to level 5, corresponding to the megacable. Thus, five hierarchical levels are used to span lengths from that of a single nanotube (approximately 100 nm) to that of the space-elevator megacable (approximately 100 Mm).
Observer-based state tracking control of uncertain stochastic systems via repetitive controller

NASA Astrophysics Data System (ADS)

Sakthivel, R.; Susana Ramya, L.; Selvaraj, P.

2017-08-01

This paper develops the repetitive control scheme for state tracking control of uncertain stochastic time-varying delay systems via equivalent-input-disturbance approach. The main purpose of this work is to design a repetitive controller to guarantee the tracking performance under the effects of unknown disturbances with bounded frequency and parameter variations. Specifically, a new set of linear matrix inequality (LMI)-based conditions is derived based on the suitable Lyapunov-Krasovskii functional theory for designing a repetitive controller which guarantees stability and desired tracking performance. More precisely, an equivalent-input-disturbance estimator is incorporated into the control design to reduce the effect of the external disturbances. Simulation results are provided to demonstrate the desired control system stability and their tracking performance. A practical stream water quality preserving system is also provided to show the effectiveness and advantage of the proposed approach.
Estimation and impact assessment of input and parameter uncertainty in predicting groundwater flow with a fully distributed model

NASA Astrophysics Data System (ADS)

Touhidul Mustafa, Syed Md.; Nossent, Jiri; Ghysels, Gert; Huysmans, Marijke

2017-04-01

Transient numerical groundwater flow models have been used to understand and forecast groundwater flow systems under anthropogenic and climatic effects, but the reliability of the predictions is strongly influenced by different sources of uncertainty. Hence, researchers in hydrological sciences are developing and applying methods for uncertainty quantification. Nevertheless, spatially distributed flow models pose significant challenges for parameter and spatially distributed input estimation and uncertainty quantification. In this study, we present a general and flexible approach for input and parameter estimation and uncertainty analysis of groundwater models. The proposed approach combines a fully distributed groundwater flow model (MODFLOW) with the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. To avoid over-parameterization, the uncertainty of the spatially distributed model input has been represented by multipliers. The posterior distributions of these multipliers and the regular model parameters were estimated using DREAM. The proposed methodology has been applied in an overexploited aquifer in Bangladesh where groundwater pumping and recharge data are highly uncertain. The results confirm that input uncertainty does have a considerable effect on the model predictions and parameter distributions. Additionally, our approach also provides a new way to optimize the spatially distributed recharge and pumping data along with the parameter values under uncertain input conditions. It can be concluded from our approach that considering model input uncertainty along with parameter uncertainty is important for obtaining realistic model predictions and a correct estimation of the uncertainty bounds.
Surface wave excitation study

NASA Astrophysics Data System (ADS)

Burke, G. J.; King, R. J.; Miller, E. K.

1984-09-01

Relative communication efficiency (RCE) as defined by Fenwick and Weeks compares the field of a test antenna to that of a reference antenna at the same location for equal input plower to each antenna. Thus, RCE is similar to power gain but is definable in the presence of ground. The effectiveness of antennas in launching TM surface waves was compared. Antennas considered included the vertical dipole, monople on a ground stake, monopole on a radial-wire ground screen, Beverage antenna and vertical half rhombic. Since the performance of these antennas is strongly dependent on parameters such as the number wires in a ground screen or the length of a Beverage antenna, results are presented with parameters varying over a reasonable range. Thus, antenna performance can be weighed against the effort and limitations of construction.

Estimating historical atmospheric mercury concentrations from silver mining and their legacies in present-day surface soil in Potosí, Bolivia

NASA Astrophysics Data System (ADS)

Hagan, Nicole; Robins, Nicholas; Hsu-Kim, Heileen; Halabi, Susan; Morris, Mark; Woodall, George; Zhang, Tong; Bacon, Allan; Richter, Daniel De B.; Vandenberg, John

2011-12-01

Detailed Spanish records of mercury use and silver production during the colonial period in Potosí, Bolivia were evaluated to estimate atmospheric emissions of mercury from silver smelting. Mercury was used in the silver production process in Potosí and nearly 32,000 metric tons of mercury were released to the environment. AERMOD was used in combination with the estimated emissions to approximate historical air concentrations of mercury from colonial mining operations during 1715, a year of relatively low silver production. Source characteristics were selected from archival documents, colonial maps and images of silver smelters in Potosí and a base case of input parameters was selected. Input parameters were varied to understand the sensitivity of the model to each parameter. Modeled maximum 1-h concentrations were most sensitive to stack height and diameter, whereas an index of community exposure was relatively insensitive to uncertainty in input parameters. Modeled 1-h and long-term concentrations were compared to inhalation reference values for elemental mercury vapor. Estimated 1-h maximum concentrations within 500 m of the silver smelters consistently exceeded present-day occupational inhalation reference values. Additionally, the entire community was estimated to have been exposed to levels of mercury vapor that exceed present-day acute inhalation reference values for the general public. Estimated long-term maximum concentrations of mercury were predicted to substantially exceed the EPA Reference Concentration for areas within 600 m of the silver smelters. A concentration gradient predicted by AERMOD was used to select soil sampling locations along transects in Potosí. Total mercury in soils ranged from 0.105 to 155 mg kg-1, among the highest levels reported for surface soils in the scientific literature. The correlation between estimated air concentrations and measured soil concentrations will guide future research to determine the extent to which the current community of Potosí and vicinity is at risk of adverse health effects from historical mercury contamination.
Approximation-Based Adaptive Neural Tracking Control of Nonlinear MIMO Unknown Time-Varying Delay Systems With Full State Constraints.

PubMed

Li, Da-Peng; Li, Dong-Juan; Liu, Yan-Jun; Tong, Shaocheng; Chen, C L Philip

2017-10-01

This paper deals with the tracking control problem for a class of nonlinear multiple input multiple output unknown time-varying delay systems with full state constraints. To overcome the challenges which cause by the appearances of the unknown time-varying delays and full-state constraints simultaneously in the systems, an adaptive control method is presented for such systems for the first time. The appropriate Lyapunov-Krasovskii functions and a separation technique are employed to eliminate the effect of unknown time-varying delays. The barrier Lyapunov functions are employed to prevent the violation of the full state constraints. The singular problems are dealt with by introducing the signal function. Finally, it is proven that the proposed method can both guarantee the good tracking performance of the systems output, all states are remained in the constrained interval and all the closed-loop signals are bounded in the design process based on choosing appropriate design parameters. The practicability of the proposed control technique is demonstrated by a simulation study in this paper.
Parametric analysis of the biomechanical response of head subjected to the primary blast loading--a data mining approach.

PubMed

Zhu, Feng; Kalra, Anil; Saif, Tal; Yang, Zaihan; Yang, King H; King, Albert I

2016-01-01

Traumatic brain injury due to primary blast loading has become a signature injury in recent military conflicts and terrorist activities. Extensive experimental and computational investigations have been conducted to study the interrelationships between intracranial pressure response and intrinsic or 'input' parameters such as the head geometry and loading conditions. However, these relationships are very complicated and are usually implicit and 'hidden' in a large amount of simulation/test data. In this study, a data mining method is proposed to explore such underlying information from the numerical simulation results. The heads of different species are described as a highly simplified two-part (skull and brain) finite element model with varying geometric parameters. The parameters considered include peak incident pressure, skull thickness, brain radius and snout length. Their interrelationship and coupling effect are discovered by developing a decision tree based on the large simulation data-set. The results show that the proposed data-driven method is superior to the conventional linear regression method and is comparable to the nonlinear regression method. Considering its capability of exploring implicit information and the relatively simple relationships between response and input variables, the data mining method is considered to be a good tool for an in-depth understanding of the mechanisms of blast-induced brain injury. As a general method, this approach can also be applied to other nonlinear complex biomechanical systems.
Three-dimensional (3D) evaluation of liquid distribution in shake flask using an optical fluorescence technique.

PubMed

Azizan, Amizon; Büchs, Jochen

2017-01-01

Biotechnological development in shake flask necessitates vital engineering parameters e.g. volumetric power input, mixing time, gas liquid mass transfer coefficient, hydromechanical stress and effective shear rate. Determination and optimization of these parameters through experiments are labor-intensive and time-consuming. Computational Fluid Dynamics (CFD) provides the ability to predict and validate these parameters in bioprocess engineering. This work provides ample experimental data which are easily accessible for future validations to represent the hydrodynamics of the fluid flow in the shake flask. A non-invasive measuring technique using an optical fluorescence method was developed for shake flasks containing a fluorescent solution with a waterlike viscosity at varying filling volume (V L = 15 to 40 mL) and shaking frequency ( n = 150 to 450 rpm) at a constant shaking diameter (d o = 25 mm). The method detected the leading edge (LB) and tail of the rotating bulk liquid (TB) relative to the direction of the centrifugal acceleration at varying circumferential heights from the base of the shake flask. The determined LB and TB points were translated into three-dimensional (3D) circumferential liquid distribution plots. The maximum liquid height (H max ) of the bulk liquid increased with increasing filling volume and shaking frequency of the shaking flask, as expected. The toroidal shapes of LB and TB are clearly asymmetrical and the measured TB differed by the elongation of the liquid particularly towards the torus part of the shake flask. The 3D liquid distribution data collected at varying filling volume and shaking frequency, comprising of LB and TB values relative to the direction of the centrifugal acceleration are essential for validating future numerical solutions using CFD to predict vital engineering parameters in shake flask.
Kinetic analysis of single molecule FRET transitions without trajectories

NASA Astrophysics Data System (ADS)

Schrangl, Lukas; Göhring, Janett; Schütz, Gerhard J.

2018-03-01

Single molecule Förster resonance energy transfer (smFRET) is a popular tool to study biological systems that undergo topological transitions on the nanometer scale. smFRET experiments typically require recording of long smFRET trajectories and subsequent statistical analysis to extract parameters such as the states' lifetimes. Alternatively, analysis of probability distributions exploits the shapes of smFRET distributions at well chosen exposure times and hence works without the acquisition of time traces. Here, we describe a variant that utilizes statistical tests to compare experimental datasets with Monte Carlo simulations. For a given model, parameters are varied to cover the full realistic parameter space. As output, the method yields p-values which quantify the likelihood for each parameter setting to be consistent with the experimental data. The method provides suitable results even if the actual lifetimes differ by an order of magnitude. We also demonstrated the robustness of the method to inaccurately determine input parameters. As proof of concept, the new method was applied to the determination of transition rate constants for Holliday junctions.
Dual-input two-compartment pharmacokinetic model of dynamic contrast-enhanced magnetic resonance imaging in hepatocellular carcinoma.

PubMed

Yang, Jian-Feng; Zhao, Zhen-Hua; Zhang, Yu; Zhao, Li; Yang, Li-Ming; Zhang, Min-Ming; Wang, Bo-Yin; Wang, Ting; Lu, Bao-Chun

2016-04-07

To investigate the feasibility of a dual-input two-compartment tracer kinetic model for evaluating tumorous microvascular properties in advanced hepatocellular carcinoma (HCC). From January 2014 to April 2015, we prospectively measured and analyzed pharmacokinetic parameters [transfer constant (Ktrans), plasma flow (Fp), permeability surface area product (PS), efflux rate constant (kep), extravascular extracellular space volume ratio (ve), blood plasma volume ratio (vp), and hepatic perfusion index (HPI)] using dual-input two-compartment tracer kinetic models [a dual-input extended Tofts model and a dual-input 2-compartment exchange model (2CXM)] in 28 consecutive HCC patients. A well-known consensus that HCC is a hypervascular tumor supplied by the hepatic artery and the portal vein was used as a reference standard. A paired Student's t-test and a nonparametric paired Wilcoxon rank sum test were used to compare the equivalent pharmacokinetic parameters derived from the two models, and Pearson correlation analysis was also applied to observe the correlations among all equivalent parameters. The tumor size and pharmacokinetic parameters were tested by Pearson correlation analysis, while correlations among stage, tumor size and all pharmacokinetic parameters were assessed by Spearman correlation analysis. The Fp value was greater than the PS value (FP = 1.07 mL/mL per minute, PS = 0.19 mL/mL per minute) in the dual-input 2CXM; HPI was 0.66 and 0.63 in the dual-input extended Tofts model and the dual-input 2CXM, respectively. There were no significant differences in the kep, vp, or HPI between the dual-input extended Tofts model and the dual-input 2CXM (P = 0.524, 0.569, and 0.622, respectively). All equivalent pharmacokinetic parameters, except for ve, were correlated in the two dual-input two-compartment pharmacokinetic models; both Fp and PS in the dual-input 2CXM were correlated with Ktrans derived from the dual-input extended Tofts model (P = 0.002, r = 0.566; P = 0.002, r = 0.570); kep, vp, and HPI between the two kinetic models were positively correlated (P = 0.001, r = 0.594; P = 0.0001, r = 0.686; P = 0.04, r = 0.391, respectively). In the dual input extended Tofts model, ve was significantly less than that in the dual input 2CXM (P = 0.004), and no significant correlation was seen between the two tracer kinetic models (P = 0.156, r = 0.276). Neither tumor size nor tumor stage was significantly correlated with any of the pharmacokinetic parameters obtained from the two models (P > 0.05). A dual-input two-compartment pharmacokinetic model (a dual-input extended Tofts model and a dual-input 2CXM) can be used in assessing the microvascular physiopathological properties before the treatment of advanced HCC. The dual-input extended Tofts model may be more stable in measuring the ve; however, the dual-input 2CXM may be more detailed and accurate in measuring microvascular permeability.
Effect of pore water velocities and solute input methods on chloride transport in the undisturbed soil columns of Loess Plateau

NASA Astrophysics Data System (ADS)

Zhou, BeiBei; Wang, QuanJiu

2017-09-01

Studies on solute transport under different pore water velocity and solute input methods in undisturbed soil could play instructive roles for crop production. Based on the experiments in the laboratory, the effect of solute input methods with small pulse input and large pulse input, as well as four pore water velocities, on chloride transport in the undisturbed soil columns obtained from the Loess Plateau under controlled condition was studied. Chloride breakthrough curves (BTCs) were generated using the miscible displacement method under water-saturated, steady flow conditions. Using the 0.15 mol L-1 CaCl2 solution as a tracer, a small pulse (0.1 pore volumes) was first induced, and then, after all the solution was wash off, a large pulse (0.5 pore volumes) was conducted. The convection-dispersion equation (CDE) and the two-region model (T-R) were used to describe the BTCs, and their prediction accuracies and fitted parameters were compared as well. All the BTCs obtained for the different input methods and the four pore water velocities were all smooth. However, the shapes of the BTCs varied greatly; small pulse inputs resulted in more rapid attainment of peak values that appeared earlier with increases in pore water velocity, whereas large pulse inputs resulted in an opposite trend. Both models could fit the experimental data well, but the prediction accuracy of the T-R was better. The values of the dispersivity, λ, calculated from the dispersion coefficient obtained from the CDE were about one order of magnitude larger than those calculated from the dispersion coefficient given by the T-R, but the calculated Peclet number, Pe, was lower. The mobile-immobile partition coefficient, β, decreased, while the mass exchange coefficient increased with increases in pore water velocity.
Adaptive non-predictor control of lower triangular uncertain nonlinear systems with an unknown time-varying delay in the input

NASA Astrophysics Data System (ADS)

Koo, Min-Sung; Choi, Ho-Lim

2018-01-01

In this paper, we consider a control problem for a class of uncertain nonlinear systems in which there exists an unknown time-varying delay in the input and lower triangular nonlinearities. Usually, in the existing results, input delays have been coupled with feedforward (or upper triangular) nonlinearities; in other words, the combination of lower triangular nonlinearities and input delay has been rare. Motivated by the existing controller for input-delayed chain of integrators with nonlinearity, we show that the control of input-delayed nonlinear systems with two particular types of lower triangular nonlinearities can be done. As a control solution, we propose a newly designed feedback controller whose main features are its dynamic gain and non-predictor approach. Three examples are given for illustration.
Aircraft noise synthesis system

NASA Technical Reports Server (NTRS)

Mccurdy, David A.; Grandle, Robert E.

1987-01-01

A second-generation Aircraft Noise Synthesis System has been developed to provide test stimuli for studies of community annoyance to aircraft flyover noise. The computer-based system generates realistic, time-varying, audio simulations of aircraft flyover noise at a specified observer location on the ground. The synthesis takes into account the time-varying aircraft position relative to the observer; specified reference spectra consisting of broadband, narrowband, and pure-tone components; directivity patterns; Doppler shift; atmospheric effects; and ground effects. These parameters can be specified and controlled in such a way as to generate stimuli in which certain noise characteristics, such as duration or tonal content, are independently varied, while the remaining characteristics, such as broadband content, are held constant. The system can also generate simulations of the predicted noise characteristics of future aircraft. A description of the synthesis system and a discussion of the algorithms and methods used to generate the simulations are provided. An appendix describing the input data and providing user instructions is also included.
A geostatistics-informed hierarchical sensitivity analysis method for complex groundwater flow and transport modeling

NASA Astrophysics Data System (ADS)

Dai, Heng; Chen, Xingyuan; Ye, Ming; Song, Xuehang; Zachara, John M.

2017-05-01

Sensitivity analysis is an important tool for development and improvement of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. In this study, we developed a new sensitivity analysis method that integrates the concept of variance-based method with a hierarchical uncertainty quantification framework. Different uncertain inputs are grouped and organized into a multilayer framework based on their characteristics and dependency relationships to reduce the dimensionality of the sensitivity analysis. A set of new sensitivity indices are defined for the grouped inputs using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially distributed input variables.
A Geostatistics-Informed Hierarchical Sensitivity Analysis Method for Complex Groundwater Flow and Transport Modeling

NASA Astrophysics Data System (ADS)

Dai, H.; Chen, X.; Ye, M.; Song, X.; Zachara, J. M.

2017-12-01

Sensitivity analysis is an important tool for development and improvement of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. In this study we developed a new sensitivity analysis method that integrates the concept of variance-based method with a hierarchical uncertainty quantification framework. Different uncertain inputs are grouped and organized into a multi-layer framework based on their characteristics and dependency relationships to reduce the dimensionality of the sensitivity analysis. A set of new sensitivity indices are defined for the grouped inputs using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially-distributed input variables.
Parametric analysis of parameters for electrical-load forecasting using artificial neural networks

NASA Astrophysics Data System (ADS)

Gerber, William J.; Gonzalez, Avelino J.; Georgiopoulos, Michael

1997-04-01

Accurate total system electrical load forecasting is a necessary part of resource management for power generation companies. The better the hourly load forecast, the more closely the power generation assets of the company can be configured to minimize the cost. Automating this process is a profitable goal and neural networks should provide an excellent means of doing the automation. However, prior to developing such a system, the optimal set of input parameters must be determined. The approach of this research was to determine what those inputs should be through a parametric study of potentially good inputs. Input parameters tested were ambient temperature, total electrical load, the day of the week, humidity, dew point temperature, daylight savings time, length of daylight, season, forecast light index and forecast wind velocity. For testing, a limited number of temperatures and total electrical loads were used as a basic reference input parameter set. Most parameters showed some forecasting improvement when added individually to the basic parameter set. Significantly, major improvements were exhibited with the day of the week, dew point temperatures, additional temperatures and loads, forecast light index and forecast wind velocity.
Homeostasis, singularities, and networks.

PubMed

Golubitsky, Martin; Stewart, Ian

2017-01-01

Homeostasis occurs in a biological or chemical system when some output variable remains approximately constant as an input parameter [Formula: see text] varies over some interval. We discuss two main aspects of homeostasis, both related to the effect of coordinate changes on the input-output map. The first is a reformulation of homeostasis in the context of singularity theory, achieved by replacing 'approximately constant over an interval' by 'zero derivative of the output with respect to the input at a point'. Unfolding theory then classifies all small perturbations of the input-output function. In particular, the 'chair' singularity, which is especially important in applications, is discussed in detail. Its normal form and universal unfolding [Formula: see text] is derived and the region of approximate homeostasis is deduced. The results are motivated by data on thermoregulation in two species of opossum and the spiny rat. We give a formula for finding chair points in mathematical models by implicit differentiation and apply it to a model of lateral inhibition. The second asks when homeostasis is invariant under appropriate coordinate changes. This is false in general, but for network dynamics there is a natural class of coordinate changes: those that preserve the network structure. We characterize those nodes of a given network for which homeostasis is invariant under such changes. This characterization is determined combinatorially by the network topology.
Numerical analysis of the heat source characteristics of a two-electrode TIG arc

NASA Astrophysics Data System (ADS)

Ogino, Y.; Hirata, Y.; Nomura, K.

2011-06-01

Various kinds of multi-electrode welding processes are used to ensure high productivity in industrial fields such as shipbuilding, automotive manufacturing and pipe fabrication. However, it is difficult to obtain the optimum welding conditions for a specific product, because there are many operating parameters, and because welding phenomena are very complicated. In the present research, the heat source characteristics of a two-electrode TIG arc were numerically investigated using a 3D arc plasma model with a focus on the distance between the two electrodes. The arc plasma shape changed significantly, depending on the electrode spacing. The heat source characteristics, such as the heat input density and the arc pressure distribution, changed significantly when the electrode separation was varied. The maximum arc pressure of the two-electrode TIG arc was much lower than that of a single-electrode TIG. However, the total heat input of the two-electrode TIG arc was nearly constant and was independent of the electrode spacing. These heat source characteristics of the two-electrode TIG arc are useful for controlling the heat input distribution at a low arc pressure. Therefore, these results indicate the possibility of a heat source based on a two-electrode TIG arc that is capable of high heat input at low pressures.
Entry dynamics of space shuttle orbiter with lateral-directional stability and control uncertainties at supersonic and hypersonic speeds

NASA Technical Reports Server (NTRS)

Stone, H. W.; Powell, R. W.

1977-01-01

A six-degree-of-freedom simulation analysis was conducted to examine the effects of the lateral-directional static aerodynamic stability and control uncertainties on the performance of the automatic (no manual inputs) entry-guidance and control systems of the space shuttle orbiter. To establish the acceptable boundaries of the uncertainties, the static aerodynamic characteristics were varied either by applying a multiplier to the aerodynamic parameter or by adding an increment. Control-system modifications were identified that decrease the sensitivity to off-nominal aerodynamics. With these modifications, the acceptable aerodynamic boundaries were determined.
An improved computer program for calculating the theoretical performance parameters of a propeller type wind turbine. An appendix to the final report on feasibility of using wind power to pump irrigation water (Texas). [PROP Code

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

Barieau, R.E.

1977-03-01

The PROP Program of Wilson and Lissaman has been modified by adding the Newton-Raphson Method and a Step Wise Search Method, as options for the method of solution. In addition, an optimization method is included. Twist angles, tip speed ratio and the pitch angle may be varied to produce maximum power coefficient. The computer program listing is presented along with sample input and output data. Further improvements to the program are discussed.
A Flight Dynamics Model for a Small Glider in Ambient Winds

NASA Technical Reports Server (NTRS)

Beeler, Scott C.; Moerder, Daniel D.; Cox, David E.

2003-01-01

In this paper we describe the equations of motion developed for a point-mass zero-thrust (gliding) aircraft model operating in an environment of spatially varying atmospheric winds. The wind effects are included as an integral part of the flight dynamics equations, and the model is controlled through the three aerodynamic control angles. Formulas for the aerodynamic coefficients for this model are constructed to include the effects of several different aspects contributing to the aerodynamic performance of the vehicle. Characteristic parameter values of the model are compared with those found in a different set of small glider simulations. We execute a set of example problems which solve the glider dynamics equations to find the aircraft trajectory given specified control inputs. The ambient wind conditions and glider characteristics are varied to compare the simulation results under these different circumstances.
A Flight Dynamics Model for a Small Glider in Ambient Winds

NASA Technical Reports Server (NTRS)

Beeler, Scott C.; Moerder, Daniel D.; Cox, David E.

2003-01-01

In this paper we describe the equations of motion developed for a point-mass zero-thrust (gliding) aircraft model operating in an environment of spatially varying atmospheric winds. The wind effects are included as an integral part of the flight dynamics equations, and the model is controlled through the three aerodynamic control angles. Formulas for the aerodynamic coefficients for this model are constructed to include the effects of several different aspects contributing to the aerodynamic performance of the vehicle. Characteristic parameter values of the model are compared with those found in a different set of small glider simulations. We execute a set of example problems which solve the glider dynamics equations to find aircraft trajectory given specified control inputs. The ambient wind conditions and glider characteristics are varied to compare the simulation results under these different circumstances.
Nonlinear dynamic characteristics of dielectric elastomer membranes

NASA Astrophysics Data System (ADS)

Fox, Jason W.; Goulbourne, Nakhiah C.

2008-03-01

The dynamic response of dielectric elastomer membranes subject to time-varying voltage inputs for various initial inflation states is investigated. These results provide new insight into the differences observed between quasi-static and dynamic actuation and presents a new challenge to modeling efforts. Dielectric elastomer membranes are a potentially enabling technology for soft robotics and biomedical devices such as implants and surgical tools. In this work, two key system parameters are varied: the chamber volume and the voltage signal offset. The chamber volume experiments reveal that increasing the size of the chamber onto which the membrane is clamped will increase the deformations as well as cause the membrane's resonance peaks to shift and change in number. For prestretched dielectric elastomer membranes at the smallest chamber volume, the maximum actuation displacement is 81 microns; while at the largest chamber volume, the maximum actuation displacement is 1431 microns. This corresponds to a 1767% increase in maximum pole displacement. In addition, actuating the membrane at the resonance frequencies provides hundreds of percent increase in strain compared to the quasi-static strain. Adding a voltage offset to the time-varying input signal causes the membrane to oscillate at two distinct frequencies rather than one and also presents a unique opportunity to increase the output displacement without electrically overloading the membrane. Experiments to capture the entire motion of the membrane reveal that classical membrane mode shapes are electrically generated although all points of the membrane do not pass through equilibrium at the same moments in time.
Relative sensitivities of DCE-MRI pharmacokinetic parameters to arterial input function (AIF) scaling.

PubMed

Li, Xin; Cai, Yu; Moloney, Brendan; Chen, Yiyi; Huang, Wei; Woods, Mark; Coakley, Fergus V; Rooney, William D; Garzotto, Mark G; Springer, Charles S

2016-08-01

Dynamic-Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been used widely for clinical applications. Pharmacokinetic modeling of DCE-MRI data that extracts quantitative contrast reagent/tissue-specific model parameters is the most investigated method. One of the primary challenges in pharmacokinetic analysis of DCE-MRI data is accurate and reliable measurement of the arterial input function (AIF), which is the driving force behind all pharmacokinetics. Because of effects such as inflow and partial volume averaging, AIF measured from individual arteries sometimes require amplitude scaling for better representation of the blood contrast reagent (CR) concentration time-courses. Empirical approaches like blinded AIF estimation or reference tissue AIF derivation can be useful and practical, especially when there is no clearly visible blood vessel within the imaging field-of-view (FOV). Similarly, these approaches generally also require magnitude scaling of the derived AIF time-courses. Since the AIF varies among individuals even with the same CR injection protocol and the perfect scaling factor for reconstructing the ground truth AIF often remains unknown, variations in estimated pharmacokinetic parameters due to varying AIF scaling factors are of special interest. In this work, using simulated and real prostate cancer DCE-MRI data, we examined parameter variations associated with AIF scaling. Our results show that, for both the fast-exchange-limit (FXL) Tofts model and the water exchange sensitized fast-exchange-regime (FXR) model, the commonly fitted CR transfer constant (K(trans)) and the extravascular, extracellular volume fraction (ve) scale nearly proportionally with the AIF, whereas the FXR-specific unidirectional cellular water efflux rate constant, kio, and the CR intravasation rate constant, kep, are both AIF scaling insensitive. This indicates that, for DCE-MRI of prostate cancer and possibly other cancers, kio and kep may be more suitable imaging biomarkers for cross-platform, multicenter applications. Data from our limited study cohort show that kio correlates with Gleason scores, suggesting that it may be a useful biomarker for prostate cancer disease progression monitoring. Copyright © 2016 Elsevier Inc. All rights reserved.

State, Parameter, and Unknown Input Estimation Problems in Active Automotive Safety Applications

NASA Astrophysics Data System (ADS)

Phanomchoeng, Gridsada

A variety of driver assistance systems such as traction control, electronic stability control (ESC), rollover prevention and lane departure avoidance systems are being developed by automotive manufacturers to reduce driver burden, partially automate normal driving operations, and reduce accidents. The effectiveness of these driver assistance systems can be significant enhanced if the real-time values of several vehicle parameters and state variables, namely tire-road friction coefficient, slip angle, roll angle, and rollover index, can be known. Since there are no inexpensive sensors available to measure these variables, it is necessary to estimate them. However, due to the significant nonlinear dynamics in a vehicle, due to unknown and changing plant parameters, and due to the presence of unknown input disturbances, the design of estimation algorithms for this application is challenging. This dissertation develops a new approach to observer design for nonlinear systems in which the nonlinearity has a globally (or locally) bounded Jacobian. The developed approach utilizes a modified version of the mean value theorem to express the nonlinearity in the estimation error dynamics as a convex combination of known matrices with time varying coefficients. The observer gains are then obtained by solving linear matrix inequalities (LMIs). A number of illustrative examples are presented to show that the developed approach is less conservative and more useful than the standard Lipschitz assumption based nonlinear observer. The developed nonlinear observer is utilized for estimation of slip angle, longitudinal vehicle velocity, and vehicle roll angle. In order to predict and prevent vehicle rollovers in tripped situations, it is necessary to estimate the vertical tire forces in the presence of unknown road disturbance inputs. An approach to estimate unknown disturbance inputs in nonlinear systems using dynamic model inversion and a modified version of the mean value theorem is presented. The developed theory is used to estimate vertical tire forces and predict tripped rollovers in situations involving road bumps, potholes, and lateral unknown force inputs. To estimate the tire-road friction coefficients at each individual tire of the vehicle, algorithms to estimate longitudinal forces and slip ratios at each tire are proposed. Subsequently, tire-road friction coefficients are obtained using recursive least squares parameter estimators that exploit the relationship between longitudinal force and slip ratio at each tire. The developed approaches are evaluated through simulations with industry standard software, CARSIM, with experimental tests on a Volvo XC90 sport utility vehicle and with experimental tests on a 1/8th scaled vehicle. The simulation and experimental results show that the developed approaches can reliably estimate the vehicle parameters and state variables needed for effective ESC and rollover prevention applications.
Net thrust calculation sensitivity of an afterburning turbofan engine to variations in input parameters

NASA Technical Reports Server (NTRS)

Hughes, D. L.; Ray, R. J.; Walton, J. T.

1985-01-01

The calculated value of net thrust of an aircraft powered by a General Electric F404-GE-400 afterburning turbofan engine was evaluated for its sensitivity to various input parameters. The effects of a 1.0-percent change in each input parameter on the calculated value of net thrust with two calculation methods are compared. This paper presents the results of these comparisons and also gives the estimated accuracy of the overall net thrust calculation as determined from the influence coefficients and estimated parameter measurement accuracies.
Coincidence detection in the medial superior olive: mechanistic implications of an analysis of input spiking patterns

PubMed Central

Franken, Tom P.; Bremen, Peter; Joris, Philip X.

2014-01-01

Coincidence detection by binaural neurons in the medial superior olive underlies sensitivity to interaural time difference (ITD) and interaural correlation (ρ). It is unclear whether this process is akin to a counting of individual coinciding spikes, or rather to a correlation of membrane potential waveforms resulting from converging inputs from each side. We analyzed spike trains of axons of the cat trapezoid body (TB) and auditory nerve (AN) in a binaural coincidence scheme. ITD was studied by delaying “ipsi-” vs. “contralateral” inputs; ρ was studied by using responses to different noises. We varied the number of inputs; the monaural and binaural threshold and the coincidence window duration. We examined physiological plausibility of output “spike trains” by comparing their rate and tuning to ITD and ρ to those of binaural cells. We found that multiple inputs are required to obtain a plausible output spike rate. In contrast to previous suggestions, monaural threshold almost invariably needed to exceed binaural threshold. Elevation of the binaural threshold to values larger than 2 spikes caused a drastic decrease in rate for a short coincidence window. Longer coincidence windows allowed a lower number of inputs and higher binaural thresholds, but decreased the depth of modulation. Compared to AN fibers, TB fibers allowed higher output spike rates for a low number of inputs, but also generated more monaural coincidences. We conclude that, within the parameter space explored, the temporal patterns of monaural fibers require convergence of multiple inputs to achieve physiological binaural spike rates; that monaural coincidences have to be suppressed relative to binaural ones; and that the neuron has to be sensitive to single binaural coincidences of spikes, for a number of excitatory inputs per side of 10 or less. These findings suggest that the fundamental operation in the mammalian binaural circuit is coincidence counting of single binaural input spikes. PMID:24822037
Guidelines for reducing dynamic loads in two-bladed teetering-hub downwind wind turbines

NASA Astrophysics Data System (ADS)

Wright, A. D.; Bir, G. S.; Butterfield, C. D.

1995-06-01

A major goal of the federal Wind Energy Program is the rapid development and validation of structural models to determine loads and response for a wide variety of different wind turbine configurations operating under extreme conditions. Such codes are crucial to the successful design of future advanced wind turbines. In previous papers the authors described steps they took to develop a model of a two-bladed teetering-hub downwind wind turbine using ADAMS (Automatic Dynamic Analysis of Mechanical Systems), as well as comparison of model predictions to test data. In this paper they show the use of this analytical model to study the influence of various turbine parameters on predicted system loads. They concentrate their study on turbine response in the frequency range of six to ten times the rotor rotational frequency (6P to 10P). Their goal is to identify the most important parameters which influence the response of this type of machine in this frequency range and give turbine designers some general design guidelines for designing two-bladed teetering-hub machines to be less susceptible to vibration. They study the effects of such parameters as blade edgewise and flapwise stiffness, tower top stiffness, blade tip-brake mass, low-speed shaft stiffness, nacelle mass momenta of inertia, and rotor speed. They show which parameters can be varied in order to make the turbine less responsive to such atmospheric inputs as wind shear and tower shadow. They then give designers a set of design guidelines in order to show how these machines can be designed to be less responsive to these inputs.
Optimal input shaping for Fisher identifiability of control-oriented lithium-ion battery models

NASA Astrophysics Data System (ADS)

Rothenberger, Michael J.

This dissertation examines the fundamental challenge of optimally shaping input trajectories to maximize parameter identifiability of control-oriented lithium-ion battery models. Identifiability is a property from information theory that determines the solvability of parameter estimation for mathematical models using input-output measurements. This dissertation creates a framework that exploits the Fisher information metric to quantify the level of battery parameter identifiability, optimizes this metric through input shaping, and facilitates faster and more accurate estimation. The popularity of lithium-ion batteries is growing significantly in the energy storage domain, especially for stationary and transportation applications. While these cells have excellent power and energy densities, they are plagued with safety and lifespan concerns. These concerns are often resolved in the industry through conservative current and voltage operating limits, which reduce the overall performance and still lack robustness in detecting catastrophic failure modes. New advances in automotive battery management systems mitigate these challenges through the incorporation of model-based control to increase performance, safety, and lifespan. To achieve these goals, model-based control requires accurate parameterization of the battery model. While many groups in the literature study a variety of methods to perform battery parameter estimation, a fundamental issue of poor parameter identifiability remains apparent for lithium-ion battery models. This fundamental challenge of battery identifiability is studied extensively in the literature, and some groups are even approaching the problem of improving the ability to estimate the model parameters. The first approach is to add additional sensors to the battery to gain more information that is used for estimation. The other main approach is to shape the input trajectories to increase the amount of information that can be gained from input-output measurements, and is the approach used in this dissertation. Research in the literature studies optimal current input shaping for high-order electrochemical battery models and focuses on offline laboratory cycling. While this body of research highlights improvements in identifiability through optimal input shaping, each optimal input is a function of nominal parameters, which creates a tautology. The parameter values must be known a priori to determine the optimal input for maximizing estimation speed and accuracy. The system identification literature presents multiple studies containing methods that avoid the challenges of this tautology, but these methods are absent from the battery parameter estimation domain. The gaps in the above literature are addressed in this dissertation through the following five novel and unique contributions. First, this dissertation optimizes the parameter identifiability of a thermal battery model, which Sergio Mendoza experimentally validates through a close collaboration with this dissertation's author. Second, this dissertation extends input-shaping optimization to a linear and nonlinear equivalent-circuit battery model and illustrates the substantial improvements in Fisher identifiability for a periodic optimal signal when compared against automotive benchmark cycles. Third, this dissertation presents an experimental validation study of the simulation work in the previous contribution. The estimation study shows that the automotive benchmark cycles either converge slower than the optimized cycle, or not at all for certain parameters. Fourth, this dissertation examines how automotive battery packs with additional power electronic components that dynamically route current to individual cells/modules can be used for parameter identifiability optimization. While the user and vehicle supervisory controller dictate the current demand for these packs, the optimized internal allocation of current still improves identifiability. Finally, this dissertation presents a robust Bayesian sequential input shaping optimization study to maximize the conditional Fisher information of the battery model parameters without prior knowledge of the nominal parameter set. This iterative algorithm only requires knowledge of the prior parameter distributions to converge to the optimal input trajectory.
A Machine Learning and Cross-Validation Approach for the Discrimination of Vegetation Physiognomic Types Using Satellite Based Multispectral and Multitemporal Data.

PubMed

Sharma, Ram C; Hara, Keitarou; Hirayama, Hidetake

2017-01-01

This paper presents the performance and evaluation of a number of machine learning classifiers for the discrimination between the vegetation physiognomic classes using the satellite based time-series of the surface reflectance data. Discrimination of six vegetation physiognomic classes, Evergreen Coniferous Forest, Evergreen Broadleaf Forest, Deciduous Coniferous Forest, Deciduous Broadleaf Forest, Shrubs, and Herbs, was dealt with in the research. Rich-feature data were prepared from time-series of the satellite data for the discrimination and cross-validation of the vegetation physiognomic types using machine learning approach. A set of machine learning experiments comprised of a number of supervised classifiers with different model parameters was conducted to assess how the discrimination of vegetation physiognomic classes varies with classifiers, input features, and ground truth data size. The performance of each experiment was evaluated by using the 10-fold cross-validation method. Experiment using the Random Forests classifier provided highest overall accuracy (0.81) and kappa coefficient (0.78). However, accuracy metrics did not vary much with experiments. Accuracy metrics were found to be very sensitive to input features and size of ground truth data. The results obtained in the research are expected to be useful for improving the vegetation physiognomic mapping in Japan.
Calibrating binary lumped parameter models

NASA Astrophysics Data System (ADS)

Morgenstern, Uwe; Stewart, Mike

2017-04-01

Groundwater at its discharge point is a mixture of water from short and long flowlines, and therefore has a distribution of ages rather than a single age. Various transfer functions describe the distribution of ages within the water sample. Lumped parameter models (LPMs), which are mathematical models of water transport based on simplified aquifer geometry and flow configuration can account for such mixing of groundwater of different age, usually representing the age distribution with two parameters, the mean residence time, and the mixing parameter. Simple lumped parameter models can often match well the measured time varying age tracer concentrations, and therefore are a good representation of the groundwater mixing at these sites. Usually a few tracer data (time series and/or multi-tracer) can constrain both parameters. With the building of larger data sets of age tracer data throughout New Zealand, including tritium, SF6, CFCs, and recently Halon-1301, and time series of these tracers, we realised that for a number of wells the groundwater ages using a simple lumped parameter model were inconsistent between the different tracer methods. Contamination or degradation of individual tracers is unlikely because the different tracers show consistent trends over years and decades. This points toward a more complex mixing of groundwaters with different ages for such wells than represented by the simple lumped parameter models. Binary (or compound) mixing models are able to represent a more complex mixing, with mixing of water of two different age distributions. The problem related to these models is that they usually have 5 parameters which makes them data-hungry and therefore difficult to constrain all parameters. Two or more age tracers with different input functions, with multiple measurements over time, can provide the required information to constrain the parameters of the binary mixing model. We obtained excellent results using tritium time series encompassing the passage of the bomb-tritium through the aquifer, and SF6 with its steep gradient currently in the input. We will show age tracer data from drinking water wells that enabled identification of young water ingression into wells, which poses the risk of bacteriological contamination from the surface into the drinking water.
Genetic Algorithm-Based Model Order Reduction of Aeroservoelastic Systems with Consistant States

NASA Technical Reports Server (NTRS)

Zhu, Jin; Wang, Yi; Pant, Kapil; Suh, Peter M.; Brenner, Martin J.

2017-01-01

This paper presents a model order reduction framework to construct linear parameter-varying reduced-order models of flexible aircraft for aeroservoelasticity analysis and control synthesis in broad two-dimensional flight parameter space. Genetic algorithms are used to automatically determine physical states for reduction and to generate reduced-order models at grid points within parameter space while minimizing the trial-and-error process. In addition, balanced truncation for unstable systems is used in conjunction with the congruence transformation technique to achieve locally optimal realization and weak fulfillment of state consistency across the entire parameter space. Therefore, aeroservoelasticity reduced-order models at any flight condition can be obtained simply through model interpolation. The methodology is applied to the pitch-plant model of the X-56A Multi-Use Technology Testbed currently being tested at NASA Armstrong Flight Research Center for flutter suppression and gust load alleviation. The present studies indicate that the reduced-order model with more than 12× reduction in the number of states relative to the original model is able to accurately predict system response among all input-output channels. The genetic-algorithm-guided approach exceeds manual and empirical state selection in terms of efficiency and accuracy. The interpolated aeroservoelasticity reduced order models exhibit smooth pole transition and continuously varying gains along a set of prescribed flight conditions, which verifies consistent state representation obtained by congruence transformation. The present model order reduction framework can be used by control engineers for robust aeroservoelasticity controller synthesis and novel vehicle design.
Genetic Algorithm-Guided, Adaptive Model Order Reduction of Flexible Aircrafts

NASA Technical Reports Server (NTRS)

Zhu, Jin; Wang, Yi; Pant, Kapil; Suh, Peter; Brenner, Martin J.

2017-01-01

This paper presents a methodology for automated model order reduction (MOR) of flexible aircrafts to construct linear parameter-varying (LPV) reduced order models (ROM) for aeroservoelasticity (ASE) analysis and control synthesis in broad flight parameter space. The novelty includes utilization of genetic algorithms (GAs) to automatically determine the states for reduction while minimizing the trial-and-error process and heuristics requirement to perform MOR; balanced truncation for unstable systems to achieve locally optimal realization of the full model; congruence transformation for "weak" fulfillment of state consistency across the entire flight parameter space; and ROM interpolation based on adaptive grid refinement to generate a globally functional LPV ASE ROM. The methodology is applied to the X-56A MUTT model currently being tested at NASA/AFRC for flutter suppression and gust load alleviation. Our studies indicate that X-56A ROM with less than one-seventh the number of states relative to the original model is able to accurately predict system response among all input-output channels for pitch, roll, and ASE control at various flight conditions. The GA-guided approach exceeds manual and empirical state selection in terms of efficiency and accuracy. The adaptive refinement allows selective addition of the grid points in the parameter space where flight dynamics varies dramatically to enhance interpolation accuracy without over-burdening controller synthesis and onboard memory efforts downstream. The present MOR framework can be used by control engineers for robust ASE controller synthesis and novel vehicle design.
Insolation-oriented model of photovoltaic module using Matlab/Simulink

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

Tsai, Huan-Liang

2010-07-15

This paper presents a novel model of photovoltaic (PV) module which is implemented and analyzed using Matlab/Simulink software package. Taking the effect of sunlight irradiance on the cell temperature, the proposed model takes ambient temperature as reference input and uses the solar insolation as a unique varying parameter. The cell temperature is then explicitly affected by the sunlight intensity. The output current and power characteristics are simulated and analyzed using the proposed PV model. The model verification has been confirmed through an experimental measurement. The impact of solar irradiation on cell temperature makes the output characteristic more practical. In addition,more » the insolation-oriented PV model enables the dynamics of PV power system to be analyzed and optimized more easily by applying the environmental parameters of ambient temperature and solar irradiance. (author)« less
Direct statistical modeling and its implications for predictive mapping in mining exploration

NASA Astrophysics Data System (ADS)

Sterligov, Boris; Gumiaux, Charles; Barbanson, Luc; Chen, Yan; Cassard, Daniel; Cherkasov, Sergey; Zolotaya, Ludmila

2010-05-01

Recent advances in geosciences make more and more multidisciplinary data available for mining exploration. This allowed developing methodologies for computing forecast ore maps from the statistical combination of such different input parameters, all based on an inverse problem theory. Numerous statistical methods (e.g. algebraic method, weight of evidence, Siris method, etc) with varying degrees of complexity in their development and implementation, have been proposed and/or adapted for ore geology purposes. In literature, such approaches are often presented through applications on natural examples and the results obtained can present specificities due to local characteristics. Moreover, though crucial for statistical computations, "minimum requirements" needed for input parameters (number of minimum data points, spatial distribution of objects, etc) are often only poorly expressed. From these, problems often arise when one has to choose between one and the other method for her/his specific question. In this study, a direct statistical modeling approach is developed in order to i) evaluate the constraints on the input parameters and ii) test the validity of different existing inversion methods. The approach particularly focused on the analysis of spatial relationships between location of points and various objects (e.g. polygons and /or polylines) which is particularly well adapted to constrain the influence of intrusive bodies - such as a granite - and faults or ductile shear-zones on spatial location of ore deposits (point objects). The method is designed in a way to insure a-dimensionality with respect to scale. In this approach, both spatial distribution and topology of objects (polygons and polylines) can be parametrized by the user (e.g. density of objects, length, surface, orientation, clustering). Then, the distance of points with respect to a given type of objects (polygons or polylines) is given using a probability distribution. The location of points is computed assuming either independency or different grades of dependency between the two probability distributions. The results show that i)polygons surface mean value, polylines length mean value, the number of objects and their clustering are critical and ii) the validity of the different tested inversion methods strongly depends on the relative importance and on the dependency between the parameters used. In addition, this combined approach of direct and inverse modeling offers an opportunity to test the robustness of the inferred distribution point laws with respect to the quality of the input data set.
Automated forward mechanical modeling of wrinkle ridges on Mars

NASA Astrophysics Data System (ADS)

Nahm, Amanda; Peterson, Samuel

2016-04-01

One of the main goals of the InSight mission to Mars is to understand the internal structure of Mars [1], in part through passive seismology. Understanding the shallow surface structure of the landing site is critical to the robust interpretation of recorded seismic signals. Faults, such as the wrinkle ridges abundant in the proposed landing site in Elysium Planitia, can be used to determine the subsurface structure of the regions they deform. Here, we test a new automated method for modeling of the topography of a wrinkle ridge (WR) in Elysium Planitia, allowing for faster and more robust determination of subsurface fault geometry for interpretation of the local subsurface structure. We perform forward mechanical modeling of fault-related topography [e.g., 2, 3], utilizing the modeling program Coulomb [4, 5] to model surface displacements surface induced by blind thrust faulting. Fault lengths are difficult to determine for WR; we initially assume a fault length of 30 km, but also test the effects of different fault lengths on model results. At present, we model the wrinkle ridge as a single blind thrust fault with a constant fault dip, though WR are likely to have more complicated fault geometry [e.g., 6-8]. Typically, the modeling is performed using the Coulomb GUI. This approach can be time consuming, requiring user inputs to change model parameters and to calculate the associated displacements for each model, which limits the number of models and parameter space that can be tested. To reduce active user computation time, we have developed a method in which the Coulomb GUI is bypassed. The general modeling procedure remains unchanged, and a set of input files is generated before modeling with ranges of pre-defined parameter values. The displacement calculations are divided into two suites. For Suite 1, a total of 3770 input files were generated in which the fault displacement (D), dip angle (δ), depth to upper fault tip (t), and depth to lower fault tip (B) were varied. A second set of input files was created (Suite 2) after the best-fit model from Suite 1 was determined, in which fault parameters were varied with a smaller range and incremental changes, resulting in a total of 28,080 input files. RMS values were calculated for each Coulomb model. RMS values for Suite 1 models were calculated over the entire profile and for a restricted x range; the latter shows a reduced RMS misfit by 1.2 m. The minimum RMS value for Suite 2 models decreases again by 0.2 m, resulting in an overall reduction of the RMS value of ~1.4 m (18%). Models with different fault lengths (15, 30, and 60 km) are visually indistinguishable. Values for δ, t, B, and RMS misfit are either the same or very similar for each best fit model. These results indicate that the subsurface structure can be reliably determined from forward mechanical modeling even with uncertainty in fault length. Future work will test this method with the more realistic WR fault geometry. References: [1] Banerdt et al. (2013), 44th LPSC, #1915. [2] Cohen (1999), Adv. Geophys., 41, 133-231. [3] Schultz and Lin (2001), JGR, 106, 16549-16566. [4] Lin and Stein (2004), JGR, 109, B02303, doi:10.1029/2003JB002607. [5] Toda et al. (2005), JGR, 103, 24543-24565. [6] Okubo and Schultz (2004), GSAB, 116, 597-605. [7] Watters (2004), Icarus, 171, 284-294. [8] Schultz (2000), JGR, 105, 12035-12052.
Analysis of uncertainties in Monte Carlo simulated organ dose for chest CT

NASA Astrophysics Data System (ADS)

Muryn, John S.; Morgan, Ashraf G.; Segars, W. P.; Liptak, Chris L.; Dong, Frank F.; Primak, Andrew N.; Li, Xiang

2015-03-01

In Monte Carlo simulation of organ dose for a chest CT scan, many input parameters are required (e.g., half-value layer of the x-ray energy spectrum, effective beam width, and anatomical coverage of the scan). The input parameter values are provided by the manufacturer, measured experimentally, or determined based on typical clinical practices. The goal of this study was to assess the uncertainties in Monte Carlo simulated organ dose as a result of using input parameter values that deviate from the truth (clinical reality). Organ dose from a chest CT scan was simulated for a standard-size female phantom using a set of reference input parameter values (treated as the truth). To emulate the situation in which the input parameter values used by the researcher may deviate from the truth, additional simulations were performed in which errors were purposefully introduced into the input parameter values, the effects of which on organ dose per CTDIvol were analyzed. Our study showed that when errors in half value layer were within ± 0.5 mm Al, the errors in organ dose per CTDIvol were less than 6%. Errors in effective beam width of up to 3 mm had negligible effect (< 2.5%) on organ dose. In contrast, when the assumed anatomical center of the patient deviated from the true anatomical center by 5 cm, organ dose errors of up to 20% were introduced. Lastly, when the assumed extra scan length was longer by 4 cm than the true value, dose errors of up to 160% were found. The results answer the important question: to what level of accuracy each input parameter needs to be determined in order to obtain accurate organ dose results.
Automatic Adaptation to Fast Input Changes in a Time-Invariant Neural Circuit

PubMed Central

Bharioke, Arjun; Chklovskii, Dmitri B.

2015-01-01

Neurons must faithfully encode signals that can vary over many orders of magnitude despite having only limited dynamic ranges. For a correlated signal, this dynamic range constraint can be relieved by subtracting away components of the signal that can be predicted from the past, a strategy known as predictive coding, that relies on learning the input statistics. However, the statistics of input natural signals can also vary over very short time scales e.g., following saccades across a visual scene. To maintain a reduced transmission cost to signals with rapidly varying statistics, neuronal circuits implementing predictive coding must also rapidly adapt their properties. Experimentally, in different sensory modalities, sensory neurons have shown such adaptations within 100 ms of an input change. Here, we show first that linear neurons connected in a feedback inhibitory circuit can implement predictive coding. We then show that adding a rectification nonlinearity to such a feedback inhibitory circuit allows it to automatically adapt and approximate the performance of an optimal linear predictive coding network, over a wide range of inputs, while keeping its underlying temporal and synaptic properties unchanged. We demonstrate that the resulting changes to the linearized temporal filters of this nonlinear network match the fast adaptations observed experimentally in different sensory modalities, in different vertebrate species. Therefore, the nonlinear feedback inhibitory network can provide automatic adaptation to fast varying signals, maintaining the dynamic range necessary for accurate neuronal transmission of natural inputs. PMID:26247884
Evaluation of severe accident risks: Quantification of major input parameters: MAACS (MELCOR Accident Consequence Code System) input

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

Sprung, J.L.; Jow, H-N; Rollstin, J.A.

1990-12-01

Estimation of offsite accident consequences is the customary final step in a probabilistic assessment of the risks of severe nuclear reactor accidents. Recently, the Nuclear Regulatory Commission reassessed the risks of severe accidents at five US power reactors (NUREG-1150). Offsite accident consequences for NUREG-1150 source terms were estimated using the MELCOR Accident Consequence Code System (MACCS). Before these calculations were performed, most MACCS input parameters were reviewed, and for each parameter reviewed, a best-estimate value was recommended. This report presents the results of these reviews. Specifically, recommended values and the basis for their selection are presented for MACCS atmospheric andmore » biospheric transport, emergency response, food pathway, and economic input parameters. Dose conversion factors and health effect parameters are not reviewed in this report. 134 refs., 15 figs., 110 tabs.« less
A third-order class-D amplifier with and without ripple compensation

NASA Astrophysics Data System (ADS)

Cox, Stephen M.; du Toit Mouton, H.

2018-06-01

We analyse the nonlinear behaviour of a third-order class-D amplifier, and demonstrate the remarkable effectiveness of the recently introduced ripple compensation (RC) technique in reducing the audio distortion of the device. The amplifier converts an input audio signal to a high-frequency train of rectangular pulses, whose widths are modulated according to the input signal (pulse-width modulation) and employs negative feedback. After determining the steady-state operating point for constant input and calculating its stability, we derive a small-signal model (SSM), which yields in closed form the transfer function relating (infinitesimal) input and output disturbances. This SSM shows how the RC technique is able to linearise the small-signal response of the device. We extend this SSM through a fully nonlinear perturbation calculation of the dynamics of the amplifier, based on the disparity in time scales between the pulse train and the audio signal. We obtain the nonlinear response of the amplifier to a general audio signal, avoiding the linearisation inherent in the SSM; we thereby more precisely quantify the reduction in distortion achieved through RC. Finally, simulations corroborate our theoretical predictions and illustrate the dramatic deterioration in performance that occurs when the amplifier is operated in an unstable regime. The perturbation calculation is rather general, and may be adapted to quantify the way in which other nonlinear negative-feedback pulse-modulated devices track a time-varying input signal that slowly modulates the system parameters.
Time-varying output performances of piezoelectric vibration energy harvesting under nonstationary random vibrations

NASA Astrophysics Data System (ADS)

Yoon, Heonjun; Kim, Miso; Park, Choon-Su; Youn, Byeng D.

2018-01-01

Piezoelectric vibration energy harvesting (PVEH) has received much attention as a potential solution that could ultimately realize self-powered wireless sensor networks. Since most ambient vibrations in nature are inherently random and nonstationary, the output performances of PVEH devices also randomly change with time. However, little attention has been paid to investigating the randomly time-varying electroelastic behaviors of PVEH systems both analytically and experimentally. The objective of this study is thus to make a step forward towards a deep understanding of the time-varying performances of PVEH devices under nonstationary random vibrations. Two typical cases of nonstationary random vibration signals are considered: (1) randomly-varying amplitude (amplitude modulation; AM) and (2) randomly-varying amplitude with randomly-varying instantaneous frequency (amplitude and frequency modulation; AM-FM). In both cases, this study pursues well-balanced correlations of analytical predictions and experimental observations to deduce the relationships between the time-varying output performances of the PVEH device and two primary input parameters, such as a central frequency and an external electrical resistance. We introduce three correlation metrics to quantitatively compare analytical prediction and experimental observation, including the normalized root mean square error, the correlation coefficient, and the weighted integrated factor. Analytical predictions are in an excellent agreement with experimental observations both mechanically and electrically. This study provides insightful guidelines for designing PVEH devices to reliably generate electric power under nonstationary random vibrations.
Using model order tests to determine sensory inputs in a motion study

NASA Technical Reports Server (NTRS)

Repperger, D. W.; Junker, A. M.

1977-01-01

In the study of motion effects on tracking performance, a problem of interest is the determination of what sensory inputs a human uses in controlling his tracking task. In the approach presented here a simple canonical model (FID or a proportional, integral, derivative structure) is used to model the human's input-output time series. A study of significant changes in reduction of the output error loss functional is conducted as different permutations of parameters are considered. Since this canonical model includes parameters which are related to inputs to the human (such as the error signal, its derivatives and integration), the study of model order is equivalent to the study of which sensory inputs are being used by the tracker. The parameters are obtained which have the greatest effect on reducing the loss function significantly. In this manner the identification procedure converts the problem of testing for model order into the problem of determining sensory inputs.
Uncertainty Quantification of Equilibrium Climate Sensitivity in CCSM4

NASA Astrophysics Data System (ADS)

Covey, C. C.; Lucas, D. D.; Tannahill, J.; Klein, R.

2013-12-01

Uncertainty in the global mean equilibrium surface warming due to doubled atmospheric CO2, as computed by a "slab ocean" configuration of the Community Climate System Model version 4 (CCSM4), is quantified using 1,039 perturbed-input-parameter simulations. The slab ocean configuration reduces the model's e-folding time when approaching an equilibrium state to ~5 years. This time is much less than for the full ocean configuration, consistent with the shallow depth of the upper well-mixed layer of the ocean represented by the "slab." Adoption of the slab ocean configuration requires the assumption of preset values for the convergence of ocean heat transport beneath the upper well-mixed layer. A standard procedure for choosing these values maximizes agreement with the full ocean version's simulation of the present-day climate when input parameters assume their default values. For each new set of input parameter values, we computed the change in ocean heat transport implied by a "Phase 1" model run in which sea surface temperatures and sea ice concentrations were set equal to present-day values. The resulting total ocean heat transport (= standard value + change implied by Phase 1 run) was then input into "Phase 2" slab ocean runs with varying values of atmospheric CO2. Our uncertainty estimate is based on Latin Hypercube sampling over expert-provided uncertainty ranges of N = 36 adjustable parameters in the atmosphere (CAM4) and sea ice (CICE4) components of CCSM4. Two-dimensional projections of our sampling distribution for the N(N-1)/2 possible pairs of input parameters indicate full coverage of the N-dimensional parameter space, including edges. We used a machine learning-based support vector regression (SVR) statistical model to estimate the probability density function (PDF) of equilibrium warming. This fitting procedure produces a PDF that is qualitatively consistent with the raw histogram of our CCSM4 results. Most of the values from the SVR statistical model are within ~0.1 K of the raw results, well below the inter-decile range inferred below. Independent validation of the fit indicates residual errors that are distributed about zero with a standard deviation of 0.17 K. Analysis of variance shows that the equilibrium warming in CCSM4 is mainly linear in parameter changes. Thus, in accord with the Central Limit Theorem of statistics, the PDF of the warming is approximately Gaussian, i.e. symmetric about its mean value (3.0 K). Since SVR allows for highly nonlinear fits, the symmetry is not an artifact of the fitting procedure. The 10-90 percentile range of the PDF is 2.6-3.4 K, consistent with earlier estimates from CCSM4 but narrower than estimates from other models, which sometimes produce a high-temperature asymmetric tail in the PDF. This work was performed under auspices of the US Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, and was funded by LLNL's Uncertainty Quantification Strategic Initiative (Laboratory Directed Research and Development Project 10-SI-013).
Probabilistic seismic hazard study based on active fault and finite element geodynamic models

NASA Astrophysics Data System (ADS)

Kastelic, Vanja; Carafa, Michele M. C.; Visini, Francesco

2016-04-01

We present a probabilistic seismic hazard analysis (PSHA) that is exclusively based on active faults and geodynamic finite element input models whereas seismic catalogues were used only in a posterior comparison. We applied the developed model in the External Dinarides, a slow deforming thrust-and-fold belt at the contact between Adria and Eurasia.. is the Our method consists of establishing s two earthquake rupture forecast models: (i) a geological active fault input (GEO) model and, (ii) a finite element (FEM) model. The GEO model is based on active fault database that provides information on fault location and its geometric and kinematic parameters together with estimations on its slip rate. By default in this model all deformation is set to be released along the active faults. The FEM model is based on a numerical geodynamic model developed for the region of study. In this model the deformation is, besides along the active faults, released also in the volumetric continuum elements. From both models we calculated their corresponding activity rates, its earthquake rates and their final expected peak ground accelerations. We investigated both the source model and the earthquake model uncertainties by varying the main active fault and earthquake rate calculation parameters through constructing corresponding branches of the seismic hazard logic tree. Hazard maps and UHS curves have been produced for horizontal ground motion on bedrock conditions VS 30 ≥ 800 m/s), thereby not considering local site amplification effects. The hazard was computed over a 0.2° spaced grid considering 648 branches of the logic tree and the mean value of 10% probability of exceedance in 50 years hazard level, while the 5th and 95th percentiles were also computed to investigate the model limits. We conducted a sensitivity analysis to control which of the input parameters influence the final hazard results in which measure. The results of such comparison evidence the deformation model and with their internal variability together with the choice of the ground motion prediction equations (GMPEs) are the most influencing parameter. Both of these parameters have significan affect on the hazard results. Thus having good knowledge of the existence of active faults and their geometric and activity characteristics is of key importance. We also show that PSHA models based exclusively on active faults and geodynamic inputs, which are thus not dependent on past earthquake occurrences, provide a valid method for seismic hazard calculation.

Modal Parameter Identification of a Flexible Arm System

NASA Technical Reports Server (NTRS)

Barrington, Jason; Lew, Jiann-Shiun; Korbieh, Edward; Wade, Montanez; Tantaris, Richard

1998-01-01

In this paper an experiment is designed for the modal parameter identification of a flexible arm system. This experiment uses a function generator to provide input signal and an oscilloscope to save input and output response data. For each vibrational mode, many sets of sine-wave inputs with frequencies close to the natural frequency of the arm system are used to excite the vibration of this mode. Then a least-squares technique is used to analyze the experimental input/output data to obtain the identified parameters for this mode. The identified results are compared with the analytical model obtained by applying finite element analysis.
Effects of molecular elongation on liquid crystalline phase behaviour: isotropic-nematic transition

NASA Astrophysics Data System (ADS)

Singh, Ram Chandra; Ram, Jokhan

2003-08-01

We present the density-functional approach to study the isotropic-nematic transitions and calculate the values of freezing parameters of the Gay-Berne liquid crystal model, concentrating on the effects of varying the molecular elongation, x0. For this, we have solved the Percus-Yevick integral equation theory to calculate the pair-correlation functions of a fluid the molecules of which interact via a Gay-Berne pair potential. These results have been used in the density-functional theory as an input to locate the isotropic-nematic transition and calculate freezing parameters for a range of length-to-width parameters 3.0⩽ x0⩽4.0 at reduced temperatures 0.95 and 1.25. We observed that as x0 is increased, the isotropic-nematic transition is seen to move to lower density at a given temperature. We find that the density-functional theory is good to study the freezing transitions in such fluids. We have also compared our results with computer simulation results wherever they are available.
Adjustment of Part Properties for an Elastomeric Laser Sintering Material

NASA Astrophysics Data System (ADS)

Wegner, A.; Ünlü, T.

2018-03-01

Laser sintering of polymers is gaining more and more importance within the field of small series productions. Polyamide 12 is predominantly used, although a variety of other materials are also available for the laser sintering process. For example, elastomeric, rubberlike materials offer very different part property profiles. Those make the production of flexible parts like, e.g., sealings, flexible tubes or shoe soles possible because they offer high part ductility and low hardness. At the chair for manufacturing technology, a new elastomeric laser sintering material has been developed and then commercialized by a spin-off from university. The aim of the presented study was the analysis of the new material's properties. Proof was found that Shore hardness can be modified by varying the parameter settings. Therefore, the correlation between process parameters, energy input, Shore hardness and other part properties like mechanical properties were analyzed. Based on these results, suitable parameter settings were established which lead to the possibility of producing parts with different Shore hardnesses.
Certification Testing Methodology for Composite Structure. Volume 2. Methodology Development

DTIC Science & Technology

1986-10-01

parameter, sample size and fa- tigue test duration. The required input are 1. Residual strength Weibull shape parameter ( ALPR ) 2. Fatigue life Weibull shape...INPUT STRENGTH ALPHA’) READ(*,*) ALPR ALPRI = 1.O/ ALPR WRITE(*, 2) 2 FORMAT( 2X, ’PLEASE INPUT LIFE ALPHA’) READ(*,*) ALPL ALPLI - 1.0/ALPL WRITE(*, 3...3 FORMAT(2X,’PLEASE INPUT SAMPLE SIZE’) READ(*,*) N AN - N WRITE(*,4) 4 FORMAT(2X,’PLEASE INPUT TEST DURATION’) READ(*,*) T RALP - ALPL/ ALPR ARGR - 1
User's manual for a parameter identification technique. [with options for model simulation for fixed input forcing functions and identification from wind tunnel and flight measurements

NASA Technical Reports Server (NTRS)

Kanning, G.

1975-01-01

A digital computer program written in FORTRAN is presented that implements the system identification theory for deterministic systems using input-output measurements. The user supplies programs simulating the mathematical model of the physical plant whose parameters are to be identified. The user may choose any one of three options. The first option allows for a complete model simulation for fixed input forcing functions. The second option identifies up to 36 parameters of the model from wind tunnel or flight measurements. The third option performs a sensitivity analysis for up to 36 parameters. The use of each option is illustrated with an example using input-output measurements for a helicopter rotor tested in a wind tunnel.
Dynamic sensitivity analysis of biological systems

PubMed Central

Wu, Wu Hsiung; Wang, Feng Sheng; Chang, Maw Shang

2008-01-01

Background A mathematical model to understand, predict, control, or even design a real biological system is a central theme in systems biology. A dynamic biological system is always modeled as a nonlinear ordinary differential equation (ODE) system. How to simulate the dynamic behavior and dynamic parameter sensitivities of systems described by ODEs efficiently and accurately is a critical job. In many practical applications, e.g., the fed-batch fermentation systems, the system admissible input (corresponding to independent variables of the system) can be time-dependent. The main difficulty for investigating the dynamic log gains of these systems is the infinite dimension due to the time-dependent input. The classical dynamic sensitivity analysis does not take into account this case for the dynamic log gains. Results We present an algorithm with an adaptive step size control that can be used for computing the solution and dynamic sensitivities of an autonomous ODE system simultaneously. Although our algorithm is one of the decouple direct methods in computing dynamic sensitivities of an ODE system, the step size determined by model equations can be used on the computations of the time profile and dynamic sensitivities with moderate accuracy even when sensitivity equations are more stiff than model equations. To show this algorithm can perform the dynamic sensitivity analysis on very stiff ODE systems with moderate accuracy, it is implemented and applied to two sets of chemical reactions: pyrolysis of ethane and oxidation of formaldehyde. The accuracy of this algorithm is demonstrated by comparing the dynamic parameter sensitivities obtained from this new algorithm and from the direct method with Rosenbrock stiff integrator based on the indirect method. The same dynamic sensitivity analysis was performed on an ethanol fed-batch fermentation system with a time-varying feed rate to evaluate the applicability of the algorithm to realistic models with time-dependent admissible input. Conclusion By combining the accuracy we show with the efficiency of being a decouple direct method, our algorithm is an excellent method for computing dynamic parameter sensitivities in stiff problems. We extend the scope of classical dynamic sensitivity analysis to the investigation of dynamic log gains of models with time-dependent admissible input. PMID:19091016
Active subspace uncertainty quantification for a polydomain ferroelectric phase-field model

NASA Astrophysics Data System (ADS)

Leon, Lider S.; Smith, Ralph C.; Miles, Paul; Oates, William S.

2018-03-01

Quantum-informed ferroelectric phase field models capable of predicting material behavior, are necessary for facilitating the development and production of many adaptive structures and intelligent systems. Uncertainty is present in these models, given the quantum scale at which calculations take place. A necessary analysis is to determine how the uncertainty in the response can be attributed to the uncertainty in the model inputs or parameters. A second analysis is to identify active subspaces within the original parameter space, which quantify directions in which the model response varies most dominantly, thus reducing sampling effort and computational cost. In this investigation, we identify an active subspace for a poly-domain ferroelectric phase-field model. Using the active variables as our independent variables, we then construct a surrogate model and perform Bayesian inference. Once we quantify the uncertainties in the active variables, we obtain uncertainties for the original parameters via an inverse mapping. The analysis provides insight into how active subspace methodologies can be used to reduce computational power needed to perform Bayesian inference on model parameters informed by experimental or simulated data.
Multi-Response Optimization of WEDM Process Parameters Using Taguchi Based Desirability Function Analysis

NASA Astrophysics Data System (ADS)

Majumder, Himadri; Maity, Kalipada

2018-03-01

Shape memory alloy has a unique capability to return to its original shape after physical deformation by applying heat or thermo-mechanical or magnetic load. In this experimental investigation, desirability function analysis (DFA), a multi-attribute decision making was utilized to find out the optimum input parameter setting during wire electrical discharge machining (WEDM) of Ni-Ti shape memory alloy. Four critical machining parameters, namely pulse on time (TON), pulse off time (TOFF), wire feed (WF) and wire tension (WT) were taken as machining inputs for the experiments to optimize three interconnected responses like cutting speed, kerf width, and surface roughness. Input parameter combination TON = 120 μs., TOFF = 55 μs., WF = 3 m/min. and WT = 8 kg-F were found to produce the optimum results. The optimum process parameters for each desired response were also attained using Taguchi’s signal-to-noise ratio. Confirmation test has been done to validate the optimum machining parameter combination which affirmed DFA was a competent approach to select optimum input parameters for the ideal response quality for WEDM of Ni-Ti shape memory alloy.
Rainfall or parameter uncertainty? The power of sensitivity analysis on grouped factors

NASA Astrophysics Data System (ADS)

Nossent, Jiri; Pereira, Fernando; Bauwens, Willy

2017-04-01

Hydrological models are typically used to study and represent (a part of) the hydrological cycle. In general, the output of these models mostly depends on their input rainfall and parameter values. Both model parameters and input precipitation however, are characterized by uncertainties and, therefore, lead to uncertainty on the model output. Sensitivity analysis (SA) allows to assess and compare the importance of the different factors for this output uncertainty. Hereto, the rainfall uncertainty can be incorporated in the SA by representing it as a probabilistic multiplier. Such multiplier can be defined for the entire time series, or several of these factors can be determined for every recorded rainfall pulse or for hydrological independent storm events. As a consequence, the number of parameters included in the SA related to the rainfall uncertainty can be (much) lower or (much) higher than the number of model parameters. Although such analyses can yield interesting results, it remains challenging to determine which type of uncertainty will affect the model output most due to the different weight both types will have within the SA. In this study, we apply the variance based Sobol' sensitivity analysis method to two different hydrological simulators (NAM and HyMod) for four diverse watersheds. Besides the different number of model parameters (NAM: 11 parameters; HyMod: 5 parameters), the setup of our sensitivity and uncertainty analysis-combination is also varied by defining a variety of scenarios including diverse numbers of rainfall multipliers. To overcome the issue of the different number of factors and, thus, the different weights of the two types of uncertainty, we build on one of the advantageous properties of the Sobol' SA, i.e. treating grouped parameters as a single parameter. The latter results in a setup with a single factor for each uncertainty type and allows for a straightforward comparison of their importance. In general, the results show a clear influence of the weights in the different SA scenarios. However, working with grouped factors resolves this issue and leads to clear importance results.
Modeling Streamflow and Water Temperature in the North Santiam and Santiam Rivers, Oregon, 2001-02

USGS Publications Warehouse

Sullivan, Annett B.; Roundsk, Stewart A.

2004-01-01

To support the development of a total maximum daily load (TMDL) for water temperature in the Willamette Basin, the laterally averaged, two-dimensional model CE-QUAL-W2 was used to construct a water temperature and streamflow model of the Santiam and North Santiam Rivers. The rivers were simulated from downstream of Detroit and Big Cliff dams to the confluence with the Willamette River. Inputs to the model included bathymetric data, flow and temperature from dam releases, tributary flow and temperature, and meteorologic data. The model was calibrated for the period July 1 through November 21, 2001, and confirmed with data from April 1 through October 31, 2002. Flow calibration made use of data from two streamflow gages and travel-time and river-width data. Temperature calibration used data from 16 temperature monitoring locations in 2001 and 5 locations in 2002. A sensitivity analysis was completed by independently varying input parameters, including point-source flow, air temperature, flow and water temperature from dam releases, and riparian shading. Scenario analyses considered hypothetical river conditions without anthropogenic heat inputs, with restored riparian vegetation, with minimum streamflow from the dams, and with a more-natural seasonal water temperature regime from dam releases.
Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor.

PubMed

Kamesh, Reddi; Rani, K Yamuna

2016-09-01

A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
Potential Flow Theory and Operation Guide for the Panel Code PMARC. Version 14

NASA Technical Reports Server (NTRS)

Ashby, Dale L.

1999-01-01

The theoretical basis for PMARC, a low-order panel code for modeling complex three-dimensional bodies, in potential flow, is outlined. PMARC can be run on a wide variety of computer platforms, including desktop machines, workstations, and supercomputers. Execution times for PMARC vary tremendously depending on the computer resources used, but typically range from several minutes for simple or moderately complex cases to several hours for very large complex cases. Several of the advanced features currently included in the code, such as internal flow modeling, boundary layer analysis, and time-dependent flow analysis, including problems involving relative motion, are discussed in some detail. The code is written in Fortran77, using adjustable-size arrays so that it can be easily redimensioned to match problem requirements and computer hardware constraints. An overview of the program input is presented. A detailed description of the input parameters is provided in the appendices. PMARC results for several test cases are presented along with analytic or experimental data, where available. The input files for these test cases are given in the appendices. PMARC currently supports plotfile output formats for several commercially available graphics packages. The supported graphics packages are Plot3D, Tecplot, and PmarcViewer.
Hybrid Simulation Modeling to Estimate U.S. Energy Elasticities

NASA Astrophysics Data System (ADS)

Baylin-Stern, Adam C.

This paper demonstrates how an U.S. application of CIMS, a technologically explicit and behaviourally realistic energy-economy simulation model which includes macro-economic feedbacks, can be used to derive estimates of elasticity of substitution (ESUB) and autonomous energy efficiency index (AEEI) parameters. The ability of economies to reduce greenhouse gas emissions depends on the potential for households and industry to decrease overall energy usage, and move from higher to lower emissions fuels. Energy economists commonly refer to ESUB estimates to understand the degree of responsiveness of various sectors of an economy, and use estimates to inform computable general equilibrium models used to study climate policies. Using CIMS, I have generated a set of future, 'pseudo-data' based on a series of simulations in which I vary energy and capital input prices over a wide range. I then used this data set to estimate the parameters for transcendental logarithmic production functions using regression techniques. From the production function parameter estimates, I calculated an array of elasticity of substitution values between input pairs. Additionally, this paper demonstrates how CIMS can be used to calculate price-independent changes in energy-efficiency in the form of the AEEI, by comparing energy consumption between technologically frozen and 'business as usual' simulations. The paper concludes with some ideas for model and methodological improvement, and how these might figure into future work in the estimation of ESUBs from CIMS. Keywords: Elasticity of substitution; hybrid energy-economy model; translog; autonomous energy efficiency index; rebound effect; fuel switching.
An economic evaluation of maxillary implant overdentures based on six vs. four implants.

PubMed

Listl, Stefan; Fischer, Leonhard; Giannakopoulos, Nikolaos Nikitas

2014-08-18

The purpose of the present study was to assess the value for money achieved by bar-retained implant overdentures based on six implants compared with four implants as treatment alternatives for the edentulous maxilla. A Markov decision tree model was constructed and populated with parameter estimates for implant and denture failure as well as patient-centred health outcomes as available from recent literature. The decision scenario was modelled within a ten year time horizon and relied on cost reimbursement regulations of the German health care system. The cost-effectiveness threshold was identified above which the six-implant solution is preferable over the four-implant solution. Uncertainties regarding input parameters were incorporated via one-way and probabilistic sensitivity analysis based on Monte-Carlo simulation. Within a base case scenario of average treatment complexity, the cost-effectiveness threshold was identified to be 17,564 € per year of denture satisfaction gained above of which the alternative with six implants is preferable over treatment including four implants. Sensitivity analysis yielded that, depending on the specification of model input parameters such as patients' denture satisfaction, the respective cost-effectiveness threshold varies substantially. The results of the present study suggest that bar-retained maxillary overdentures based on six implants provide better patient satisfaction than bar-retained overdentures based on four implants but are considerably more expensive. Final judgements about value for money require more comprehensive clinical evidence including patient-centred health outcomes.
An economic evaluation of maxillary implant overdentures based on six vs. four implants

PubMed Central

2014-01-01

Background The purpose of the present study was to assess the value for money achieved by bar-retained implant overdentures based on six implants compared with four implants as treatment alternatives for the edentulous maxilla. Methods A Markov decision tree model was constructed and populated with parameter estimates for implant and denture failure as well as patient-centred health outcomes as available from recent literature. The decision scenario was modelled within a ten year time horizon and relied on cost reimbursement regulations of the German health care system. The cost-effectiveness threshold was identified above which the six-implant solution is preferable over the four-implant solution. Uncertainties regarding input parameters were incorporated via one-way and probabilistic sensitivity analysis based on Monte-Carlo simulation. Results Within a base case scenario of average treatment complexity, the cost-effectiveness threshold was identified to be 17,564 € per year of denture satisfaction gained above of which the alternative with six implants is preferable over treatment including four implants. Sensitivity analysis yielded that, depending on the specification of model input parameters such as patients’ denture satisfaction, the respective cost-effectiveness threshold varies substantially. Conclusions The results of the present study suggest that bar-retained maxillary overdentures based on six implants provide better patient satisfaction than bar-retained overdentures based on four implants but are considerably more expensive. Final judgements about value for money require more comprehensive clinical evidence including patient-centred health outcomes. PMID:25135370
A comparative study of radiofrequency antennas for Helicon plasma sources

NASA Astrophysics Data System (ADS)

Melazzi, D.; Lancellotti, V.

2015-04-01

Since Helicon plasma sources can efficiently couple power and generate high-density plasma, they have received interest also as spacecraft propulsive devices, among other applications. In order to maximize the power deposited into the plasma, it is necessary to assess the performance of the radiofrequency (RF) antenna that drives the discharge, as typical plasma parameters (e.g. the density) are varied. For this reason, we have conducted a comparative analysis of three Helicon sources which feature different RF antennas, namely, the single-loop, the Nagoya type-III and the fractional helix. These antennas are compared in terms of input impedance and induced current density; in particular, the real part of the impedance constitutes a measure of the antenna ability to couple power into the plasma. The results presented in this work have been obtained through a full-wave approach which (being hinged on the numerical solution of a system of integral equations) allows computing the antenna current and impedance self-consistently. Our findings indicate that certain combinations of plasma parameters can indeed maximize the real part of the input impedance and, thus, the deposited power, and that one of the three antennas analyzed performs best for a given plasma. Furthermore, unlike other strategies which rely on approximate antenna models, our approach enables us to reveal that the antenna current density is not spatially uniform, and that a correlation exists between the plasma parameters and the spatial distribution of the current density.
Characterization of Modified Tapioca Starch Solutions and Their Sprays for High Temperature Coating Applications

PubMed Central

Naz, M. Y.; Sulaiman, S. A.; Ariwahjoedi, B.; Shaari, Ku Zilati Ku

2014-01-01

The objective of the research was to understand and improve the unusual physical and atomization properties of the complexes/adhesives derived from the tapioca starch by addition of borate and urea. The characterization of physical properties of the synthesized adhesives was carried out by determining the effect of temperature, shear rate, and mass concentration of thickener/stabilizer on the complex viscosity, density, and surface tension. In later stage, phenomenological analyses of spray jet breakup of heated complexes were performed in still air. Using a high speed digital camera, the jet breakup dynamics were visualized as a function of the system input parameters. The further analysis of the grabbed images confirmed the strong influence of the input processing parameters on full cone spray patternation. It was also predicted that the heated starch adhesive solutions generate a dispersed spray pattern by utilizing the partial evaporation of the spraying medium. Below 40°C of heating temperature, the radial spray cone width and angle did not vary significantly with increasing Reynolds and Weber numbers at early injection phases leading to increased macroscopic spray propagation. The discharge coefficient, mean flow rate, and mean flow velocity were significantly influenced by the load pressure but less affected by the temperature. PMID:24592165
Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network - A novel structure.

PubMed

El-Nagar, Ahmad M

2018-01-01

In this study, a novel structure of a recurrent interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (FNN) is introduced for nonlinear dynamic and time-varying systems identification. It combines the type-2 fuzzy sets (T2FSs) and a recurrent FNN to avoid the data uncertainties. The fuzzy firing strengths in the proposed structure are returned to the network input as internal variables. The interval type-2 fuzzy sets (IT2FSs) is used to describe the antecedent part for each rule while the consequent part is a TSK-type, which is a linear function of the internal variables and the external inputs with interval weights. All the type-2 fuzzy rules for the proposed RIT2TSKFNN are learned on-line based on structure and parameter learning, which are performed using the type-2 fuzzy clustering. The antecedent and consequent parameters of the proposed RIT2TSKFNN are updated based on the Lyapunov function to achieve network stability. The obtained results indicate that our proposed network has a small root mean square error (RMSE) and a small integral of square error (ISE) with a small number of rules and a small computation time compared with other type-2 FNNs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Hepatic transporter drug-drug interactions: an evaluation of approaches and methodologies.

PubMed

Williamson, Beth; Riley, Robert J

2017-12-01

Drug-drug interactions (DDIs) continue to account for 5% of hospital admissions and therefore remain a major regulatory concern. Effective, quantitative prediction of DDIs will reduce unexpected clinical findings and encourage projects to frontload DDI investigations rather than concentrating on risk management ('manage the baggage') later in drug development. A key challenge in DDI prediction is the discrepancies between reported models. Areas covered: The current synopsis focuses on four recent influential publications on hepatic drug transporter DDIs using static models that tackle interactions with individual transporters and in combination with other drug transporters and metabolising enzymes. These models vary in their assumptions (including input parameters), transparency, reproducibility and complexity. In this review, these facets are compared and contrasted with recommendations made as to their application. Expert opinion: Over the past decade, static models have evolved from simple [I]/k i models to incorporate victim and perpetrator disposition mechanisms including the absorption rate constant, the fraction of the drug metabolised/eliminated and/or clearance concepts. Nonetheless, models that comprise additional parameters and complexity do not necessarily out-perform simpler models with fewer inputs. Further, consideration of the property space to exploit some drug target classes has also highlighted the fine balance required between frontloading and back-loading studies to design out or 'manage the baggage'.
Impregnation of Composite Materials: a Numerical Study

NASA Astrophysics Data System (ADS)

Baché, Elliott; Dupleix-Couderc, Chloé; Arquis, Eric; Berdoyes, Isabelle

2017-12-01

Oxide ceramic matrix composites are currently being developed for aerospace applications such as the exhaust, where the parts are subject to moderately high temperatures (≈ 700 ∘C) and oxidation. These composite materials are normally formed by, among other steps, impregnating a ceramic fabric with a slurry of ceramic particles. This impregnation process can be complex, with voids possibly forming in the fabric depending on the process parameters and material properties. Unwanted voids or macroporosity within the fabric can decrease the mechanical properties of the parts. In order to design an efficient manufacturing process able to impregnate the fabric well, numerical simulations may be used to design the process as well as the slurry. In this context, a tool is created for modeling different processes. Thétis, which solves the Navier-Stokes-Darcy-Brinkman equation using finite volumes, is expanded to take into account capillary pressures on the mesoscale. This formulation allows for more representativity than for Darcy's law (homogeneous preform) simulations while avoiding the prohibitive simulation times of a full discretization for the composing fibers at the representative elementary volume scale. The resulting tool is first used to investigate the effect of varying the slurry parameters on impregnation evolution. Two different processes, open bath impregnation and wet lay-up, are then studied with emphasis on varying their input parameters (e.g. inlet velocity).

Identification of gene regulation models from single-cell data

NASA Astrophysics Data System (ADS)

Weber, Lisa; Raymond, William; Munsky, Brian

2018-09-01

In quantitative analyses of biological processes, one may use many different scales of models (e.g. spatial or non-spatial, deterministic or stochastic, time-varying or at steady-state) or many different approaches to match models to experimental data (e.g. model fitting or parameter uncertainty/sloppiness quantification with different experiment designs). These different analyses can lead to surprisingly different results, even when applied to the same data and the same model. We use a simplified gene regulation model to illustrate many of these concerns, especially for ODE analyses of deterministic processes, chemical master equation and finite state projection analyses of heterogeneous processes, and stochastic simulations. For each analysis, we employ MATLAB and PYTHON software to consider a time-dependent input signal (e.g. a kinase nuclear translocation) and several model hypotheses, along with simulated single-cell data. We illustrate different approaches (e.g. deterministic and stochastic) to identify the mechanisms and parameters of the same model from the same simulated data. For each approach, we explore how uncertainty in parameter space varies with respect to the chosen analysis approach or specific experiment design. We conclude with a discussion of how our simulated results relate to the integration of experimental and computational investigations to explore signal-activated gene expression models in yeast (Neuert et al 2013 Science 339 584–7) and human cells (Senecal et al 2014 Cell Rep. 8 75–83)5.
An optimized time varying filtering based empirical mode decomposition method with grey wolf optimizer for machinery fault diagnosis

NASA Astrophysics Data System (ADS)

Zhang, Xin; Liu, Zhiwen; Miao, Qiang; Wang, Lei

2018-03-01

A time varying filtering based empirical mode decomposition (EMD) (TVF-EMD) method was proposed recently to solve the mode mixing problem of EMD method. Compared with the classical EMD, TVF-EMD was proven to improve the frequency separation performance and be robust to noise interference. However, the decomposition parameters (i.e., bandwidth threshold and B-spline order) significantly affect the decomposition results of this method. In original TVF-EMD method, the parameter values are assigned in advance, which makes it difficult to achieve satisfactory analysis results. To solve this problem, this paper develops an optimized TVF-EMD method based on grey wolf optimizer (GWO) algorithm for fault diagnosis of rotating machinery. Firstly, a measurement index termed weighted kurtosis index is constructed by using kurtosis index and correlation coefficient. Subsequently, the optimal TVF-EMD parameters that match with the input signal can be obtained by GWO algorithm using the maximum weighted kurtosis index as objective function. Finally, fault features can be extracted by analyzing the sensitive intrinsic mode function (IMF) owning the maximum weighted kurtosis index. Simulations and comparisons highlight the performance of TVF-EMD method for signal decomposition, and meanwhile verify the fact that bandwidth threshold and B-spline order are critical to the decomposition results. Two case studies on rotating machinery fault diagnosis demonstrate the effectiveness and advantages of the proposed method.
Finding identifiable parameter combinations in nonlinear ODE models and the rational reparameterization of their input-output equations.

PubMed

Meshkat, Nicolette; Anderson, Chris; Distefano, Joseph J

2011-09-01

When examining the structural identifiability properties of dynamic system models, some parameters can take on an infinite number of values and yet yield identical input-output data. These parameters and the model are then said to be unidentifiable. Finding identifiable combinations of parameters with which to reparameterize the model provides a means for quantitatively analyzing the model and computing solutions in terms of the combinations. In this paper, we revisit and explore the properties of an algorithm for finding identifiable parameter combinations using Gröbner Bases and prove useful theoretical properties of these parameter combinations. We prove a set of M algebraically independent identifiable parameter combinations can be found using this algorithm and that there exists a unique rational reparameterization of the input-output equations over these parameter combinations. We also demonstrate application of the procedure to a nonlinear biomodel. Copyright © 2011 Elsevier Inc. All rights reserved.
Post-Launch Calibration and Testing of Space Weather Instruments on GOES-R Satellite

NASA Technical Reports Server (NTRS)

Tadikonda, S. K.; Merrow, Cynthia S.; Kronenwetter, Jeffrey A.; Comeyne, Gustave J.; Flanagan, Daniel G.; Todrita, Monica

2016-01-01

The Geostationary Operational Environmental Satellite - R (GOES-R) is the first of a series of satellites to be launched, with the first launch scheduled for October 2016. The three instruments Solar UltraViolet Imager (SUVI), Extreme ultraviolet and X-ray Irradiance Sensor (EXIS), and Space Environment In-Situ Suite (SEISS) provide the data needed as inputs for the product updates National Oceanic and Atmospheric Administration (NOAA) provides to the public. SUVI is a full-disk extreme ultraviolet imager enabling Active Region characterization, filament eruption, and flare detection. EXIS provides inputs to solar back-ground-sevents impacting climate models. SEISS provides particle measurements over a wide energy-and-flux range that varies by several orders of magnitude and these data enable updates to spacecraft charge models for electrostatic discharge. EXIS and SEISS have been tested and calibrated end-to-end in ground test facilities around the United States. Due to the complexity of the SUVI design, data from component tests were used in a model to predict on-orbit performance. The ground tests and model updates provided inputs for designing the on-orbit calibration tests. A series of such tests have been planned for the Post-Launch Testing (PLT) of each of these instruments, and specific parameters have been identified that will be updated in the Ground Processing Algorithms, on-orbit parameter tables, or both. Some of SUVI and EXIS calibrations require slewing them off the Sun, while no such maneuvers are needed for SEISS. After a six-month PLT period the GOES-R is expected to be operational. The calibration details are presented in this paper.
Post-Launch Calibration and Testing of Space Weather Instruments on GOES-R Satellite

NASA Technical Reports Server (NTRS)

Tadikonda, Sivakumara S. K.; Merrow, Cynthia S.; Kronenwetter, Jeffrey A.; Comeyne, Gustave J.; Flanagan, Daniel G.; Todirita, Monica

2016-01-01

The Geostationary Operational Environmental Satellite - R (GOES-R) is the first of a series of satellites to be launched, with the first launch scheduled for October 2016. The three instruments - Solar Ultra Violet Imager (SUVI), Extreme ultraviolet and X-ray Irradiance Sensor (EXIS), and Space Environment In-Situ Suite (SEISS) provide the data needed as inputs for the product updates National Oceanic and Atmospheric Administration (NOAA) provides to the public. SUVI is a full-disk extreme ultraviolet imager enabling Active Region characterization, filament eruption, and flare detection. EXIS provides inputs to solar backgrounds/events impacting climate models. SEISS provides particle measurements over a wide energy-and-flux range that varies by several orders of magnitude and these data enable updates to spacecraft charge models for electrostatic discharge. EXIS and SEISS have been tested and calibrated end-to-end in ground test facilities around the United States. Due to the complexity of the SUVI design, data from component tests were used in a model to predict on-orbit performance. The ground tests and model updates provided inputs for designing the on-orbit calibration tests. A series of such tests have been planned for the Post-Launch Testing (PLT) of each of these instruments, and specific parameters have been identified that will be updated in the Ground Processing Algorithms, on-orbit parameter tables, or both. Some of SUVI and EXIS calibrations require slewing them off the Sun, while no such maneuvers are needed for SEISS. After a six-month PLT period the GOES-R is expected to be operational. The calibration details are presented in this paper.
Optimization of a Thermodynamic Model Using a Dakota Toolbox Interface

NASA Astrophysics Data System (ADS)

Cyrus, J.; Jafarov, E. E.; Schaefer, K. M.; Wang, K.; Clow, G. D.; Piper, M.; Overeem, I.

2016-12-01

Scientific modeling of the Earth physical processes is an important driver of modern science. The behavior of these scientific models is governed by a set of input parameters. It is crucial to choose accurate input parameters that will also preserve the corresponding physics being simulated in the model. In order to effectively simulate real world processes the models output data must be close to the observed measurements. To achieve this optimal simulation, input parameters are tuned until we have minimized the objective function, which is the error between the simulation model outputs and the observed measurements. We developed an auxiliary package, which serves as a python interface between the user and DAKOTA. The package makes it easy for the user to conduct parameter space explorations, parameter optimizations, as well as sensitivity analysis while tracking and storing results in a database. The ability to perform these analyses via a Python library also allows the users to combine analysis techniques, for example finding an approximate equilibrium with optimization then immediately explore the space around it. We used the interface to calibrate input parameters for the heat flow model, which is commonly used in permafrost science. We performed optimization on the first three layers of the permafrost model, each with two thermal conductivity coefficients input parameters. Results of parameter space explorations indicate that the objective function not always has a unique minimal value. We found that gradient-based optimization works the best for the objective functions with one minimum. Otherwise, we employ more advanced Dakota methods such as genetic optimization and mesh based convergence in order to find the optimal input parameters. We were able to recover 6 initially unknown thermal conductivity parameters within 2% accuracy of their known values. Our initial tests indicate that the developed interface for the Dakota toolbox could be used to perform analysis and optimization on a `black box' scientific model more efficiently than using just Dakota.
A seasonal Bartlett-Lewis Rectangular Pulse model

NASA Astrophysics Data System (ADS)

Ritschel, Christoph; Agbéko Kpogo-Nuwoklo, Komlan; Rust, Henning; Ulbrich, Uwe; Névir, Peter

2016-04-01

Precipitation time series with a high temporal resolution are needed as input for several hydrological applications, e.g. river runoff or sewer system models. As adequate observational data sets are often not available, simulated precipitation series come to use. Poisson-cluster models are commonly applied to generate these series. It has been shown that this class of stochastic precipitation models is able to well reproduce important characteristics of observed rainfall. For the gauge based case study presented here, the Bartlett-Lewis rectangular pulse model (BLRPM) has been chosen. As it has been shown that certain model parameters vary with season in a midlatitude moderate climate due to different rainfall mechanisms dominating in winter and summer, model parameters are typically estimated separately for individual seasons or individual months. Here, we suggest a simultaneous parameter estimation for the whole year under the assumption that seasonal variation of parameters can be described with harmonic functions. We use an observational precipitation series from Berlin with a high temporal resolution to exemplify the approach. We estimate BLRPM parameters with and without this seasonal extention and compare the results in terms of model performance and robustness of the estimation.
Bayesian nonlinear structural FE model and seismic input identification for damage assessment of civil structures

NASA Astrophysics Data System (ADS)

Astroza, Rodrigo; Ebrahimian, Hamed; Li, Yong; Conte, Joel P.

2017-09-01

A methodology is proposed to update mechanics-based nonlinear finite element (FE) models of civil structures subjected to unknown input excitation. The approach allows to jointly estimate unknown time-invariant model parameters of a nonlinear FE model of the structure and the unknown time histories of input excitations using spatially-sparse output response measurements recorded during an earthquake event. The unscented Kalman filter, which circumvents the computation of FE response sensitivities with respect to the unknown model parameters and unknown input excitations by using a deterministic sampling approach, is employed as the estimation tool. The use of measurement data obtained from arrays of heterogeneous sensors, including accelerometers, displacement sensors, and strain gauges is investigated. Based on the estimated FE model parameters and input excitations, the updated nonlinear FE model can be interrogated to detect, localize, classify, and assess damage in the structure. Numerically simulated response data of a three-dimensional 4-story 2-by-1 bay steel frame structure with six unknown model parameters subjected to unknown bi-directional horizontal seismic excitation, and a three-dimensional 5-story 2-by-1 bay reinforced concrete frame structure with nine unknown model parameters subjected to unknown bi-directional horizontal seismic excitation are used to illustrate and validate the proposed methodology. The results of the validation studies show the excellent performance and robustness of the proposed algorithm to jointly estimate unknown FE model parameters and unknown input excitations.
The response of cortical neurons to in vivo-like input current: theory and experiment: II. Time-varying and spatially distributed inputs.

PubMed

Giugliano, Michele; La Camera, Giancarlo; Fusi, Stefano; Senn, Walter

2008-11-01

The response of a population of neurons to time-varying synaptic inputs can show a rich phenomenology, hardly predictable from the dynamical properties of the membrane's inherent time constants. For example, a network of neurons in a state of spontaneous activity can respond significantly more rapidly than each single neuron taken individually. Under the assumption that the statistics of the synaptic input is the same for a population of similarly behaving neurons (mean field approximation), it is possible to greatly simplify the study of neural circuits, both in the case in which the statistics of the input are stationary (reviewed in La Camera et al. in Biol Cybern, 2008) and in the case in which they are time varying and unevenly distributed over the dendritic tree. Here, we review theoretical and experimental results on the single-neuron properties that are relevant for the dynamical collective behavior of a population of neurons. We focus on the response of integrate-and-fire neurons and real cortical neurons to long-lasting, noisy, in vivo-like stationary inputs and show how the theory can predict the observed rhythmic activity of cultures of neurons. We then show how cortical neurons adapt on multiple time scales in response to input with stationary statistics in vitro. Next, we review how it is possible to study the general response properties of a neural circuit to time-varying inputs by estimating the response of single neurons to noisy sinusoidal currents. Finally, we address the dendrite-soma interactions in cortical neurons leading to gain modulation and spike bursts, and show how these effects can be captured by a two-compartment integrate-and-fire neuron. Most of the experimental results reviewed in this article have been successfully reproduced by simple integrate-and-fire model neurons.
Spatial-Temporal Heterogeneity in Regional Watershed Phosphorus Cycles Driven by Changes in Human Activity over the Past Century

NASA Astrophysics Data System (ADS)

Hale, R. L.; Grimm, N. B.; Vorosmarty, C. J.

2014-12-01

An ongoing challenge for society is to harness the benefits of phosphorus (P) while minimizing negative effects on downstream ecosystems. To meet this challenge we must understand the controls on the delivery of anthropogenic P from landscapes to downstream ecosystems. We used a model that incorporates P inputs to watersheds, hydrology, and infrastructure (sewers, waste-water treatment plants, and reservoirs) to reconstruct historic P yields for the northeastern U.S. from 1930 to 2002. At the regional scale, increases in P inputs were paralleled by increased fractional retention, thus P loading to the coast did not increase significantly. We found that temporal variation in regional P yield was correlated with P inputs. Spatial patterns of watershed P yields were best predicted by inputs, but the correlation between inputs and yields in space weakened over time, due to infrastructure development. Although the magnitude of infrastructure effect was small, its role changed over time and was important in creating spatial and temporal heterogeneity in input-yield relationships. We then conducted a hierarchical cluster analysis to identify a typology of anthropogenic P cycling, using data on P inputs (fertilizer, livestock feed, and human food), infrastructure (dams, wastewater treatment plants, sewers), and hydrology (runoff coefficient). We identified 6 key types of watersheds that varied significantly in climate, infrastructure, and the types and amounts of P inputs. Annual watershed P yields and retention varied significantly across watershed types. Although land cover varied significantly across typologies, clusters based on land cover alone did not explain P budget patterns, suggesting that this variable is insufficient to understand patterns of P cycling across large spatial scales. Furthermore, clusters varied over time as patterns of climate, P use, and infrastructure changed. Our results demonstrate that the drivers of P cycles are spatially and temporally heterogeneous, yet they also suggest that a relatively simple typology of watersheds can be useful for understanding regional P cycles and may help inform P management approaches.
Persistent Organic Pollutants in albacore tuna (Thunnus alalunga) from Reunion Island (Southwest Indian Ocean) and South Africa in relation to biological and trophic characteristics.

PubMed

Munschy, C; Bodin, N; Potier, M; Héas-Moisan, K; Pollono, C; Degroote, M; West, W; Hollanda, S J; Puech, A; Bourjea, J; Nikolic, N

2016-07-01

The contamination of albacore tuna (Thunnus alalunga) by Persistent Organic Pollutants (POPs), namely polychlorinated biphenyls (PCBs) and dichlorodiphenyl-trichloroethane (DDT), was investigated in individuals collected from Reunion Island (RI) and South Africa's (SA) southern coastlines in 2013, in relation to biological parameters and feeding ecology. The results showed lower PCB and DDT concentrations than those previously reported in various tuna species worldwide. A predominance of DDTs over PCBs was revealed, reflecting continuing inputs of DDT. Tuna collected from SA exhibited higher contamination levels than those from RI, related to higher dietary inputs and higher total lipid content. Greater variability in contamination levels and profiles was identified in tuna from RI, explained by a higher diversity of prey and more individualistic foraging behaviour. PCB and DDT contamination levels and profiles varied significantly in tuna from the two investigated areas, probably reflecting exposure to different sources of contamination. Copyright © 2016 Elsevier Inc. All rights reserved.
Modeling maintenance-strategies with rainbow nets

NASA Astrophysics Data System (ADS)

Johnson, Allen M., Jr.; Schoenfelder, Michael A.; Lebold, David

The Rainbow net (RN) modeling technique offers a promising alternative to traditional reliability modeling techniques. RNs are evaluated through discrete event simulation. Using specialized tokens to represent systems and faults, an RN models the fault-handling behavior of an inventory of systems produced over time. In addition, a portion of the RN represents system repair and the vendor's spare part production. Various dependability parameters are measured and used to calculate the impact of four variations of maintenance strategies. Input variables are chosen to demonstrate the technique. The number of inputs allowed to vary is intentionally constrained to limit the volume of data presented and to avoid overloading the reader with complexity. If only availability data were reviewed, it is possible that the conclusion might be drawn that both strategies are about the same and therefore the cheaper strategy from the vendor's perspective may be chosen. The richer set of metrics provided by the RN simulation gives greater insight into the problem, which leads to better decisions. By using RNs, the impact of several different variables is integrated.
Investigation of a Macromechanical Approach to Analyzing Triaxially-Braided Polymer Composites

NASA Technical Reports Server (NTRS)

Goldberg, Robert K.; Blinzler, Brina J.; Binienda, Wieslaw K.

2010-01-01

A macro level finite element-based model has been developed to simulate the mechanical and impact response of triaxially-braided polymer matrix composites. In the analytical model, the triaxial braid architecture is simulated by using four parallel shell elements, each of which is modeled as a laminated composite. The commercial transient dynamic finite element code LS-DYNA is used to conduct the simulations, and a continuum damage mechanics model internal to LS-DYNA is used as the material constitutive model. The material stiffness and strength values required for the constitutive model are determined based on coupon level tests on the braided composite. Simulations of quasi-static coupon tests of a representative braided composite are conducted. Varying the strength values that are input to the material model is found to have a significant influence on the effective material response predicted by the finite element analysis, sometimes in ways that at first glance appear non-intuitive. A parametric study involving the input strength parameters provides guidance on how the analysis model can be improved.
The significance of spatial variability of rainfall on streamflow: A synthetic analysis at the Upper Lee catchment, UK

NASA Astrophysics Data System (ADS)

Pechlivanidis, Ilias; McIntyre, Neil; Wheater, Howard

2017-04-01

Rainfall, one of the main inputs in hydrological modeling, is a highly heterogeneous process over a wide range of scales in space, and hence the ignorance of the spatial rainfall information could affect the simulated streamflow. Calibration of hydrological model parameters is rarely a straightforward task due to parameter equifinality and parameters' 'nature' to compensate for other uncertainties, i.e. structural and forcing input. In here, we analyse the significance of spatial variability of rainfall on streamflow as a function of catchment scale and type, and antecedent conditions using the continuous time, semi-distributed PDM hydrological model at the Upper Lee catchment, UK. The impact of catchment scale and type is assessed using 11 nested catchments ranging in scale from 25 to 1040 km2, and further assessed by artificially changing the catchment characteristics and translating these to model parameters with uncertainty using model regionalisation. Synthetic rainfall events are introduced to directly relate the change in simulated streamflow to the spatial variability of rainfall. Overall, we conclude that the antecedent catchment wetness and catchment type play an important role in controlling the significance of the spatial distribution of rainfall on streamflow. Results show a relationship between hydrograph characteristics (streamflow peak and volume) and the degree of spatial variability of rainfall for the impermeable catchments under dry antecedent conditions, although this decreases at larger scales; however this sensitivity is significantly undermined under wet antecedent conditions. Although there is indication that the impact of spatial rainfall on streamflow varies as a function of catchment scale, the variability of antecedent conditions between the synthetic catchments seems to mask this significance. Finally, hydrograph responses to different spatial patterns in rainfall depend on assumptions used for model parameter estimation and also the spatial variation in parameters indicating the need of an uncertainty framework in such investigation.
The effect of changes in space shuttle parameters on the NASA/MSFC multilayer diffusion model predictions of surface HCl concentrations

NASA Technical Reports Server (NTRS)

Glasser, M. E.; Rundel, R. D.

1978-01-01

A method for formulating these changes into the model input parameters using a preprocessor program run on a programed data processor was implemented. The results indicate that any changes in the input parameters are small enough to be negligible in comparison to meteorological inputs and the limitations of the model and that such changes will not substantially increase the number of meteorological cases for which the model will predict surface hydrogen chloride concentrations exceeding public safety levels.
Applicability of superelastic materials in seismic protection systems: a parametric study of performance in isolation of structures

NASA Astrophysics Data System (ADS)

Soul, H.; Yawny, A.

2017-08-01

The dynamic response to different seismic inputs of an isolated structure disposed on a sliding layer and connected to the ground with a superelastic NiTi device was analyzed. The device allows wires of NiTi to be mechanically cycled by supporting externally applied tension/compression forces exploiting both dissipative and self-centering capabilities associated with superelasticity. Simulations were carried out modifying the wires length and the structural mass. Both parameters were varied over two orders of magnitude with the aim of evaluating the type of response, the mitigation level that can be accomplished and the combination of parameters resulting in an optimal response. Results indicate that the proposed device is suitable for seismic protection of isolated structures and it is demonstrated that the protective action is more related with the restraining and self-centering properties of the NiTi superelastic wires than with its damping capacity.
An inexpensive frequency-modulated (FM) audio monitor of time-dependent analog parameters.

PubMed

Langdon, R B; Jacobs, R S

1980-02-01

The standard method for quantification and presentation of an experimental variable in real time is the use of visual display on the ordinate of an oscilloscope screen or chart recorder. This paper describes a relatively simple electronic circuit, using commercially available and inexpensive integrated circuits (IC), which generates an audible tone, the pitch of which varies in proportion to a running variable of interest. This device, which we call an "Audioscope," can accept as input the monitor output from any instrument that expresses an experimental parameter as a dc voltage. The Audioscope is particularly useful in implanting microelectrodes intracellularly. It may also function to mediate the first step in data recording on magnetic tape, and/or data analysis and reduction by electronic circuitary. We estimate that this device can be built, with two-channel capability, for less than $50, and in less than 10 hr by an experienced electronics technician.
A motion-constraint logic for moving-base simulators based on variable filter parameters

NASA Technical Reports Server (NTRS)

Miller, G. K., Jr.

1974-01-01

A motion-constraint logic for moving-base simulators has been developed that is a modification to the linear second-order filters generally employed in conventional constraints. In the modified constraint logic, the filter parameters are not constant but vary with the instantaneous motion-base position to increase the constraint as the system approaches the positional limits. With the modified constraint logic, accelerations larger than originally expected are limited while conventional linear filters would result in automatic shutdown of the motion base. In addition, the modified washout logic has frequency-response characteristics that are an improvement over conventional linear filters with braking for low-frequency pilot inputs. During simulated landing approaches of an externally blown flap short take-off and landing (STOL) transport using decoupled longitudinal controls, the pilots were unable to detect much difference between the modified constraint logic and the logic based on linear filters with braking.
Effects of Systematic and Random Errors on the Retrieval of Particle Microphysical Properties from Multiwavelength Lidar Measurements Using Inversion with Regularization

NASA Technical Reports Server (NTRS)

Ramirez, Daniel Perez; Whiteman, David N.; Veselovskii, Igor; Kolgotin, Alexei; Korenskiy, Michael; Alados-Arboledas, Lucas

2013-01-01

In this work we study the effects of systematic and random errors on the inversion of multiwavelength (MW) lidar data using the well-known regularization technique to obtain vertically resolved aerosol microphysical properties. The software implementation used here was developed at the Physics Instrumentation Center (PIC) in Troitsk (Russia) in conjunction with the NASA/Goddard Space Flight Center. Its applicability to Raman lidar systems based on backscattering measurements at three wavelengths (355, 532 and 1064 nm) and extinction measurements at two wavelengths (355 and 532 nm) has been demonstrated widely. The systematic error sensitivity is quantified by first determining the retrieved parameters for a given set of optical input data consistent with three different sets of aerosol physical parameters. Then each optical input is perturbed by varying amounts and the inversion is repeated. Using bimodal aerosol size distributions, we find a generally linear dependence of the retrieved errors in the microphysical properties on the induced systematic errors in the optical data. For the retrievals of effective radius, number/surface/volume concentrations and fine-mode radius and volume, we find that these results are not significantly affected by the range of the constraints used in inversions. But significant sensitivity was found to the allowed range of the imaginary part of the particle refractive index. Our results also indicate that there exists an additive property for the deviations induced by the biases present in the individual optical data. This property permits the results here to be used to predict deviations in retrieved parameters when multiple input optical data are biased simultaneously as well as to study the influence of random errors on the retrievals. The above results are applied to questions regarding lidar design, in particular for the spaceborne multiwavelength lidar under consideration for the upcoming ACE mission.
Next generation lightweight mirror modeling software

NASA Astrophysics Data System (ADS)

Arnold, William R.; Fitzgerald, Matthew; Rosa, Rubin Jaca; Stahl, H. Philip

2013-09-01

The advances in manufacturing techniques for lightweight mirrors, such as EXELSIS deep core low temperature fusion, Corning's continued improvements in the Frit bonding process and the ability to cast large complex designs, combined with water-jet and conventional diamond machining of glasses and ceramics has created the need for more efficient means of generating finite element models of these structures. Traditional methods of assembling 400,000 + element models can take weeks of effort, severely limiting the range of possible optimization variables. This paper will introduce model generation software developed under NASA sponsorship for the design of both terrestrial and space based mirrors. The software deals with any current mirror manufacturing technique, single substrates, multiple arrays of substrates, as well as the ability to merge submodels into a single large model. The modeler generates both mirror and suspension system elements, suspensions can be created either for each individual petal or the whole mirror. A typical model generation of 250,000 nodes and 450,000 elements only takes 3-5 minutes, much of that time being variable input time. The program can create input decks for ANSYS, ABAQUS and NASTRAN. An archive/retrieval system permits creation of complete trade studies, varying cell size, depth, and petal size, suspension geometry with the ability to recall a particular set of parameters and make small or large changes with ease. The input decks created by the modeler are text files which can be modified by any text editor, all the shell thickness parameters and suspension spring rates are accessible and comments in deck identify which groups of elements are associated with these parameters. This again makes optimization easier. With ANSYS decks, the nodes representing support attachments are grouped into components; in ABAQUS these are SETS and in NASTRAN as GRIDPOINT SETS, this make integration of these models into large telescope or satellite models easier.

Effect of high-pressure homogenization preparation on mean globule size and large-diameter tail of oil-in-water injectable emulsions.

PubMed

Peng, Jie; Dong, Wu-Jun; Li, Ling; Xu, Jia-Ming; Jin, Du-Jia; Xia, Xue-Jun; Liu, Yu-Ling

2015-12-01

The effect of different high pressure homogenization energy input parameters on mean diameter droplet size (MDS) and droplets with > 5 μm of lipid injectable emulsions were evaluated. All emulsions were prepared at different water bath temperatures or at different rotation speeds and rotor-stator system times, and using different homogenization pressures and numbers of high-pressure system recirculations. The MDS and polydispersity index (PI) value of the emulsions were determined using the dynamic light scattering (DLS) method, and large-diameter tail assessments were performed using the light-obscuration/single particle optical sensing (LO/SPOS) method. Using 1000 bar homogenization pressure and seven recirculations, the energy input parameters related to the rotor-stator system will not have an effect on the final particle size results. When rotor-stator system energy input parameters are fixed, homogenization pressure and recirculation will affect mean particle size and large diameter droplet. Particle size will decrease with increasing homogenization pressure from 400 bar to 1300 bar when homogenization recirculation is fixed; when the homogenization pressure is fixed at 1000 bar, the particle size of both MDS and percent of fat droplets exceeding 5 μm (PFAT 5 ) will decrease with increasing homogenization recirculations, MDS dropped to 173 nm after five cycles and maintained this level, volume-weighted PFAT 5 will drop to 0.038% after three cycles, so the "plateau" of MDS will come up later than that of PFAT 5 , and the optimal particle size is produced when both of them remained at plateau. Excess homogenization recirculation such as nine times under the 1000 bar may lead to PFAT 5 increase to 0.060% rather than a decrease; therefore, the high-pressure homogenization procedure is the key factor affecting the particle size distribution of emulsions. Varying storage conditions (4-25°C) also influenced particle size, especially the PFAT 5 . Copyright © 2015. Published by Elsevier B.V.
Adaptive Phase Delay Generator

NASA Technical Reports Server (NTRS)

Greer, Lawrence

2013-01-01

There are several experimental setups involving rotating machinery that require some form of synchronization. The adaptive phase delay generator (APDG) the Bencic-1000 is a flexible instrument that allows the user to generate pulses synchronized to the rising edge of a tachometer signal from any piece of rotating machinery. These synchronized pulses can vary by the delay angle, pulse width, number of pulses per period, number of skipped pulses, and total number of pulses. Due to the design of the pulse generator, any and all of these parameters can be changed independently, yielding an unparalleled level of versatility. There are two user interfaces to the APDG. The first is a LabVIEW program that has the advantage of displaying all of the pulse parameters and input signal data within one neatly organized window on the PC monitor. Furthermore, the LabVIEW interface plots the rpm of the two input signal channels in real time. The second user interface is a handheld portable device that goes anywhere a computer is not accessible. It consists of a liquid-crystal display and keypad, which enable the user to control the unit by scrolling through a host of command menus and parameter listings. The APDG combines all of the desired synchronization control into one unit. The experimenter can adjust the delay, pulse width, pulse count, number of skipped pulses, and produce a specified number of pulses per revolution. Each of these parameters can be changed independently, providing an unparalleled level of versatility when synchronizing hardware to a host of rotating machinery. The APDG allows experimenters to set up quickly and generate a host of synchronizing configurations using a simple user interface, which hopefully leads to faster results.
Input dependent cell assembly dynamics in a model of the striatal medium spiny neuron network.

PubMed

Ponzi, Adam; Wickens, Jeff

2012-01-01

The striatal medium spiny neuron (MSN) network is sparsely connected with fairly weak GABAergic collaterals receiving an excitatory glutamatergic cortical projection. Peri-stimulus time histograms (PSTH) of MSN population response investigated in various experimental studies display strong firing rate modulations distributed throughout behavioral task epochs. In previous work we have shown by numerical simulation that sparse random networks of inhibitory spiking neurons with characteristics appropriate for UP state MSNs form cell assemblies which fire together coherently in sequences on long behaviorally relevant timescales when the network receives a fixed pattern of constant input excitation. Here we first extend that model to the case where cortical excitation is composed of many independent noisy Poisson processes and demonstrate that cell assembly dynamics is still observed when the input is sufficiently weak. However if cortical excitation strength is increased more regularly firing and completely quiescent cells are found, which depend on the cortical stimulation. Subsequently we further extend previous work to consider what happens when the excitatory input varies as it would when the animal is engaged in behavior. We investigate how sudden switches in excitation interact with network generated patterned activity. We show that sequences of cell assembly activations can be locked to the excitatory input sequence and outline the range of parameters where this behavior is shown. Model cell population PSTH display both stimulus and temporal specificity, with large population firing rate modulations locked to elapsed time from task events. Thus the random network can generate a large diversity of temporally evolving stimulus dependent responses even though the input is fixed between switches. We suggest the MSN network is well suited to the generation of such slow coherent task dependent response which could be utilized by the animal in behavior.
Input Dependent Cell Assembly Dynamics in a Model of the Striatal Medium Spiny Neuron Network

PubMed Central

Ponzi, Adam; Wickens, Jeff

2012-01-01

The striatal medium spiny neuron (MSN) network is sparsely connected with fairly weak GABAergic collaterals receiving an excitatory glutamatergic cortical projection. Peri-stimulus time histograms (PSTH) of MSN population response investigated in various experimental studies display strong firing rate modulations distributed throughout behavioral task epochs. In previous work we have shown by numerical simulation that sparse random networks of inhibitory spiking neurons with characteristics appropriate for UP state MSNs form cell assemblies which fire together coherently in sequences on long behaviorally relevant timescales when the network receives a fixed pattern of constant input excitation. Here we first extend that model to the case where cortical excitation is composed of many independent noisy Poisson processes and demonstrate that cell assembly dynamics is still observed when the input is sufficiently weak. However if cortical excitation strength is increased more regularly firing and completely quiescent cells are found, which depend on the cortical stimulation. Subsequently we further extend previous work to consider what happens when the excitatory input varies as it would when the animal is engaged in behavior. We investigate how sudden switches in excitation interact with network generated patterned activity. We show that sequences of cell assembly activations can be locked to the excitatory input sequence and outline the range of parameters where this behavior is shown. Model cell population PSTH display both stimulus and temporal specificity, with large population firing rate modulations locked to elapsed time from task events. Thus the random network can generate a large diversity of temporally evolving stimulus dependent responses even though the input is fixed between switches. We suggest the MSN network is well suited to the generation of such slow coherent task dependent response which could be utilized by the animal in behavior. PMID:22438838
Cost Analyses in the US and Japan: A Cross-Country Comparative Analysis Applied to the PRONOUNCE Trial in Non-Squamous Non-Small Cell Lung Cancer.

PubMed

Hess, Lisa M; Rajan, Narayan; Winfree, Katherine; Davey, Peter; Ball, Mark; Knox, Hediyyih; Graham, Christopher

2015-12-01

Health technology assessment is not required for regulatory submission or approval in either the United States (US) or Japan. This study was designed as a cross-country evaluation of cost analyses conducted in the US and Japan based on the PRONOUNCE phase III lung cancer trial, which compared pemetrexed plus carboplatin followed by pemetrexed (PemC) versus paclitaxel plus carboplatin plus bevacizumab followed by bevacizumab (PCB). Two cost analyses were conducted in accordance with International Society For Pharmacoeconomics and Outcomes Research good research practice standards. Costs were obtained based on local pricing structures; outcomes were considered equivalent based on the PRONOUNCE trial results. Other inputs were included from the trial data (e.g., toxicity rates) or from local practice sources (e.g., toxicity management). The models were compared across key input and transferability factors. Despite differences in local input data, both models demonstrated a similar direction, with the cost of PemC being consistently lower than the cost of PCB. The variation in individual input parameters did affect some of the specific categories, such as toxicity, and impacted sensitivity analyses, with the cost differential between comparators being greater in Japan than in the US. When economic models are based on clinical trial data, many inputs and outcomes are held consistent. The alterable inputs were not in and of themselves large enough to significantly impact the results between countries, which were directionally consistent with greater variation seen in sensitivity analyses. The factors that vary across jurisdictions, even when minor, can have an impact on trial-based economic analyses. Eli Lilly and Company.
Water and solute mass balance of five small, relatively undisturbed watersheds in the U.S.

USGS Publications Warehouse

Peters, N.E.; Shanley, J.B.; Aulenbach, Brent T.; Webb, R.M.; Campbell, D.H.; Hunt, R.; Larsen, M.C.; Stallard, R.F.; Troester, J.; Walker, J.F.

2006-01-01

Geochemical mass balances were computed for water years 1992-1997 (October 1991 through September 1997) for the five watersheds of the U.S. Geological Survey Water, Energy, and Biogeochemical Budgets (WEBB) Program to determine the primary regional controls on yields of the major dissolved inorganic solutes. The sites, which vary markedly with respect to climate, geology, physiography, and ecology, are: Allequash Creek, Wisconsin (low-relief, humid continental forest); Andrews Creek, Colorado (cold alpine, taiga/tundra, and subalpine boreal forest); Ri??o Icacos, Puerto Rico (lower montane, wet tropical forest); Panola Mountain, Georgia (humid subtropical piedmont forest); and Sleepers River, Vermont (humid northern hardwood forest). Streamwater output fluxes were determined by constructing empirical multivariate concentration models including discharge and seasonal components. Input fluxes were computed from weekly wet-only or bulk precipitation sampling. Despite uncertainties in input fluxes arising from poorly defined elevation gradients, lack of dry-deposition and occult-deposition measurements, and uncertain sea-salt contributions, the following was concluded: (1) for solutes derived primarily from rock weathering (Ca, Mg, Na, K, and H4SiO4), net fluxes (outputs in streamflow minus inputs in deposition) varied by two orders of magnitude, which is attributed to a large gradient in rock weathering rates controlled by climate and geologic parent material; (2) the net flux of atmospherically derived solutes (NH4, NO3, SO4, and Cl) was similar among sites, with SO4 being the most variable and NH4 and NO3 generally retained (except for NO 3 at Andrews); and (3) relations among monthly solute fluxes and differences among solute concentration model parameters yielded additional insights into comparative biogeochemical processes at the sites. ?? 2005 Elsevier B.V. All rights reserved.
Greedy Sampling and Incremental Surrogate Model-Based Tailoring of Aeroservoelastic Model Database for Flexible Aircraft

NASA Technical Reports Server (NTRS)

Wang, Yi; Pant, Kapil; Brenner, Martin J.; Ouellette, Jeffrey A.

2018-01-01

This paper presents a data analysis and modeling framework to tailor and develop linear parameter-varying (LPV) aeroservoelastic (ASE) model database for flexible aircrafts in broad 2D flight parameter space. The Kriging surrogate model is constructed using ASE models at a fraction of grid points within the original model database, and then the ASE model at any flight condition can be obtained simply through surrogate model interpolation. The greedy sampling algorithm is developed to select the next sample point that carries the worst relative error between the surrogate model prediction and the benchmark model in the frequency domain among all input-output channels. The process is iterated to incrementally improve surrogate model accuracy till a pre-determined tolerance or iteration budget is met. The methodology is applied to the ASE model database of a flexible aircraft currently being tested at NASA/AFRC for flutter suppression and gust load alleviation. Our studies indicate that the proposed method can reduce the number of models in the original database by 67%. Even so the ASE models obtained through Kriging interpolation match the model in the original database constructed directly from the physics-based tool with the worst relative error far below 1%. The interpolated ASE model exhibits continuously-varying gains along a set of prescribed flight conditions. More importantly, the selected grid points are distributed non-uniformly in the parameter space, a) capturing the distinctly different dynamic behavior and its dependence on flight parameters, and b) reiterating the need and utility for adaptive space sampling techniques for ASE model database compaction. The present framework is directly extendible to high-dimensional flight parameter space, and can be used to guide the ASE model development, model order reduction, robust control synthesis and novel vehicle design of flexible aircraft.
Quantitative assessment of key parameters in qualitative vulnerability methods applied in karst systems based on an integrated numerical modelling approach

NASA Astrophysics Data System (ADS)

Doummar, Joanna; Kassem, Assaad

2017-04-01

In the framework of a three-year PEER (USAID/NSF) funded project, flow in a Karst system in Lebanon (Assal) dominated by snow and semi arid conditions was simulated and successfully calibrated using an integrated numerical model (MIKE-She 2016) based on high resolution input data and detailed catchment characterization. Point source infiltration and fast flow pathways were simulated by a bypass function and a high conductive lens respectively. The approach consisted of identifying all the factors used in qualitative vulnerability methods (COP, EPIK, PI, DRASTIC, GOD) applied in karst systems and to assess their influence on recharge signals in the different hydrological karst compartments (Atmosphere, Unsaturated zone and Saturated zone) based on the integrated numerical model. These parameters are usually attributed different weights according to their estimated impact on Groundwater vulnerability. The aim of this work is to quantify the importance of each of these parameters and outline parameters that are not accounted for in standard methods, but that might play a role in the vulnerability of a system. The spatial distribution of the detailed evapotranspiration, infiltration, and recharge signals from atmosphere to unsaturated zone to saturated zone was compared and contrasted among different surface settings and under varying flow conditions (e.g., in varying slopes, land cover, precipitation intensity, and soil properties as well point source infiltration). Furthermore a sensitivity analysis of individual or coupled major parameters allows quantifying their impact on recharge and indirectly on vulnerability. The preliminary analysis yields a new methodology that accounts for most of the factors influencing vulnerability while refining the weights attributed to each one of them, based on a quantitative approach.
Charging of the Van Allen Probes: Theory and Simulations

NASA Astrophysics Data System (ADS)

Delzanno, G. L.; Meierbachtol, C.; Svyatskiy, D.; Denton, M.

2017-12-01

The electrical charging of spacecraft has been a known problem since the beginning of the space age. Its consequences can vary from moderate (single event upsets) to catastrophic (total loss of the spacecraft) depending on a variety of causes, some of which could be related to the surrounding plasma environment, including emission processes from the spacecraft surface. Because of its complexity and cost, this problem is typically studied using numerical simulations. However, inherent unknowns in both plasma parameters and spacecraft material properties can lead to inaccurate predictions of overall spacecraft charging levels. The goal of this work is to identify and study the driving causes and necessary parameters for particular spacecraft charging events on the Van Allen Probes (VAP) spacecraft. This is achieved by making use of plasma theory, numerical simulations, and on-board data. First, we present a simple theoretical spacecraft charging model, which assumes a spherical spacecraft geometry and is based upon the classical orbital-motion-limited approximation. Some input parameters to the model (such as the warm plasma distribution function) are taken directly from on-board VAP data, while other parameters are either varied parametrically to assess their impact on the spacecraft potential, or constrained through spacecraft charging data and statistical techniques. Second, a fully self-consistent numerical simulation is performed by supplying these parameters to CPIC, a particle-in-cell code specifically designed for studying plasma-material interactions. CPIC simulations remove some of the assumptions of the theoretical model and also capture the influence of the full geometry of the spacecraft. The CPIC numerical simulation results will be presented and compared with on-board VAP data. This work will set the foundation for our eventual goal of importing the full plasma environment from the LANL-developed SHIELDS framework into CPIC, in order to more accurately predict spacecraft charging.
Extension of the PC version of VEPFIT with input and output routines running under Windows

NASA Astrophysics Data System (ADS)

Schut, H.; van Veen, A.

1995-01-01

The fitting program VEPFIT has been extended with applications running under the Microsoft-Windows environment facilitating the input and output of the VEPFIT fitting module. We have exploited the Microsoft-Windows graphical users interface by making use of dialog windows, scrollbars, command buttons, etc. The user communicates with the program simply by clicking and dragging with the mouse pointing device. Keyboard actions are limited to a minimum. Upon changing one or more input parameters the results of the modeling of the S-parameter and Ps fractions versus positron implantation energy are updated and displayed. This action can be considered as the first step in the fitting procedure upon which the user can decide to further adapt the input parameters or to forward these parameters as initial values to the fitting routine. The modeling step has proven to be helpful for designing positron beam experiments.
Correlation of Tumor Immunohistochemistry with Dynamic Contrast-Enhanced and DSC-MRI Parameters in Patients with Gliomas.

PubMed

Nguyen, T B; Cron, G O; Bezzina, K; Perdrizet, K; Torres, C H; Chakraborty, S; Woulfe, J; Jansen, G H; Thornhill, R E; Zanette, B; Cameron, I G

2016-12-01

Tumor CBV is a prognostic and predictive marker for patients with gliomas. Tumor CBV can be measured noninvasively with different MR imaging techniques; however, it is not clear which of these techniques most closely reflects histologically-measured tumor CBV. Our aim was to investigate the correlations between dynamic contrast-enhanced and DSC-MR imaging parameters and immunohistochemistry in patients with gliomas. Forty-three patients with a new diagnosis of glioma underwent a preoperative MR imaging examination with dynamic contrast-enhanced and DSC sequences. Unnormalized and normalized cerebral blood volume was obtained from DSC MR imaging. Two sets of plasma volume and volume transfer constant maps were obtained from dynamic contrast-enhanced MR imaging. Plasma volume obtained from the phase-derived vascular input function and bookend T1 mapping (Vp_Φ) and volume transfer constant obtained from phase-derived vascular input function and bookend T1 mapping (K trans _Φ) were determined. Plasma volume obtained from magnitude-derived vascular input function (Vp_SI) and volume transfer constant obtained from magnitude-derived vascular input function (K trans _SI) were acquired, without T1 mapping. Using CD34 staining, we measured microvessel density and microvessel area within 3 representative areas of the resected tumor specimen. The Mann-Whitney U test was used to test for differences according to grade and degree of enhancement. The Spearman correlation was performed to determine the relationship between dynamic contrast-enhanced and DSC parameters and histopathologic measurements. Microvessel area, microvessel density, dynamic contrast-enhanced, and DSC-MR imaging parameters varied according to the grade and degree of enhancement (P < .05). A strong correlation was found between microvessel area and Vp_Φ and between microvessel area and unnormalized blood volume (r s ≥ 0.61). A moderate correlation was found between microvessel area and normalized blood volume, microvessel area and Vp_SI, microvessel area and K trans _Φ, microvessel area and K trans _SI, microvessel density and Vp_Φ, microvessel density and unnormalized blood volume, and microvessel density and normalized blood volume (0.44 ≤ r s ≤ 0.57). A weaker correlation was found between microvessel density and K trans _Φ and between microvessel density and K trans _SI (r s ≤ 0.41). With dynamic contrast-enhanced MR imaging, use of a phase-derived vascular input function and bookend T1 mapping improves the correlation between immunohistochemistry and plasma volume, but not between immunohistochemistry and the volume transfer constant. With DSC-MR imaging, normalization of tumor CBV could decrease the correlation with microvessel area. © 2016 by American Journal of Neuroradiology.
Parameter Balancing in Kinetic Models of Cell Metabolism†

PubMed Central

2010-01-01

Kinetic modeling of metabolic pathways has become a major field of systems biology. It combines structural information about metabolic pathways with quantitative enzymatic rate laws. Some of the kinetic constants needed for a model could be collected from ever-growing literature and public web resources, but they are often incomplete, incompatible, or simply not available. We address this lack of information by parameter balancing, a method to complete given sets of kinetic constants. Based on Bayesian parameter estimation, it exploits the thermodynamic dependencies among different biochemical quantities to guess realistic model parameters from available kinetic data. Our algorithm accounts for varying measurement conditions in the input data (pH value and temperature). It can process kinetic constants and state-dependent quantities such as metabolite concentrations or chemical potentials, and uses prior distributions and data augmentation to keep the estimated quantities within plausible ranges. An online service and free software for parameter balancing with models provided in SBML format (Systems Biology Markup Language) is accessible at www.semanticsbml.org. We demonstrate its practical use with a small model of the phosphofructokinase reaction and discuss its possible applications and limitations. In the future, parameter balancing could become an important routine step in the kinetic modeling of large metabolic networks. PMID:21038890
Instrument to average 100 data sets

NASA Technical Reports Server (NTRS)

Tuma, G. B.; Birchenough, A. G.; Rice, W. J.

1977-01-01

An instrumentation system is currently under development which will measure many of the important parameters associated with the operation of an internal combustion engine. Some of these parameters include mass-fraction burn rate, ignition energy, and the indicated mean effective pressure. One of the characteristics of an internal combustion engine is the cycle-to-cycle variation of these parameters. A curve-averaging instrument has been produced which will generate the average curve, over 100 cycles, of any engine parameter. the average curve is described by 2048 discrete points which are displayed on an oscilloscope screen to facilitate recording and is available in real time. Input can be any parameter which is expressed as a + or - 10-volt signal. Operation of the curve-averaging instrument is defined between 100 and 6000 rpm. Provisions have also been made for averaging as many as four parameters simultaneously, with a subsequent decrease in resolution. This provides the means to correlate and perhaps interrelate the phenomena occurring in an internal combustion engine. This instrument has been used successfully on a 1975 Chevrolet V8 engine, and on a Continental 6-cylinder aircraft engine. While this instrument was designed for use on an internal combustion engine, with some modification it can be used to average any cyclically varying waveform.
Aircraft Hydraulic Systems Dynamic Analysis Component Data Handbook

DTIC Science & Technology

1980-04-01

82 13. QUINCKE TUBE ...................................... 85 14. 11EAT EXCHANGER ............. ................... 90...Input Parameters ....... ........... .7 61 )uincke Tube Input Parameters with Hole Locat ions 87 62 "rototype Quincke Tube Data ........... 89 6 3 Fo-,:ed...Elasticity (Line 3) PSI 1.6E7 FIGURE 58 HSFR INPUT DATA FOR PULSCO TYPE ACOUSTIC FILTER 84 13. QUINCKE TUBE A means to dampen acoustic noise at resonance
Ensemble urban flood simulation in comparison with laboratory-scale experiments: Impact of interaction models for manhole, sewer pipe, and surface flow

NASA Astrophysics Data System (ADS)

Noh, Seong Jin; Lee, Seungsoo; An, Hyunuk; Kawaike, Kenji; Nakagawa, Hajime

2016-11-01

An urban flood is an integrated phenomenon that is affected by various uncertainty sources such as input forcing, model parameters, complex geometry, and exchanges of flow among different domains in surfaces and subsurfaces. Despite considerable advances in urban flood modeling techniques, limited knowledge is currently available with regard to the impact of dynamic interaction among different flow domains on urban floods. In this paper, an ensemble method for urban flood modeling is presented to consider the parameter uncertainty of interaction models among a manhole, a sewer pipe, and surface flow. Laboratory-scale experiments on urban flood and inundation are performed under various flow conditions to investigate the parameter uncertainty of interaction models. The results show that ensemble simulation using interaction models based on weir and orifice formulas reproduces experimental data with high accuracy and detects the identifiability of model parameters. Among interaction-related parameters, the parameters of the sewer-manhole interaction show lower uncertainty than those of the sewer-surface interaction. Experimental data obtained under unsteady-state conditions are more informative than those obtained under steady-state conditions to assess the parameter uncertainty of interaction models. Although the optimal parameters vary according to the flow conditions, the difference is marginal. Simulation results also confirm the capability of the interaction models and the potential of the ensemble-based approaches to facilitate urban flood simulation.
How Many Wolves (Canis lupus) Fit into Germany? The Role of Assumptions in Predictive Rule-Based Habitat Models for Habitat Generalists

PubMed Central

Fechter, Dominik; Storch, Ilse

2014-01-01

Due to legislative protection, many species, including large carnivores, are currently recolonizing Europe. To address the impending human-wildlife conflicts in advance, predictive habitat models can be used to determine potentially suitable habitat and areas likely to be recolonized. As field data are often limited, quantitative rule based models or the extrapolation of results from other studies are often the techniques of choice. Using the wolf (Canis lupus) in Germany as a model for habitat generalists, we developed a habitat model based on the location and extent of twelve existing wolf home ranges in Eastern Germany, current knowledge on wolf biology, different habitat modeling techniques and various input data to analyze ten different input parameter sets and address the following questions: (1) How do a priori assumptions and different input data or habitat modeling techniques affect the abundance and distribution of potentially suitable wolf habitat and the number of wolf packs in Germany? (2) In a synthesis across input parameter sets, what areas are predicted to be most suitable? (3) Are existing wolf pack home ranges in Eastern Germany consistent with current knowledge on wolf biology and habitat relationships? Our results indicate that depending on which assumptions on habitat relationships are applied in the model and which modeling techniques are chosen, the amount of potentially suitable habitat estimated varies greatly. Depending on a priori assumptions, Germany could accommodate between 154 and 1769 wolf packs. The locations of the existing wolf pack home ranges in Eastern Germany indicate that wolves are able to adapt to areas densely populated by humans, but are limited to areas with low road densities. Our analysis suggests that predictive habitat maps in general, should be interpreted with caution and illustrates the risk for habitat modelers to concentrate on only one selection of habitat factors or modeling technique. PMID:25029506
Suggestions for CAP-TSD mesh and time-step input parameters

NASA Technical Reports Server (NTRS)

Bland, Samuel R.

1991-01-01

Suggestions for some of the input parameters used in the CAP-TSD (Computational Aeroelasticity Program-Transonic Small Disturbance) computer code are presented. These parameters include those associated with the mesh design and time step. The guidelines are based principally on experience with a one-dimensional model problem used to study wave propagation in the vertical direction.
Robust synergetic control design under inputs and states constraints

NASA Astrophysics Data System (ADS)

Rastegar, Saeid; Araújo, Rui; Sadati, Jalil

2018-03-01

In this paper, a novel robust-constrained control methodology for discrete-time linear parameter-varying (DT-LPV) systems is proposed based on a synergetic control theory (SCT) approach. It is shown that in DT-LPV systems without uncertainty, and for any unmeasured bounded additive disturbance, the proposed controller accomplishes the goal of stabilising the system by asymptotically driving the error of the controlled variable to a bounded set containing the origin and then maintaining it there. Moreover, given an uncertain DT-LPV system jointly subject to unmeasured and constrained additive disturbances, and constraints in states, input commands and reference signals (set points), then invariant set theory is used to find an appropriate polyhedral robust invariant region in which the proposed control framework is guaranteed to robustly stabilise the closed-loop system. Furthermore, this is achieved even for the case of varying non-zero control set points in such uncertain DT-LPV systems. The controller is characterised to have a simple structure leading to an easy implementation, and a non-complex design process. The effectiveness of the proposed method and the implications of the controller design on feasibility and closed-loop performance are demonstrated through application examples on the temperature control on a continuous-stirred tank reactor plant, on the control of a real-coupled DC motor plant, and on an open-loop unstable system example.
Multiple-input single-output closed-loop isometric force control using asynchronous intrafascicular multi-electrode stimulation.

PubMed

Frankel, Mitchell A; Dowden, Brett R; Mathews, V John; Normann, Richard A; Clark, Gregory A; Meek, Sanford G

2011-06-01

Although asynchronous intrafascicular multi-electrode stimulation (IFMS) can evoke fatigue-resistant muscle force, a priori determination of the necessary stimulation parameters for precise force production is not possible. This paper presents a proportionally-modulated, multiple-input single-output (MISO) controller that was designed and experimentally validated for real-time, closed-loop force-feedback control of asynchronous IFMS. Experiments were conducted on anesthetized felines with a Utah Slanted Electrode Array implanted in the sciatic nerve, either acutely or chronically ( n = 1 for each). Isometric forces were evoked in plantar-flexor muscles, and target forces consisted of up to 7 min of step, sinusoidal, and more complex time-varying trajectories. The controller was successful in evoking steps in force with time-to-peak of less than 0.45 s, steady-state ripple of less than 7% of the mean steady-state force, and near-zero steady-state error even in the presence of muscle fatigue, but with transient overshoot of near 20%. The controller was also successful in evoking target sinusoidal and complex time-varying force trajectories with amplitude error of less than 0.5 N and time delay of approximately 300 ms. This MISO control strategy can potentially be used to develop closed-loop asynchronous IFMS controllers for a wide variety of multi-electrode stimulation applications to restore lost motor function.
Unsteady hovering wake parameters identified from dynamic model tests, part 1

NASA Technical Reports Server (NTRS)

Hohenemser, K. H.; Crews, S. T.

1977-01-01

The development of a 4-bladed model rotor is reported that can be excited with a simple eccentric mechanism in progressing and regressing modes with either harmonic or transient inputs. Parameter identification methods were applied to the problem of extracting parameters for linear perturbation models, including rotor dynamic inflow effects, from the measured blade flapping responses to transient pitch stirring excitations. These perturbation models were then used to predict blade flapping response to other pitch stirring transient inputs, and rotor wake and blade flapping responses to harmonic inputs. The viability and utility of using parameter identification methods for extracting the perturbation models from transients are demonstrated through these combined analytical and experimental studies.

Development and weighting of a life cycle assessment screening model

NASA Astrophysics Data System (ADS)

Bates, Wayne E.; O'Shaughnessy, James; Johnson, Sharon A.; Sisson, Richard

2004-02-01

Nearly all life cycle assessment tools available today are high priced, comprehensive and quantitative models requiring a significant amount of data collection and data input. In addition, most of the available software packages require a great deal of training time to learn how to operate the model software. Even after this time investment, results are not guaranteed because of the number of estimations and assumptions often necessary to run the model. As a result, product development, design teams and environmental specialists need a simplified tool that will allow for the qualitative evaluation and "screening" of various design options. This paper presents the development and design of a generic, qualitative life cycle screening model and demonstrates its applicability and ease of use. The model uses qualitative environmental, health and safety factors, based on site or product-specific issues, to sensitize the overall results for a given set of conditions. The paper also evaluates the impact of different population input ranking values on model output. The final analysis is based on site or product-specific variables. The user can then evaluate various design changes and the apparent impact or improvement on the environment, health and safety, compliance cost and overall corporate liability. Major input parameters can be varied, and factors such as materials use, pollution prevention, waste minimization, worker safety, product life, environmental impacts, return of investment, and recycle are evaluated. The flexibility of the model format will be discussed in order to demonstrate the applicability and usefulness within nearly any industry sector. Finally, an example using audience input value scores will be compared to other population input results.
Segmentation, dynamic storage, and variable loading on CDC equipment

NASA Technical Reports Server (NTRS)

Tiffany, S. H.

1980-01-01

Techniques for varying the segmented load structure of a program and for varying the dynamic storage allocation, depending upon whether a batch type or interactive type run is desired, are explained and demonstrated. All changes are based on a single data input to the program. The techniques involve: code within the program to suppress scratch pad input/output (I/O) for a batch run or translate the in-core data storage area from blank common to the end-of-code+1 address of a particular segment for an interactive run; automatic editing of the segload directives prior to loading, based upon data input to the program, to vary the structure of the load for interactive and batch runs; and automatic editing of the load map to determine the initial addresses for in core data storage for an interactive run.
String resistance detector

NASA Technical Reports Server (NTRS)

Hall, A. Daniel (Inventor); Davies, Francis J. (Inventor)

2007-01-01

Method and system are disclosed for determining individual string resistance in a network of strings when the current through a parallel connected string is unknown and when the voltage across a series connected string is unknown. The method/system of the invention involves connecting one or more frequency-varying impedance components with known electrical characteristics to each string and applying a frequency-varying input signal to the network of strings. The frequency-varying impedance components may be one or more capacitors, inductors, or both, and are selected so that each string is uniquely identifiable in the output signal resulting from the frequency-varying input signal. Numerical methods, such as non-linear regression, may then be used to resolve the resistance associated with each string.
Sensitivity analysis and nonlinearity assessment of steam cracking furnace process

NASA Astrophysics Data System (ADS)

Rosli, M. N.; Sudibyo, Aziz, N.

2017-11-01

In this paper, sensitivity analysis and nonlinearity assessment of cracking furnace process are presented. For the sensitivity analysis, the fractional factorial design method is employed as a method to analyze the effect of input parameters, which consist of four manipulated variables and two disturbance variables, to the output variables and to identify the interaction between each parameter. The result of the factorial design method is used as a screening method to reduce the number of parameters, and subsequently, reducing the complexity of the model. It shows that out of six input parameters, four parameters are significant. After the screening is completed, step test is performed on the significant input parameters to assess the degree of nonlinearity of the system. The result shows that the system is highly nonlinear with respect to changes in an air-to-fuel ratio (AFR) and feed composition.
Evaluation of INL Supplied MOOSE/OSPREY Model: Modeling Water Adsorption on Type 3A Molecular Sieve

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

Pompilio, L. M.; DePaoli, D. W.; Spencer, B. B.

The purpose of this study was to evaluate Idaho National Lab’s Multiphysics Object-Oriented Simulation Environment (MOOSE) software in modeling the adsorption of water onto type 3A molecular sieve (3AMS). MOOSE can be thought-of as a computing framework within which applications modeling specific coupled-phenomena can be developed and run. The application titled Off-gas SeParation and REcoverY (OSPREY) has been developed to model gas sorption in packed columns. The sorbate breakthrough curve calculated by MOOSE/OSPREY was compared to results previously obtained in the deep bed hydration tests conducted at Oak Ridge National Laboratory. The coding framework permits selection of various options, whenmore » they exist, for modeling a process. For example, the OSPREY module includes options to model the adsorption equilibrium with a Langmuir model or a generalized statistical thermodynamic adsorption (GSTA) model. The vapor solid equilibria and the operating conditions of the process (e.g., gas phase concentration) are required to calculate the concentration gradient driving the mass transfer between phases. Both the Langmuir and GSTA models were tested in this evaluation. Input variables were either known from experimental conditions, or were available (e.g., density) or were estimated (e.g., thermal conductivity of sorbent) from the literature. Variables were considered independent of time, i.e., rather than having a mass transfer coefficient that varied with time or position in the bed, the parameter was set to remain constant. The calculated results did not coincide with data from laboratory tests. The model accurately estimated the number of bed volumes processed for the given operating parameters, but breakthrough times were not accurately predicted, varying 50% or more from the data. The shape of the breakthrough curves also differed from the experimental data, indicating a much wider sorption band. Model modifications are needed to improve its utility and predictive capability. Recommended improvements include: greater flexibility for input of mass transfer parameters, time-variable gas inlet concentration, direct output of loading and temperature profiles along the bed, and capability to conduct simulations of beds in series.« less
Evaluation of Uncertainty in Constituent Input Parameters for Modeling the Fate of IMX 101 Components

DTIC Science & Technology

2017-05-01

ER D C/ EL T R- 17 -7 Environmental Security Technology Certification Program (ESTCP) Evaluation of Uncertainty in Constituent Input...Environmental Security Technology Certification Program (ESTCP) ERDC/EL TR-17-7 May 2017 Evaluation of Uncertainty in Constituent Input Parameters...Environmental Evaluation and Characterization Sys- tem (TREECS™) was applied to a groundwater site and a surface water site to evaluate the sensitivity
Piloted Parameter Identification Flight Test Maneuvers for Closed Loop Modeling of the F-18 High Alpha Research Vehicle (HARV)

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

1996-01-01

Flight test maneuvers are specified for the F-18 High Alpha Research Vehicle (HARV). The maneuvers were designed for closed loop parameter identification purposes, specifically for longitudinal and lateral linear model parameter estimation at 5, 20, 30, 45, and 60 degrees angle of attack, using the NASA 1A control law. Each maneuver is to be realized by the pilot applying square wave inputs to specific pilot station controls. Maneuver descriptions and complete specifications of the time/amplitude points defining each input are included, along with plots of the input time histories.
The relative effectiveness of empirical and physical models for simulating the dense undercurrent of pyroclastic flows under different emplacement conditions

USGS Publications Warehouse

Ogburn, Sarah E.; Calder, Eliza S

2017-01-01

High concentration pyroclastic density currents (PDCs) are hot avalanches of volcanic rock and gas and are among the most destructive volcanic hazards due to their speed and mobility. Mitigating the risk associated with these flows depends upon accurate forecasting of possible impacted areas, often using empirical or physical models. TITAN2D, VolcFlow, LAHARZ, and ΔH/L or energy cone models each employ different rheologies or empirical relationships and therefore differ in appropriateness of application for different types of mass flows and topographic environments. This work seeks to test different statistically- and physically-based models against a range of PDCs of different volumes, emplaced under different conditions, over different topography in order to test the relative effectiveness, operational aspects, and ultimately, the utility of each model for use in hazard assessments. The purpose of this work is not to rank models, but rather to understand the extent to which the different modeling approaches can replicate reality in certain conditions, and to explore the dynamics of PDCs themselves. In this work, these models are used to recreate the inundation areas of the dense-basal undercurrent of all 13 mapped, land-confined, Soufrière Hills Volcano dome-collapse PDCs emplaced from 1996 to 2010 to test the relative effectiveness of different computational models. Best-fit model results and their input parameters are compared with results using observation- and deposit-derived input parameters. Additional comparison is made between best-fit model results and those using empirically-derived input parameters from the FlowDat global database, which represent “forward” modeling simulations as would be completed for hazard assessment purposes. Results indicate that TITAN2D is able to reproduce inundated areas well using flux sources, although velocities are often unrealistically high. VolcFlow is also able to replicate flow runout well, but does not capture the lateral spreading in distal regions of larger-volume flows. Both models are better at reproducing the inundated area of single-pulse, valley-confined, smaller-volume flows than sustained, highly unsteady, larger-volume flows, which are often partially unchannelized. The simple rheological models of TITAN2D and VolcFlow are not able to recreate all features of these more complex flows. LAHARZ is fast to run and can give a rough approximation of inundation, but may not be appropriate for all PDCs and the designation of starting locations is difficult. The ΔH/L cone model is also very quick to run and gives reasonable approximations of runout distance, but does not inherently model flow channelization or directionality and thus unrealistically covers all interfluves. Empirically-based models like LAHARZ and ΔH/L cones can be quick, first-approximations of flow runout, provided a database of similar flows, e.g., FlowDat, is available to properly calculate coefficients or ΔH/L. For hazard assessment purposes, geophysical models like TITAN2D and VolcFlow can be useful for producing both scenario-based or probabilistic hazard maps, but must be run many times with varying input parameters. LAHARZ and ΔH/L cones can be used to produce simple modeling-based hazard maps when run with a variety of input volumes, but do not explicitly consider the probability of occurrence of different volumes. For forward modeling purposes, the ability to derive potential input parameters from global or local databases is crucial, though important input parameters for VolcFlow cannot be empirically estimated. Not only does this work provide a useful comparison of the operational aspects and behavior of various models for hazard assessment, but it also enriches conceptual understanding of the dynamics of the PDCs themselves.
Ensemble Kalman Filter for Dynamic State Estimation of Power Grids Stochastically Driven by Time-correlated Mechanical Input Power

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

Rosenthal, William Steven; Tartakovsky, Alex; Huang, Zhenyu

State and parameter estimation of power transmission networks is important for monitoring power grid operating conditions and analyzing transient stability. Wind power generation depends on fluctuating input power levels, which are correlated in time and contribute to uncertainty in turbine dynamical models. The ensemble Kalman filter (EnKF), a standard state estimation technique, uses a deterministic forecast and does not explicitly model time-correlated noise in parameters such as mechanical input power. However, this uncertainty affects the probability of fault-induced transient instability and increased prediction bias. Here a novel approach is to model input power noise with time-correlated stochastic fluctuations, and integratemore » them with the network dynamics during the forecast. While the EnKF has been used to calibrate constant parameters in turbine dynamical models, the calibration of a statistical model for a time-correlated parameter has not been investigated. In this study, twin experiments on a standard transmission network test case are used to validate our time-correlated noise model framework for state estimation of unsteady operating conditions and transient stability analysis, and a methodology is proposed for the inference of the mechanical input power time-correlation length parameter using time-series data from PMUs monitoring power dynamics at generator buses.« less
Ensemble Kalman Filter for Dynamic State Estimation of Power Grids Stochastically Driven by Time-correlated Mechanical Input Power

DOE PAGES

Rosenthal, William Steven; Tartakovsky, Alex; Huang, Zhenyu

2017-10-31

State and parameter estimation of power transmission networks is important for monitoring power grid operating conditions and analyzing transient stability. Wind power generation depends on fluctuating input power levels, which are correlated in time and contribute to uncertainty in turbine dynamical models. The ensemble Kalman filter (EnKF), a standard state estimation technique, uses a deterministic forecast and does not explicitly model time-correlated noise in parameters such as mechanical input power. However, this uncertainty affects the probability of fault-induced transient instability and increased prediction bias. Here a novel approach is to model input power noise with time-correlated stochastic fluctuations, and integratemore » them with the network dynamics during the forecast. While the EnKF has been used to calibrate constant parameters in turbine dynamical models, the calibration of a statistical model for a time-correlated parameter has not been investigated. In this study, twin experiments on a standard transmission network test case are used to validate our time-correlated noise model framework for state estimation of unsteady operating conditions and transient stability analysis, and a methodology is proposed for the inference of the mechanical input power time-correlation length parameter using time-series data from PMUs monitoring power dynamics at generator buses.« less
INDES User's guide multistep input design with nonlinear rotorcraft modeling

NASA Technical Reports Server (NTRS)

1979-01-01

The INDES computer program, a multistep input design program used as part of a data processing technique for rotorcraft systems identification, is described. Flight test inputs base on INDES improve the accuracy of parameter estimates. The input design algorithm, program input, and program output are presented.
MIA-Clustering: a novel method for segmentation of paleontological material.

PubMed

Dunmore, Christopher J; Wollny, Gert; Skinner, Matthew M

2018-01-01

Paleontological research increasingly uses high-resolution micro-computed tomography (μCT) to study the inner architecture of modern and fossil bone material to answer important questions regarding vertebrate evolution. This non-destructive method allows for the measurement of otherwise inaccessible morphology. Digital measurement is predicated on the accurate segmentation of modern or fossilized bone from other structures imaged in μCT scans, as errors in segmentation can result in inaccurate calculations of structural parameters. Several approaches to image segmentation have been proposed with varying degrees of automation, ranging from completely manual segmentation, to the selection of input parameters required for computational algorithms. Many of these segmentation algorithms provide speed and reproducibility at the cost of flexibility that manual segmentation provides. In particular, the segmentation of modern and fossil bone in the presence of materials such as desiccated soft tissue, soil matrix or precipitated crystalline material can be difficult. Here we present a free open-source segmentation algorithm application capable of segmenting modern and fossil bone, which also reduces subjective user decisions to a minimum. We compare the effectiveness of this algorithm with another leading method by using both to measure the parameters of a known dimension reference object, as well as to segment an example problematic fossil scan. The results demonstrate that the medical image analysis-clustering method produces accurate segmentations and offers more flexibility than those of equivalent precision. Its free availability, flexibility to deal with non-bone inclusions and limited need for user input give it broad applicability in anthropological, anatomical, and paleontological contexts.
Variations in Modeled Dengue Transmission over Puerto Rico Using a Climate Driven Dynamic Model

NASA Technical Reports Server (NTRS)

Morin, Cory; Monaghan, Andrew; Crosson, William; Quattrochi, Dale; Luvall, Jeffrey

2014-01-01

Dengue fever is a mosquito-borne viral disease reemerging throughout much of the tropical Americas. Dengue virus transmission is explicitly influenced by climate and the environment through its primary vector, Aedes aegypti. Temperature regulates Ae. aegypti development, survival, and replication rates as well as the incubation period of the virus within the mosquito. Precipitation provides water for many of the preferred breeding habitats of the mosquito, including buckets, old tires, and other places water can collect. Because of variations in topography, ocean influences and atmospheric processes, temperature and rainfall patterns vary across Puerto Rico and so do dengue virus transmission rates. Using NASA's TRMM (Tropical Rainfall Measuring Mission) satellite for precipitation input, ground-based observations for temperature input, and laboratory confirmed dengue cases reported by the Centers for Disease Control and Prevention for parameter calibration, we modeled dengue transmission at the county level across Puerto Rico from 2010-2013 using a dynamic dengue transmission model that includes interacting vector ecology and epidemiological components. Employing a Monte Carlo approach, we performed ensembles of several thousands of model simulations for each county in order to resolve the model uncertainty arising from using different combinations of parameter values that are not well known. The top 1% of model simulations that best reproduced the reported dengue case data were then analyzed to determine the most important parameters for dengue virus transmission in each county, as well as the relative influence of climate variability on transmission. These results can be used by public health workers to implement dengue control methods that are targeted for specific locations and climate conditions.
Incorporating uncertainty in RADTRAN 6.0 input files.

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

Dennis, Matthew L.; Weiner, Ruth F.; Heames, Terence John

Uncertainty may be introduced into RADTRAN analyses by distributing input parameters. The MELCOR Uncertainty Engine (Gauntt and Erickson, 2004) has been adapted for use in RADTRAN to determine the parameter shape and minimum and maximum of the distribution, to sample on the distribution, and to create an appropriate RADTRAN batch file. Coupling input parameters is not possible in this initial application. It is recommended that the analyst be very familiar with RADTRAN and able to edit or create a RADTRAN input file using a text editor before implementing the RADTRAN Uncertainty Analysis Module. Installation of the MELCOR Uncertainty Engine ismore » required for incorporation of uncertainty into RADTRAN. Gauntt and Erickson (2004) provides installation instructions as well as a description and user guide for the uncertainty engine.« less
Reconstruction of paediatric organ doses from axial CT scans performed in the 1990s - range of doses as input to uncertainty estimates.

PubMed

Olerud, Hilde M; Toft, Benthe; Flatabø, Silje; Jahnen, Andreas; Lee, Choonsik; Thierry-Chef, Isabelle

2016-09-01

To assess the range of doses in paediatric CT scans conducted in the 1990s in Norway as input to an international epidemiology study: the EPI-CT study, http://epi-ct.iarc.fr/ . National Cancer Institute dosimetry system for Computed Tomography (NCICT) program based on pre-calculated organ dose conversion coefficients was used to convert CT Dose Index to organ doses in paediatric CT in the 1990s. Protocols reported from local hospitals in a previous Norwegian CT survey were used as input, presuming these were used without optimization for paediatric patients. Large variations in doses between different scanner models and local scan parameter settings are demonstrated. Small children will receive a factor of 2-3 times higher doses compared with adults if the protocols are not optimized for them. For common CT examinations, the doses to the active bone marrow, breast tissue and brain may have exceeded 30 mGy, 60 mGy and 100 mGy respectively, for the youngest children in the 1990s. The doses children received from non-optimised CT examinations during the 1990s are of such magnitude that they may provide statistically significant effects in the EPI-CT study, but probably do not reflect current practice. • Some organ doses from paediatric CT in the 1990s may have exceeded 100 mGy. • Small children may have received doses 2-3 times higher compared with adults. • Different scanner models varied by a factor of 2-3 in dose to patients. • Different local scan parameter settings gave dose variations of a factor 2-3. • Modern CTs and age-adjusted protocols will give much lower paediatric doses.
Testing to Characterize the Advanced Stirling Radioisotope Generator Engineering Unit

NASA Technical Reports Server (NTRS)

Lewandowski, Edward; Schreiber, Jeffrey

2010-01-01

The Advanced Stirling Radioisotope Generator (ASRG), a high efficiency generator, is being considered for space missions. Lockheed Martin designed and fabricated an engineering unit (EU), the ASRG EU, under contract to the Department of Energy. This unit is currently undergoing extended operation testing at the NASA Glenn Research Center to generate performance data and validate life and reliability predictions for the generator and the Stirling convertors. It has also undergone performance tests to characterize generator operation while varying control parameters and system inputs. This paper summarizes and explains test results in the context of designing operating strategies for the generator during a space mission and notes expected differences between the EU performance and future generators.
A Frequency Reconfigurable MIMO Antenna System for Cognitive Radio Applications

NASA Astrophysics Data System (ADS)

Raza, A.; Khan, Muhammad U.; Tahir, Farooq A.

2017-10-01

In this paper, a two element frequency reconfigurable multiple-input-multiple-output (MIMO) antenna system is presented. The proposed antenna consists of miniaturized patch antenna elements, loaded with varactor diodes to achieve frequency reconfigurability. The antenna has bandwidth of 30 MHz and provides a smooth frequency sweep from 2.12 GHz to 2.4 GHz by varying the reverse bias voltage of varactor diode. The antenna is designed on an FR4 substrate and occupies a space of 50×100 × 0.8 mm3. The antenna is analyzed for its far-field characteristics as well as for MIMO performance parameters. Designed antenna showed good performance and is suitable for cognitive radios (CR) applications.
Dry sliding wear behavior of TIG welding clad WC composite coatings

NASA Astrophysics Data System (ADS)

Buytoz, Soner; Ulutan, Mustafa; Yildirim, M. Mustafa

2005-12-01

In this study, melted tungsten carbide powders on the surface of AISI 4340 steel was applied by using tungsten inert gas (TIG) method. It was observed that it has been solidified in different microstructures depending on the production parameters. As a result of microstructure examinations, in the surface modified layers an eutectic and dendrite solidification was observed together with WC, W 2C phases. In the layer produced, the hardness values varied between 950 and 1200 HV. The minimum mass loss was observed in the sample, which was treated in 1.209 mm/s production rate, 0.5 g/s powder feed rate and 13.9 kJ/cm heat input.
Self-tuning control of attitude and momentum management for the Space Station

NASA Technical Reports Server (NTRS)

Shieh, L. S.; Sunkel, J. W.; Yuan, Z. Z.; Zhao, X. M.

1992-01-01

This paper presents a hybrid state-space self-tuning design methodology using dual-rate sampling for suboptimal digital adaptive control of attitude and momentum management for the Space Station. This new hybrid adaptive control scheme combines an on-line recursive estimation algorithm for indirectly identifying the parameters of a continuous-time system from the available fast-rate sampled data of the inputs and states and a controller synthesis algorithm for indirectly finding the slow-rate suboptimal digital controller from the designed optimal analog controller. The proposed method enables the development of digitally implementable control algorithms for the robust control of Space Station Freedom with unknown environmental disturbances and slowly time-varying dynamics.
Mixing-model Sensitivity to Initial Conditions in Hydrodynamic Predictions

NASA Astrophysics Data System (ADS)

Bigelow, Josiah; Silva, Humberto; Truman, C. Randall; Vorobieff, Peter

2017-11-01

Amagat and Dalton mixing-models were studied to compare their thermodynamic prediction of shock states. Numerical simulations with the Sandia National Laboratories shock hydrodynamic code CTH modeled University of New Mexico (UNM) shock tube laboratory experiments shocking a 1:1 molar mixture of helium (He) and sulfur hexafluoride (SF6) . Five input parameters were varied for sensitivity analysis: driver section pressure, driver section density, test section pressure, test section density, and mixture ratio (mole fraction). We show via incremental Latin hypercube sampling (LHS) analysis that significant differences exist between Amagat and Dalton mixing-model predictions. The differences observed in predicted shock speeds, temperatures, and pressures grow more pronounced with higher shock speeds. Supported by NNSA Grant DE-0002913.

Fuzzy Stochastic Petri Nets for Modeling Biological Systems with Uncertain Kinetic Parameters

PubMed Central

Liu, Fei; Heiner, Monika; Yang, Ming

2016-01-01

Stochastic Petri nets (SPNs) have been widely used to model randomness which is an inherent feature of biological systems. However, for many biological systems, some kinetic parameters may be uncertain due to incomplete, vague or missing kinetic data (often called fuzzy uncertainty), or naturally vary, e.g., between different individuals, experimental conditions, etc. (often called variability), which has prevented a wider application of SPNs that require accurate parameters. Considering the strength of fuzzy sets to deal with uncertain information, we apply a specific type of stochastic Petri nets, fuzzy stochastic Petri nets (FSPNs), to model and analyze biological systems with uncertain kinetic parameters. FSPNs combine SPNs and fuzzy sets, thereby taking into account both randomness and fuzziness of biological systems. For a biological system, SPNs model the randomness, while fuzzy sets model kinetic parameters with fuzzy uncertainty or variability by associating each parameter with a fuzzy number instead of a crisp real value. We introduce a simulation-based analysis method for FSPNs to explore the uncertainties of outputs resulting from the uncertainties associated with input parameters, which works equally well for bounded and unbounded models. We illustrate our approach using a yeast polarization model having an infinite state space, which shows the appropriateness of FSPNs in combination with simulation-based analysis for modeling and analyzing biological systems with uncertain information. PMID:26910830
Systematic development of technical textiles

NASA Astrophysics Data System (ADS)

Beer, M.; Schrank, V.; Gloy, Y.-S.; Gries, T.

2016-07-01

Technical textiles are used in various fields of applications, ranging from small scale (e.g. medical applications) to large scale products (e.g. aerospace applications). The development of new products is often complex and time consuming, due to multiple interacting parameters. These interacting parameters are production process related and also a result of the textile structure and used material. A huge number of iteration steps are necessary to adjust the process parameter to finalize the new fabric structure. A design method is developed to support the systematic development of technical textiles and to reduce iteration steps. The design method is subdivided into six steps, starting from the identification of the requirements. The fabric characteristics vary depending on the field of application. If possible, benchmarks are tested. A suitable fabric production technology needs to be selected. The aim of the method is to support a development team within the technology selection without restricting the textile developer. After a suitable technology is selected, the transformation and correlation between input and output parameters follows. This generates the information for the production of the structure. Afterwards, the first prototype can be produced and tested. The resulting characteristics are compared with the initial product requirements.
Self-tuning control algorithm design for vehicle adaptive cruise control system through real-time estimation of vehicle parameters and road grade

NASA Astrophysics Data System (ADS)

Marzbanrad, Javad; Tahbaz-zadeh Moghaddam, Iman

2016-09-01

The main purpose of this paper is to design a self-tuning control algorithm for an adaptive cruise control (ACC) system that can adapt its behaviour to variations of vehicle dynamics and uncertain road grade. To this aim, short-time linear quadratic form (STLQF) estimation technique is developed so as to track simultaneously the trend of the time-varying parameters of vehicle longitudinal dynamics with a small delay. These parameters are vehicle mass, road grade and aerodynamic drag-area coefficient. Next, the values of estimated parameters are used to tune the throttle and brake control inputs and to regulate the throttle/brake switching logic that governs the throttle and brake switching. The performance of the designed STLQF-based self-tuning control (STLQF-STC) algorithm for ACC system is compared with the conventional method based on fixed control structure regarding the speed/distance tracking control modes. Simulation results show that the proposed control algorithm improves the performance of throttle and brake controllers, providing more comfort while travelling, enhancing driving safety and giving a satisfactory performance in the presence of different payloads and road grade variations.
On Time Delay Margin Estimation for Adaptive Control and Optimal Control Modification

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.

2011-01-01

This paper presents methods for estimating time delay margin for adaptive control of input delay systems with almost linear structured uncertainty. The bounded linear stability analysis method seeks to represent an adaptive law by a locally bounded linear approximation within a small time window. The time delay margin of this input delay system represents a local stability measure and is computed analytically by three methods: Pade approximation, Lyapunov-Krasovskii method, and the matrix measure method. These methods are applied to the standard model-reference adaptive control, s-modification adaptive law, and optimal control modification adaptive law. The windowing analysis results in non-unique estimates of the time delay margin since it is dependent on the length of a time window and parameters which vary from one time window to the next. The optimal control modification adaptive law overcomes this limitation in that, as the adaptive gain tends to infinity and if the matched uncertainty is linear, then the closed-loop input delay system tends to a LTI system. A lower bound of the time delay margin of this system can then be estimated uniquely without the need for the windowing analysis. Simulation results demonstrates the feasibility of the bounded linear stability method for time delay margin estimation.
Linear Parameter Varying Identification of Dynamic Joint Stiffness during Time-Varying Voluntary Contractions

PubMed Central

Golkar, Mahsa A.; Sobhani Tehrani, Ehsan; Kearney, Robert E.

2017-01-01

Dynamic joint stiffness is a dynamic, nonlinear relationship between the position of a joint and the torque acting about it, which can be used to describe the biomechanics of the joint and associated limb(s). This paper models and quantifies changes in ankle dynamic stiffness and its individual elements, intrinsic and reflex stiffness, in healthy human subjects during isometric, time-varying (TV) contractions of the ankle plantarflexor muscles. A subspace, linear parameter varying, parallel-cascade (LPV-PC) algorithm was used to identify the model from measured input position perturbations and output torque data using voluntary torque as the LPV scheduling variable (SV). Monte-Carlo simulations demonstrated that the algorithm is accurate, precise, and robust to colored measurement noise. The algorithm was then used to examine stiffness changes associated with TV isometric contractions. The SV was estimated from the Soleus EMG using a Hammerstein model of EMG-torque dynamics identified from unperturbed trials. The LPV-PC algorithm identified (i) a non-parametric LPV impulse response function (LPV IRF) for intrinsic stiffness and (ii) a LPV-Hammerstein model for reflex stiffness consisting of a LPV static nonlinearity followed by a time-invariant state-space model of reflex dynamics. The results demonstrated that: (a) intrinsic stiffness, in particular ankle elasticity, increased significantly and monotonically with activation level; (b) the gain of the reflex pathway increased from rest to around 10–20% of subject's MVC and then declined; and (c) the reflex dynamics were second order. These findings suggest that in healthy human ankle, reflex stiffness contributes most at low muscle contraction levels, whereas, intrinsic contributions monotonically increase with activation level. PMID:28579954
Neural-adaptive control of single-master-multiple-slaves teleoperation for coordinated multiple mobile manipulators with time-varying communication delays and input uncertainties.

PubMed

Li, Zhijun; Su, Chun-Yi

2013-09-01

In this paper, adaptive neural network control is investigated for single-master-multiple-slaves teleoperation in consideration of time delays and input dead-zone uncertainties for multiple mobile manipulators carrying a common object in a cooperative manner. Firstly, concise dynamics of teleoperation systems consisting of a single master robot, multiple coordinated slave robots, and the object are developed in the task space. To handle asymmetric time-varying delays in communication channels and unknown asymmetric input dead zones, the nonlinear dynamics of the teleoperation system are transformed into two subsystems through feedback linearization: local master or slave dynamics including the unknown input dead zones and delayed dynamics for the purpose of synchronization. Then, a model reference neural network control strategy based on linear matrix inequalities (LMI) and adaptive techniques is proposed. The developed control approach ensures that the defined tracking errors converge to zero whereas the coordination internal force errors remain bounded and can be made arbitrarily small. Throughout this paper, stability analysis is performed via explicit Lyapunov techniques under specific LMI conditions. The proposed adaptive neural network control scheme is robust against motion disturbances, parametric uncertainties, time-varying delays, and input dead zones, which is validated by simulation studies.
Comparison of Two Global Sensitivity Analysis Methods for Hydrologic Modeling over the Columbia River Basin

NASA Astrophysics Data System (ADS)

Hameed, M.; Demirel, M. C.; Moradkhani, H.

2015-12-01

Global Sensitivity Analysis (GSA) approach helps identify the effectiveness of model parameters or inputs and thus provides essential information about the model performance. In this study, the effects of the Sacramento Soil Moisture Accounting (SAC-SMA) model parameters, forcing data, and initial conditions are analysed by using two GSA methods: Sobol' and Fourier Amplitude Sensitivity Test (FAST). The simulations are carried out over five sub-basins within the Columbia River Basin (CRB) for three different periods: one-year, four-year, and seven-year. Four factors are considered and evaluated by using the two sensitivity analysis methods: the simulation length, parameter range, model initial conditions, and the reliability of the global sensitivity analysis methods. The reliability of the sensitivity analysis results is compared based on 1) the agreement between the two sensitivity analysis methods (Sobol' and FAST) in terms of highlighting the same parameters or input as the most influential parameters or input and 2) how the methods are cohered in ranking these sensitive parameters under the same conditions (sub-basins and simulation length). The results show the coherence between the Sobol' and FAST sensitivity analysis methods. Additionally, it is found that FAST method is sufficient to evaluate the main effects of the model parameters and inputs. Another conclusion of this study is that the smaller parameter or initial condition ranges, the more consistency and coherence between the sensitivity analysis methods results.
Improved Strength and Damage Modeling of Geologic Materials

NASA Astrophysics Data System (ADS)

Stewart, Sarah; Senft, Laurel

2007-06-01

Collisions and impact cratering events are important processes in the evolution of planetary bodies. The time and length scales of planetary collisions, however, are inaccessible in the laboratory and require the use of shock physics codes. We present the results from a new rheological model for geological materials implemented in the CTH code [1]. The `ROCK' model includes pressure, temperature, and damage effects on strength, as well as acoustic fluidization during impact crater collapse. We demonstrate that the model accurately reproduces final crater shapes, tensile cracking, and damaged zones from laboratory to planetary scales. The strength model requires basic material properties; hence, the input parameters may be benchmarked to laboratory results and extended to planetary collision events. We show the effects of varying material strength parameters, which are dependent on both scale and strain rate, and discuss choosing appropriate parameters for laboratory and planetary situations. The results are a significant improvement in models of continuum rock deformation during large scale impact events. [1] Senft, L. E., Stewart, S. T. Modeling Impact Cratering in Layered Surfaces, J. Geophys. Res., submitted.
Methods and systems for determining angular orientation of a drill string

DOEpatents

Cobern, Martin E.

2010-03-23

Preferred methods and systems generate a control input based on a periodically-varying characteristic associated with the rotation of a drill string. The periodically varying characteristic can be correlated with the magnetic tool face and gravity tool face of a rotating component of the drill string, so that the control input can be used to initiate a response in the rotating component as a function of gravity tool face.
Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering

PubMed Central

Havlicek, Martin; Friston, Karl J.; Jan, Jiri; Brazdil, Milan; Calhoun, Vince D.

2011-01-01

This paper presents a new approach to inverting (fitting) models of coupled dynamical systems based on state-of-the-art (cubature) Kalman filtering. Crucially, this inversion furnishes posterior estimates of both the hidden states and parameters of a system, including any unknown exogenous input. Because the underlying generative model is formulated in continuous time (with a discrete observation process) it can be applied to a wide variety of models specified with either ordinary or stochastic differential equations. These are an important class of models that are particularly appropriate for biological time-series, where the underlying system is specified in terms of kinetics or dynamics (i.e., dynamic causal models). We provide comparative evaluations with generalized Bayesian filtering (dynamic expectation maximization) and demonstrate marked improvements in accuracy and computational efficiency. We compare the schemes using a series of difficult (nonlinear) toy examples and conclude with a special focus on hemodynamic models of evoked brain responses in fMRI. Our scheme promises to provide a significant advance in characterizing the functional architectures of distributed neuronal systems, even in the absence of known exogenous (experimental) input; e.g., resting state fMRI studies and spontaneous fluctuations in electrophysiological studies. Importantly, unlike current Bayesian filters (e.g. DEM), our scheme provides estimates of time-varying parameters, which we will exploit in future work on the adaptation and enabling of connections in the brain. PMID:21396454
Prediction of fracture healing under axial loading, shear loading and bending is possible using distortional and dilatational strains as determining mechanical stimuli.

PubMed

Steiner, Malte; Claes, Lutz; Ignatius, Anita; Niemeyer, Frank; Simon, Ulrich; Wehner, Tim

2013-09-06

Numerical models of secondary fracture healing are based on mechanoregulatory algorithms that use distortional strain alone or in combination with either dilatational strain or fluid velocity as determining stimuli for tissue differentiation and development. Comparison of these algorithms has previously suggested that healing processes under torsional rotational loading can only be properly simulated by considering fluid velocity and deviatoric strain as the regulatory stimuli. We hypothesize that sufficient calibration on uncertain input parameters will enhance our existing model, which uses distortional and dilatational strains as determining stimuli, to properly simulate fracture healing under various loading conditions including also torsional rotation. Therefore, we minimized the difference between numerically simulated and experimentally measured courses of interfragmentary movements of two axial compressive cases and two shear load cases (torsional and translational) by varying several input parameter values within their predefined bounds. The calibrated model was then qualitatively evaluated on the ability to predict physiological changes of spatial and temporal tissue distributions, based on respective in vivo data. Finally, we corroborated the model on five additional axial compressive and one asymmetrical bending load case. We conclude that our model, using distortional and dilatational strains as determining stimuli, is able to simulate fracture-healing processes not only under axial compression and torsional rotation but also under translational shear and asymmetrical bending loading conditions.
Rapid Debris Analysis Project Task 3 Final Report - Sensitivity of Fallout to Source Parameters, Near-Detonation Environment Material Properties, Topography, and Meteorology

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

Goldstein, Peter

2014-01-24

This report describes the sensitivity of predicted nuclear fallout to a variety of model input parameters, including yield, height of burst, particle and activity size distribution parameters, wind speed, wind direction, topography, and precipitation. We investigate sensitivity over a wide but plausible range of model input parameters. In addition, we investigate a specific example with a relatively narrow range to illustrate the potential for evaluating uncertainties in predictions when there are more precise constraints on model parameters.
Estimating Unsaturated Zone N Fluxes and Travel Times to Groundwater at Watershed Scales

NASA Astrophysics Data System (ADS)

Liao, L.; Green, C. T.; Harter, T.; Nolan, B. T.; Juckem, P. F.; Shope, C. L.

2016-12-01

Nitrate concentrations in groundwater vary at spatial and temporal scales. Local variability depends on soil properties, unsaturated zone properties, hydrology, reactivity, and other factors. For example, the travel time in the unsaturated zone can cause contaminant responses in aquifers to lag behind changes in N inputs at the land surface, and variable leaching-fractions of applied N fertilizer to groundwater can elevate (or reduce) concentrations in groundwater. In this study, we apply the vertical flux model (VFM) (Liao et al., 2012) to address the importance of travel time of N in the unsaturated zone and its fraction leached from the unsaturated zone to groundwater. The Fox-Wolf-Peshtigo basins, including 34 out of 72 counties in Wisconsin, were selected as the study area. Simulated concentrations of NO3-, N2 from denitrification, O2, and environmental tracers of groundwater age were matched to observations by adjusting parameters for recharge rate, unsaturated zone travel time, fractions of N inputs leached to groundwater, O2 reduction rate, O2 threshold for denitrification, denitrification rate, and dispersivity. Correlations between calibrated parameters and GIS parameters (land use, drainage class and soil properties etc.) were evaluated. Model results revealed a median of recharge rate of 0.11 m/yr, which is comparable with results from three independent estimates of recharge rates in the study area. The unsaturated travel times ranged from 0.2 yr to 25 yr with median of 6.8 yr. The correlation analysis revealed that relationships between VFM parameters and landscape characteristics (GIS parameters) were consistent with expected relationships. Fraction N leached was lower in the vicinity of wetlands and greater in the vicinity of crop lands. Faster unsaturated zone transport in forested areas was consistent with results of studies showing rapid vertical transport in forested soils. Reaction rate coefficients correlated with chemical indicators such as Fe and P concentrations. Overall, the results demonstrate applicability of the VFM at a regional scale, as well as potential to generate N transport estimates continuously across regions based on statistical relationships between VFM model parameters and GIS parameters.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Maurer, K. D.; Bohrer, G.; Kenny, W. T.

Surface roughness parameters, namely the roughness length and displacement height, are an integral input used to model surface fluxes. However, most models assume these parameters to be a fixed property of plant functional type and disregard the governing structural heterogeneity and dynamics. In this study, we use large-eddy simulations to explore, in silico, the effects of canopy-structure characteristics on surface roughness parameters. We performed a virtual experiment to test the sensitivity of resolved surface roughness to four axes of canopy structure: (1) leaf area index, (2) the vertical profile of leaf density, (3) canopy height, and (4) canopy gap fraction.more » We found roughness parameters to be highly variable, but uncovered positive relationships between displacement height and maximum canopy height, aerodynamic canopy height and maximum canopy height and leaf area index, and eddy-penetration depth and gap fraction. We also found negative relationships between aerodynamic canopy height and gap fraction, as well as between eddy-penetration depth and maximum canopy height and leaf area index. We generalized our model results into a virtual "biometric" parameterization that relates roughness length and displacement height to canopy height, leaf area index, and gap fraction. Using a decade of wind and canopy-structure observations in a site in Michigan, we tested the effectiveness of our model-driven biometric parameterization approach in predicting the friction velocity over heterogeneous and disturbed canopies. We compared the accuracy of these predictions with the friction-velocity predictions obtained from the common simple approximation related to canopy height, the values calculated with large-eddy simulations of the explicit canopy structure as measured by airborne and ground-based lidar, two other parameterization approaches that utilize varying canopy-structure inputs, and the annual and decadal means of the surface roughness parameters at the site from meteorological observations. We found that the classical representation of constant roughness parameters (in space and time) as a fraction of canopy height performed relatively well. Nonetheless, of the approaches we tested, most of the empirical approaches that incorporate seasonal and interannual variation of roughness length and displacement height as a function of the dynamics of canopy structure produced more precise and less biased estimates for friction velocity than models with temporally invariable parameters.« less
Network and neuronal membrane properties in hybrid networks reciprocally regulate selectivity to rapid thalamocortical inputs.

PubMed

Pesavento, Michael J; Pinto, David J

2012-11-01

Rapidly changing environments require rapid processing from sensory inputs. Varying deflection velocities of a rodent's primary facial vibrissa cause varying temporal neuronal activity profiles within the ventral posteromedial thalamic nucleus. Local neuron populations in a single somatosensory layer 4 barrel transform sparsely coded input into a spike count based on the input's temporal profile. We investigate this transformation by creating a barrel-like hybrid network with whole cell recordings of in vitro neurons from a cortical slice preparation, embedding the biological neuron in the simulated network by presenting virtual synaptic conductances via a conductance clamp. Utilizing the hybrid network, we examine the reciprocal network properties (local excitatory and inhibitory synaptic convergence) and neuronal membrane properties (input resistance) by altering the barrel population response to diverse thalamic input. In the presence of local network input, neurons are more selective to thalamic input timing; this arises from strong feedforward inhibition. Strongly inhibitory (damping) network regimes are more selective to timing and less selective to the magnitude of input but require stronger initial input. Input selectivity relies heavily on the different membrane properties of excitatory and inhibitory neurons. When inhibitory and excitatory neurons had identical membrane properties, the sensitivity of in vitro neurons to temporal vs. magnitude features of input was substantially reduced. Increasing the mean leak conductance of the inhibitory cells decreased the network's temporal sensitivity, whereas increasing excitatory leak conductance enhanced magnitude sensitivity. Local network synapses are essential in shaping thalamic input, and differing membrane properties of functional classes reciprocally modulate this effect.
The dynamics of integrate-and-fire: mean versus variance modulations and dependence on baseline parameters.

PubMed

Pressley, Joanna; Troyer, Todd W

2011-05-01

The leaky integrate-and-fire (LIF) is the simplest neuron model that captures the essential properties of neuronal signaling. Yet common intuitions are inadequate to explain basic properties of LIF responses to sinusoidal modulations of the input. Here we examine responses to low and moderate frequency modulations of both the mean and variance of the input current and quantify how these responses depend on baseline parameters. Across parameters, responses to modulations in the mean current are low pass, approaching zero in the limit of high frequencies. For very low baseline firing rates, the response cutoff frequency matches that expected from membrane integration. However, the cutoff shows a rapid, supralinear increase with firing rate, with a steeper increase in the case of lower noise. For modulations of the input variance, the gain at high frequency remains finite. Here, we show that the low-frequency responses depend strongly on baseline parameters and derive an analytic condition specifying the parameters at which responses switch from being dominated by low versus high frequencies. Additionally, we show that the resonant responses for variance modulations have properties not expected for common oscillatory resonances: they peak at frequencies higher than the baseline firing rate and persist when oscillatory spiking is disrupted by high noise. Finally, the responses to mean and variance modulations are shown to have a complementary dependence on baseline parameters at higher frequencies, resulting in responses to modulations of Poisson input rates that are independent of baseline input statistics.
Generalized compliant motion primitive

NASA Technical Reports Server (NTRS)

Backes, Paul G. (Inventor)

1994-01-01

This invention relates to a general primitive for controlling a telerobot with a set of input parameters. The primitive includes a trajectory generator; a teleoperation sensor; a joint limit generator; a force setpoint generator; a dither function generator, which produces telerobot motion inputs in a common coordinate frame for simultaneous combination in sensor summers. Virtual return spring motion input is provided by a restoration spring subsystem. The novel features of this invention include use of a single general motion primitive at a remote site to permit the shared and supervisory control of the robot manipulator to perform tasks via a remotely transferred input parameter set.
Adaptive control of a quadrotor aerial vehicle with input constraints and uncertain parameters

NASA Astrophysics Data System (ADS)

Tran, Trong-Toan; Ge, Shuzhi Sam; He, Wei

2018-05-01

In this paper, we address the problem of adaptive bounded control for the trajectory tracking of a Quadrotor Aerial Vehicle (QAV) while the input saturations and uncertain parameters with the known bounds are simultaneously taken into account. First, to deal with the underactuated property of the QAV model, we decouple and construct the QAV model as a cascaded structure which consists of two fully actuated subsystems. Second, to handle the input constraints and uncertain parameters, we use a combination of the smooth saturation function and smooth projection operator in the control design. Third, to ensure the stability of the overall system of the QAV, we develop the technique for the cascaded system in the presence of both the input constraints and uncertain parameters. Finally, the region of stability of the closed-loop system is constructed explicitly, and our design ensures the asymptotic convergence of the tracking errors to the origin. The simulation results are provided to illustrate the effectiveness of the proposed method.
Real-Time Ensemble Forecasting of Coronal Mass Ejections Using the Wsa-Enlil+Cone Model

NASA Astrophysics Data System (ADS)

Mays, M. L.; Taktakishvili, A.; Pulkkinen, A. A.; Odstrcil, D.; MacNeice, P. J.; Rastaetter, L.; LaSota, J. A.

2014-12-01

Ensemble forecasting of coronal mass ejections (CMEs) provides significant information in that it provides an estimation of the spread or uncertainty in CME arrival time predictions. Real-time ensemble modeling of CME propagation is performed by forecasters at the Space Weather Research Center (SWRC) using the WSA-ENLIL+cone model available at the Community Coordinated Modeling Center (CCMC). To estimate the effect of uncertainties in determining CME input parameters on arrival time predictions, a distribution of n (routinely n=48) CME input parameter sets are generated using the CCMC Stereo CME Analysis Tool (StereoCAT) which employs geometrical triangulation techniques. These input parameters are used to perform n different simulations yielding an ensemble of solar wind parameters at various locations of interest, including a probability distribution of CME arrival times (for hits), and geomagnetic storm strength (for Earth-directed hits). We present the results of ensemble simulations for a total of 38 CME events in 2013-2014. For 28 of the ensemble runs containing hits, the observed CME arrival was within the range of ensemble arrival time predictions for 14 runs (half). The average arrival time prediction was computed for each of the 28 ensembles predicting hits and using the actual arrival time, an average absolute error of 10.0 hours (RMSE=11.4 hours) was found for all 28 ensembles, which is comparable to current forecasting errors. Some considerations for the accuracy of ensemble CME arrival time predictions include the importance of the initial distribution of CME input parameters, particularly the mean and spread. When the observed arrivals are not within the predicted range, this still allows the ruling out of prediction errors caused by tested CME input parameters. Prediction errors can also arise from ambient model parameters such as the accuracy of the solar wind background, and other limitations. Additionally the ensemble modeling sysem was used to complete a parametric event case study of the sensitivity of the CME arrival time prediction to free parameters for ambient solar wind model and CME. The parameter sensitivity study suggests future directions for the system, such as running ensembles using various magnetogram inputs to the WSA model.
Dual-user nonlinear teleoperation subjected to varying time delay and bounded inputs.

PubMed

Zakerimanesh, Amir; Hashemzadeh, Farzad; Ghiasi, Amir Rikhtehgar

2017-05-01

A novel trilateral control architecture for Dual-master/Single-slave teleoperation system with taking account of saturation in actuators, nonlinear dynamics for telemanipulators and bounded varying time delay which affects the transmitted signals in the communication channels, is proposed in this paper. In this research, we will address the stability and desired position coordination problem of trilateral teleoperation system by extension of (nP+D) controller that is used for Single-master/Single-slave teleoperation system. Our proposed controller is weighted summation of nonlinear Proportional plus Damping (nP+D) controller that incorporate gravity compensation and the weights are specified by the dominance factor, which determines the supremacy of each user over the slave robot and over the other user. The asymptotic stability of closed loop dynamics is studied using Lyapunov-Krasovskii functional under conditions on the controller parameters, the actuator saturation characteristics and the maximum values of varying time delays. It is shown that these controllers satisfy the desired position coordination problem in free motion condition. To show the effectiveness of the proposed method, a number of simulations have been conducted on a varying time delay Dual-master/Single-slave teleoperation system using 3-DOF planar robots for each telemanipulator subjected to actuator saturation. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Optimization to Develop Multiple Response Microstructure and Hardness of Ductile Iron Casting by using GRA

NASA Astrophysics Data System (ADS)

Kabnure, Bahubali Bhupal; Shinde, Vasudev Dhondiram; Kolhapure, Rakesh Ramchandra

2018-05-01

Ductile irons are important engineering materials because of its high strength to weight ratio and castability. The ductile iron castings are used widely for automobile applications due to their wide spectrum of property range. Weight reduction is important in automobile to improve its fuel efficiency which can be achieved by thinning down the casting sections without altering its functionality. Generally, automobile castings are having varying section thickness. Varying thickness castings offers different cooling rates while solidification of the casting. The solidification cooling rate decides the final microstructure of the cast components. Cooling rate was found to affect directly the amount of pearlite and ultimately the as cast properties in varying thickness ductile iron castings. In view of this, the automobile impeller casting is selected for study in the present work as it consists of varying section thickness in which small sections are connected to central hub. The casting solidification simulations were performed and analyzed. The solidification cooling rates were analyzed further to correlate the experimental processing parameters. The samples from poured castings were analyzed for microstructure and hardness at different section thickness. Multiple response optimization of microstructure and hardness was carried out by combined Taguchi and Grey Relational Analysis (GRA). Contribution of input variables on the output variables is attained using ANOVA.
Measurand transient signal suppressor

NASA Technical Reports Server (NTRS)

Bozeman, Richard J., Jr. (Inventor)

1994-01-01

A transient signal suppressor for use in a controls system which is adapted to respond to a change in a physical parameter whenever it crosses a predetermined threshold value in a selected direction of increasing or decreasing values with respect to the threshold value and is sustained for a selected discrete time interval is presented. The suppressor includes a sensor transducer for sensing the physical parameter and generating an electrical input signal whenever the sensed physical parameter crosses the threshold level in the selected direction. A manually operated switch is provided for adapting the suppressor to produce an output drive signal whenever the physical parameter crosses the threshold value in the selected direction of increasing or decreasing values. A time delay circuit is selectively adjustable for suppressing the transducer input signal for a preselected one of a plurality of available discrete suppression time and producing an output signal only if the input signal is sustained for a time greater than the selected suppression time. An electronic gate is coupled to receive the transducer input signal and the timer output signal and produce an output drive signal for energizing a control relay whenever the transducer input is a non-transient signal which is sustained beyond the selected time interval.
Comparison between different sets of suspension parameters and introduction of new modified skyhook control strategy incorporating varying road condition

NASA Astrophysics Data System (ADS)

Abul Kashem, Saad Bin; Ektesabi, Mehran; Nagarajah, Romesh

2012-07-01

This study examines the uncertainties in modelling a quarter car suspension system caused by the effect of different sets of suspension parameters of a corresponding mathematical model. To overcome this problem, 11 sets of identified parameters of a suspension system have been compared, taken from the most recent published work. From this investigation, a set of parameters were chosen which showed a better performance than others in respect of peak amplitude and settling time. These chosen parameters were then used to investigate the performance of a new modified continuous skyhook control strategy with adaptive gain that dictates the vehicle's semi-active suspension system. The proposed system first captures the road profile input over a certain period. Then it calculates the best possible value of the skyhook gain (SG) for the subsequent process. Meanwhile the system is controlled according to the new modified skyhook control law using an initial or previous value of the SG. In this study, the proposed suspension system is compared with passive and other recently reported skyhook controlled semi-active suspension systems. Its performances have been evaluated in terms of ride comfort and road handling performance. The model has been validated in accordance with the international standards of admissible acceleration levels ISO2631 and human vibration perception.
Prediction of Tensile Strength of Friction Stir Weld Joints with Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural Network

NASA Technical Reports Server (NTRS)

Dewan, Mohammad W.; Huggett, Daniel J.; Liao, T. Warren; Wahab, Muhammad A.; Okeil, Ayman M.

2015-01-01

Friction-stir-welding (FSW) is a solid-state joining process where joint properties are dependent on welding process parameters. In the current study three critical process parameters including spindle speed (??), plunge force (????), and welding speed (??) are considered key factors in the determination of ultimate tensile strength (UTS) of welded aluminum alloy joints. A total of 73 weld schedules were welded and tensile properties were subsequently obtained experimentally. It is observed that all three process parameters have direct influence on UTS of the welded joints. Utilizing experimental data, an optimized adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict UTS of FSW joints. A total of 1200 models were developed by varying the number of membership functions (MFs), type of MFs, and combination of four input variables (??,??,????,??????) utilizing a MATLAB platform. Note EFI denotes an empirical force index derived from the three process parameters. For comparison, optimized artificial neural network (ANN) models were also developed to predict UTS from FSW process parameters. By comparing ANFIS and ANN predicted results, it was found that optimized ANFIS models provide better results than ANN. This newly developed best ANFIS model could be utilized for prediction of UTS of FSW joints.
Variable input observer for state estimation of high-rate dynamics

NASA Astrophysics Data System (ADS)

Hong, Jonathan; Cao, Liang; Laflamme, Simon; Dodson, Jacob

2017-04-01

High-rate systems operating in the 10 μs to 10 ms timescale are likely to experience damaging effects due to rapid environmental changes (e.g., turbulence, ballistic impact). Some of these systems could benefit from real-time state estimation to enable their full potential. Examples of such systems include blast mitigation strategies, automotive airbag technologies, and hypersonic vehicles. Particular challenges in high-rate state estimation include: 1) complex time varying nonlinearities of system (e.g. noise, uncertainty, and disturbance); 2) rapid environmental changes; 3) requirement of high convergence rate. Here, we propose using a Variable Input Observer (VIO) concept to vary the input space as the event unfolds. When systems experience high-rate dynamics, rapid changes in the system occur. To investigate the VIO's potential, a VIO-based neuro-observer is constructed and studied using experimental data collected from a laboratory impact test. Results demonstrate that the input space is unique to different impact conditions, and that adjusting the input space throughout the dynamic event produces better estimations than using a traditional fixed input space strategy.
A Sensitivity Analysis Method to Study the Behavior of Complex Process-based Models

NASA Astrophysics Data System (ADS)

Brugnach, M.; Neilson, R.; Bolte, J.

2001-12-01

The use of process-based models as a tool for scientific inquiry is becoming increasingly relevant in ecosystem studies. Process-based models are artificial constructs that simulate the system by mechanistically mimicking the functioning of its component processes. Structurally, a process-based model can be characterized, in terms of its processes and the relationships established among them. Each process comprises a set of functional relationships among several model components (e.g., state variables, parameters and input data). While not encoded explicitly, the dynamics of the model emerge from this set of components and interactions organized in terms of processes. It is the task of the modeler to guarantee that the dynamics generated are appropriate and semantically equivalent to the phenomena being modeled. Despite the availability of techniques to characterize and understand model behavior, they do not suffice to completely and easily understand how a complex process-based model operates. For example, sensitivity analysis studies model behavior by determining the rate of change in model output as parameters or input data are varied. One of the problems with this approach is that it considers the model as a "black box", and it focuses on explaining model behavior by analyzing the relationship input-output. Since, these models have a high degree of non-linearity, understanding how the input affects an output can be an extremely difficult task. Operationally, the application of this technique may constitute a challenging task because complex process-based models are generally characterized by a large parameter space. In order to overcome some of these difficulties, we propose a method of sensitivity analysis to be applicable to complex process-based models. This method focuses sensitivity analysis at the process level, and it aims to determine how sensitive the model output is to variations in the processes. Once the processes that exert the major influence in the output are identified, the causes of its variability can be found. Some of the advantages of this approach are that it reduces the dimensionality of the search space, it facilitates the interpretation of the results and it provides information that allows exploration of uncertainty at the process level, and how it might affect model output. We present an example using the vegetation model BIOME-BGC.
RIPL - Reference Input Parameter Library for Calculation of Nuclear Reactions and Nuclear Data Evaluations

NASA Astrophysics Data System (ADS)

Capote, R.; Herman, M.; Obložinský, P.; Young, P. G.; Goriely, S.; Belgya, T.; Ignatyuk, A. V.; Koning, A. J.; Hilaire, S.; Plujko, V. A.; Avrigeanu, M.; Bersillon, O.; Chadwick, M. B.; Fukahori, T.; Ge, Zhigang; Han, Yinlu; Kailas, S.; Kopecky, J.; Maslov, V. M.; Reffo, G.; Sin, M.; Soukhovitskii, E. Sh.; Talou, P.

2009-12-01

We describe the physics and data included in the Reference Input Parameter Library, which is devoted to input parameters needed in calculations of nuclear reactions and nuclear data evaluations. Advanced modelling codes require substantial numerical input, therefore the International Atomic Energy Agency (IAEA) has worked extensively since 1993 on a library of validated nuclear-model input parameters, referred to as the Reference Input Parameter Library (RIPL). A final RIPL coordinated research project (RIPL-3) was brought to a successful conclusion in December 2008, after 15 years of challenging work carried out through three consecutive IAEA projects. The RIPL-3 library was released in January 2009, and is available on the Web through http://www-nds.iaea.org/RIPL-3/. This work and the resulting database are extremely important to theoreticians involved in the development and use of nuclear reaction modelling (ALICE, EMPIRE, GNASH, UNF, TALYS) both for theoretical research and nuclear data evaluations. The numerical data and computer codes included in RIPL-3 are arranged in seven segments: MASSES contains ground-state properties of nuclei for about 9000 nuclei, including three theoretical predictions of masses and the evaluated experimental masses of Audi et al. (2003). DISCRETE LEVELS contains 117 datasets (one for each element) with all known level schemes, electromagnetic and γ-ray decay probabilities available from ENSDF in October 2007. NEUTRON RESONANCES contains average resonance parameters prepared on the basis of the evaluations performed by Ignatyuk and Mughabghab. OPTICAL MODEL contains 495 sets of phenomenological optical model parameters defined in a wide energy range. When there are insufficient experimental data, the evaluator has to resort to either global parameterizations or microscopic approaches. Radial density distributions to be used as input for microscopic calculations are stored in the MASSES segment. LEVEL DENSITIES contains phenomenological parameterizations based on the modified Fermi gas and superfluid models and microscopic calculations which are based on a realistic microscopic single-particle level scheme. Partial level densities formulae are also recommended. All tabulated total level densities are consistent with both the recommended average neutron resonance parameters and discrete levels. GAMMA contains parameters that quantify giant resonances, experimental gamma-ray strength functions and methods for calculating gamma emission in statistical model codes. The experimental GDR parameters are represented by Lorentzian fits to the photo-absorption cross sections for 102 nuclides ranging from 51V to 239Pu. FISSION includes global prescriptions for fission barriers and nuclear level densities at fission saddle points based on microscopic HFB calculations constrained by experimental fission cross sections.
RIPL - Reference Input Parameter Library for Calculation of Nuclear Reactions and Nuclear Data Evaluations

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

Capote, R.; Herman, M.; Oblozinsky, P.

We describe the physics and data included in the Reference Input Parameter Library, which is devoted to input parameters needed in calculations of nuclear reactions and nuclear data evaluations. Advanced modelling codes require substantial numerical input, therefore the International Atomic Energy Agency (IAEA) has worked extensively since 1993 on a library of validated nuclear-model input parameters, referred to as the Reference Input Parameter Library (RIPL). A final RIPL coordinated research project (RIPL-3) was brought to a successful conclusion in December 2008, after 15 years of challenging work carried out through three consecutive IAEA projects. The RIPL-3 library was released inmore » January 2009, and is available on the Web through (http://www-nds.iaea.org/RIPL-3/). This work and the resulting database are extremely important to theoreticians involved in the development and use of nuclear reaction modelling (ALICE, EMPIRE, GNASH, UNF, TALYS) both for theoretical research and nuclear data evaluations. The numerical data and computer codes included in RIPL-3 are arranged in seven segments: MASSES contains ground-state properties of nuclei for about 9000 nuclei, including three theoretical predictions of masses and the evaluated experimental masses of Audi et al. (2003). DISCRETE LEVELS contains 117 datasets (one for each element) with all known level schemes, electromagnetic and {gamma}-ray decay probabilities available from ENSDF in October 2007. NEUTRON RESONANCES contains average resonance parameters prepared on the basis of the evaluations performed by Ignatyuk and Mughabghab. OPTICAL MODEL contains 495 sets of phenomenological optical model parameters defined in a wide energy range. When there are insufficient experimental data, the evaluator has to resort to either global parameterizations or microscopic approaches. Radial density distributions to be used as input for microscopic calculations are stored in the MASSES segment. LEVEL DENSITIES contains phenomenological parameterizations based on the modified Fermi gas and superfluid models and microscopic calculations which are based on a realistic microscopic single-particle level scheme. Partial level densities formulae are also recommended. All tabulated total level densities are consistent with both the recommended average neutron resonance parameters and discrete levels. GAMMA contains parameters that quantify giant resonances, experimental gamma-ray strength functions and methods for calculating gamma emission in statistical model codes. The experimental GDR parameters are represented by Lorentzian fits to the photo-absorption cross sections for 102 nuclides ranging from {sup 51}V to {sup 239}Pu. FISSION includes global prescriptions for fission barriers and nuclear level densities at fission saddle points based on microscopic HFB calculations constrained by experimental fission cross sections.« less
RIPL-Reference Input Parameter Library for Calculation of Nuclear Reactions and Nuclear Data Evaluations

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

Capote, R.; Herman, M.; Capote,R.

We describe the physics and data included in the Reference Input Parameter Library, which is devoted to input parameters needed in calculations of nuclear reactions and nuclear data evaluations. Advanced modelling codes require substantial numerical input, therefore the International Atomic Energy Agency (IAEA) has worked extensively since 1993 on a library of validated nuclear-model input parameters, referred to as the Reference Input Parameter Library (RIPL). A final RIPL coordinated research project (RIPL-3) was brought to a successful conclusion in December 2008, after 15 years of challenging work carried out through three consecutive IAEA projects. The RIPL-3 library was released inmore » January 2009, and is available on the Web through http://www-nds.iaea.org/RIPL-3/. This work and the resulting database are extremely important to theoreticians involved in the development and use of nuclear reaction modelling (ALICE, EMPIRE, GNASH, UNF, TALYS) both for theoretical research and nuclear data evaluations. The numerical data and computer codes included in RIPL-3 are arranged in seven segments: MASSES contains ground-state properties of nuclei for about 9000 nuclei, including three theoretical predictions of masses and the evaluated experimental masses of Audi et al. (2003). DISCRETE LEVELS contains 117 datasets (one for each element) with all known level schemes, electromagnetic and {gamma}-ray decay probabilities available from ENSDF in October 2007. NEUTRON RESONANCES contains average resonance parameters prepared on the basis of the evaluations performed by Ignatyuk and Mughabghab. OPTICAL MODEL contains 495 sets of phenomenological optical model parameters defined in a wide energy range. When there are insufficient experimental data, the evaluator has to resort to either global parameterizations or microscopic approaches. Radial density distributions to be used as input for microscopic calculations are stored in the MASSES segment. LEVEL DENSITIES contains phenomenological parameterizations based on the modified Fermi gas and superfluid models and microscopic calculations which are based on a realistic microscopic single-particle level scheme. Partial level densities formulae are also recommended. All tabulated total level densities are consistent with both the recommended average neutron resonance parameters and discrete levels. GAMMA contains parameters that quantify giant resonances, experimental gamma-ray strength functions and methods for calculating gamma emission in statistical model codes. The experimental GDR parameters are represented by Lorentzian fits to the photo-absorption cross sections for 102 nuclides ranging from {sup 51}V to {sup 239}Pu. FISSION includes global prescriptions for fission barriers and nuclear level densities at fission saddle points based on microscopic HFB calculations constrained by experimental fission cross sections.« less
Layerwise Monitoring of the Selective Laser Melting Process by Thermography

NASA Astrophysics Data System (ADS)

Krauss, Harald; Zeugner, Thomas; Zaeh, Michael F.

Selective Laser Melting is utilized to build parts directly from CAD data. In this study layerwise monitoring of the temperature distribution is used to gather information about the process stability and the resulting part quality. The heat distribution varies with different kinds of parameters including scan vector length, laser power, layer thickness and inter-part distance in the job layout. By integration of an off-axis mounted uncooled thermal detector, the solidification as well as the layer deposition are monitored and evaluated. This enables the identification of hot spots in an early stage during the solidification process and helps to avoid process interrupts. Potential quality indicators are derived from spatially resolved measurement data and are correlated to the resulting part properties. A model of heat dissipation is presented based on the measurement of the material response for varying heat input. Current results show the feasibility of process surveillance by thermography for a limited section of the building platform in a commercial system.
UV-B measurements in India

NASA Astrophysics Data System (ADS)

Prasad, B. S. N.; Gayathri, H. B.; Muralikrishnan, N.

1992-01-01

Global UV-B flux (sum of direct and diffuse radiations) data at four wavelengths 280, 290, 300 and 310 nm are recorded at several locations in India as part of Indian Middle Atmosphere Programme (IMAP). The stations have been selected considering distinct geographic features and possible influence of atmospheric aerosols and particulates on the ground reaching UV-B flux. Mysore (12.6°N, 76.6°E) has been selected as a continental station largely free from any industrial pollution and large scale bio-mass burning. An examination of the ground reaching UV-B flux at Mysore shows a marked dirunal and seasonal asymmetry. This can be attributed to the seasonally varying atmospheric aerosols and particulates which influence the scattering of UV-B radiation. The available parameterization models are used to reproduce the experimental UV-B irradiance by varying the input parameters to the model. These results on the dirunal and seasonal variation of global UV-B flux from experiment and models are discussed in this paper.
Exo-Transmit: An Open-Source Code for Calculating Transmission Spectra for Exoplanet Atmospheres of Varied Composition

NASA Astrophysics Data System (ADS)

Kempton, Eliza M.-R.; Lupu, Roxana; Owusu-Asare, Albert; Slough, Patrick; Cale, Bryson

2017-04-01

We present Exo-Transmit, a software package to calculate exoplanet transmission spectra for planets of varied composition. The code is designed to generate spectra of planets with a wide range of atmospheric composition, temperature, surface gravity, and size, and is therefore applicable to exoplanets ranging in mass and size from hot Jupiters down to rocky super-Earths. Spectra can be generated with or without clouds or hazes with options to (1) include an optically thick cloud deck at a user-specified atmospheric pressure or (2) to augment the nominal Rayleigh scattering by a user-specified factor. The Exo-Transmit code is written in C and is extremely easy to use. Typically the user will only need to edit parameters in a single user input file in order to run the code for a planet of their choosing. Exo-Transmit is available publicly on Github with open-source licensing at https://github.com/elizakempton/Exo_Transmit.
Measured performance of a 3 ton LiBr absorption water chiller and its effect on cooling system operation

NASA Technical Reports Server (NTRS)

Namkoong, D.

1976-01-01

A three ton lithium bromide absorption water chiller was tested for a number of conditions involving hot water input, chilled water, and the cooling water. The primary influences on chiller capacity were the hot water inlet temperature and the cooling water inlet temperature. One combination of these two parameters extended the output to as much as 125% of design capacity, but no combination could lower the capacity to below 60% of design. A cooling system was conceptually designed so that it could provide several modes of operation. Such flexibility is needed for any solar cooling system to be able to accommodate the varying solar energy collection and the varying building demand. It was concluded that a three-ton absorption water chiller with the kind of performance that was measured can be incorporated into a cooling system such as that proposed, to provide efficient cooling over the specified ranges of operating conditions.
Measured performance of a 3-ton LiBr absorption water chiller and its effect on cooling system operation

NASA Technical Reports Server (NTRS)

Namkoong, D.

1976-01-01

A 3-ton lithium bromide absorption water chiller was tested for a number of conditions involving hot-water input, chilled water, and the cooling water. The primary influences on chiller capacity were the hot water inlet temperature and the cooling water inlet temperature. One combination of these two parameters extended the output to as much as 125% of design capacity, but no combination could lower the capacity to below 60% of design. A cooling system was conceptually designed so that it could provide several modes of operation. Such flexibility is needed for any solar cooling system to be able to accommodate the varying solar energy collection and the varying building demand. It is concluded that a 3-ton absorption water chiller with the kind of performance that was measured can be incorporated into a cooling system such as that proposed, to provide efficient cooling over the specified ranges of operating conditions.
Evaluation of Uncertainty in Constituent Input Parameters for Modeling the Fate of RDX

DTIC Science & Technology

2015-07-01

exercise was to evaluate the importance of chemical -specific model input parameters, the impacts of their uncertainty, and the potential benefits of... chemical -specific inputs for RDX that were determined to be sensitive with relatively high uncertainty: these included the soil-water linear...Koc for organic chemicals . The EFS values provided for log Koc of RDX were 1.72 and 1.95. OBJECTIVE: TREECS™ (http://el.erdc.usace.army.mil/treecs
A Design of Experiments Approach Defining the Relationships Between Processing and Microstructure for Ti-6Al-4V

NASA Technical Reports Server (NTRS)

Wallace, Terryl A.; Bey, Kim S.; Taminger, Karen M. B.; Hafley, Robert A.

2004-01-01

A study was conducted to evaluate the relative significance of input parameters on Ti- 6Al-4V deposits produced by an electron beam free form fabrication process under development at the NASA Langley Research Center. Five input parameters where chosen (beam voltage, beam current, translation speed, wire feed rate, and beam focus), and a design of experiments (DOE) approach was used to develop a set of 16 experiments to evaluate the relative importance of these parameters on the resulting deposits. Both single-bead and multi-bead stacks were fabricated using 16 combinations, and the resulting heights and widths of the stack deposits were measured. The resulting microstructures were also characterized to determine the impact of these parameters on the size of the melt pool and heat affected zone. The relative importance of each input parameter on the height and width of the multi-bead stacks will be discussed. .
Seasonal variations of nitrogen and phosphorus retention in an agricultural drainage river in East China.

PubMed

Chen, Dingjiang; Lu, Jun; Wang, Hailong; Shen, Yena; Kimberley, Mark O

2010-02-01

Riverine retention decreases loads of nitrogen (N) and phosphorus (P) in running water. It is an important process in nutrient cycling in watersheds. However, temporal riverine nutrient retention capacity varies due to changes in hydrological, ecological, and nutrient inputs into the watershed. Quantitative information of seasonal riverine N and P retention is critical for developing strategies to combat diffuse source pollution and eutrophication in riverine and coastal systems. This study examined seasonal variation of riverine total N (TN) and total P (TP) retention in the ChangLe River, an agricultural drainage river in east China. Water quality, hydrological parameters, and hydrophyte coverage were monitored along the ChangLe River monthly during 2004-2006. Nutrient export loads (including chemical fertilizer, livestock, and domestic sources) entering the river from the catchment area were computed using an export coefficient model based on estimated nutrient sources. Riverine TN and TP retention loads (RNRL and RPRL) were estimated using mass balance calculations. Temporal variations in riverine nutrient retention were analyzed statistically. Estimated annual riverine retention loads ranged from 1,538 to 2,127 t year(-1) for RNRL and from 79.4 to 90.4 t year(-1) for RPRL. Monthly retention loads varied from 6.4 to 300.8 t month(-1) for RNRL and from 1.4 to 15.3 t month(-1) for RPRL. Both RNRL and RPRL increased with river flow, water temperature, hydrophyte coverage, monthly sunshine hours, and total TN and TP inputs. Dissolved oxygen concentration and the pH level of the river water decreased with RNRL and RPRL. Riverine nutrient retention ratios (retention as a percentage of total input) were only related to hydrophyte coverage and monthly sunshine hours. Monthly variations in RNRL and RPRL were functions of TN and TP loads. Riverine nutrient retention capacity varied with environmental conditions. Annual RNRL and RPRL accounted for 30.3-48.3% and 52.5-71.2%, respectively, of total input TN and TP loads in the ChangLe River. Monthly riverine retention ratios were 3.5-88.7% for TN and 20.5-92.6% for TP. Hydrophyte growth and coverage on the river bed is the main cause for seasonal variation in riverine nutrient retention capacity. The total input TN and TP loads were the best indicators of RNRL and RPRL, respectively. High riverine nutrient retention capacity during summer due to hydrophytic growth is favorable to the avoidance of algal bloom in both river systems and coastal water in southeast China. Policies should be developed to strictly control nutrient applications on agricultural lands. Strategies for promoting hydrophyte growth in rivers are desirable for water quality management.
F-18 High Alpha Research Vehicle (HARV) parameter identification flight test maneuvers for optimal input design validation and lateral control effectiveness

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

1995-01-01

Flight test maneuvers are specified for the F-18 High Alpha Research Vehicle (HARV). The maneuvers were designed for open loop parameter identification purposes, specifically for optimal input design validation at 5 degrees angle of attack, identification of individual strake effectiveness at 40 and 50 degrees angle of attack, and study of lateral dynamics and lateral control effectiveness at 40 and 50 degrees angle of attack. Each maneuver is to be realized by applying square wave inputs to specific control effectors using the On-Board Excitation System (OBES). Maneuver descriptions and complete specifications of the time/amplitude points define each input are included, along with plots of the input time histories.
Quantifying uncertainty and sensitivity in sea ice models

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

Urrego Blanco, Jorge Rolando; Hunke, Elizabeth Clare; Urban, Nathan Mark

The Los Alamos Sea Ice model has a number of input parameters for which accurate values are not always well established. We conduct a variance-based sensitivity analysis of hemispheric sea ice properties to 39 input parameters. The method accounts for non-linear and non-additive effects in the model.
Heat Control via Torque Control in Friction Stir Welding

NASA Technical Reports Server (NTRS)

Venable, Richard; Colligan, Kevin; Knapp, Alan

2004-01-01

In a proposed advance in friction stir welding, the torque exerted on the workpiece by the friction stir pin would be measured and controlled in an effort to measure and control the total heat input to the workpiece. The total heat input to the workpiece is an important parameter of any welding process (fusion or friction stir welding). In fusion welding, measurement and control of heat input is a difficult problem. However, in friction stir welding, the basic principle of operation affords the potential of a straightforward solution: Neglecting thermal losses through the pin and the spindle that supports it, the rate of heat input to the workpiece is the product of the torque and the speed of rotation of the friction stir weld pin and, hence, of the spindle. Therefore, if one acquires and suitably processes data on torque and rotation and controls the torque, the rotation, or both, one should be able to control the heat input into the workpiece. In conventional practice in friction stir welding, one uses feedback control of the spindle motor to maintain a constant speed of rotation. According to the proposal, one would not maintain a constant speed of rotation: Instead, one would use feedback control to maintain a constant torque and would measure the speed of rotation while allowing it to vary. The torque exerted on the workpiece would be estimated as the product of (1) the torque-multiplication ratio of the spindle belt and/or gear drive, (2) the force measured by a load cell mechanically coupled to the spindle motor, and (3) the moment arm of the load cell. Hence, the output of the load cell would be used as a feedback signal for controlling the torque (see figure).

Application of artificial neural networks to assess pesticide contamination in shallow groundwater

USGS Publications Warehouse

Sahoo, G.B.; Ray, C.; Mehnert, E.; Keefer, D.A.

2006-01-01

In this study, a feed-forward back-propagation neural network (BPNN) was developed and applied to predict pesticide concentrations in groundwater monitoring wells. Pesticide concentration data are challenging to analyze because they tend to be highly censored. Input data to the neural network included the categorical indices of depth to aquifer material, pesticide leaching class, aquifer sensitivity to pesticide contamination, time (month) of sample collection, well depth, depth to water from land surface, and additional travel distance in the saturated zone (i.e., distance from land surface to midpoint of well screen). The output of the neural network was the total pesticide concentration detected in the well. The model prediction results produced good agreements with observed data in terms of correlation coefficient (R = 0.87) and pesticide detection efficiency (E = 89%), as well as good match between the observed and predicted "class" groups. The relative importance of input parameters to pesticide occurrence in groundwater was examined in terms of R, E, mean error (ME), root mean square error (RMSE), and pesticide occurrence "class" groups by eliminating some key input parameters to the model. Well depth and time of sample collection were the most sensitive input parameters for predicting the pesticide contamination potential of a well. This infers that wells tapping shallow aquifers are more vulnerable to pesticide contamination than those wells tapping deeper aquifers. Pesticide occurrences during post-application months (June through October) were found to be 2.5 to 3 times higher than pesticide occurrences during other months (November through April). The BPNN was used to rank the input parameters with highest potential to contaminate groundwater, including two original and five ancillary parameters. The two original parameters are depth to aquifer material and pesticide leaching class. When these two parameters were the only input parameters for the BPNN, they were not able to predict contamination potential. However, when they were used with other parameters, the predictive performance efficiency of the BPNN in terms of R, E, ME, RMSE, and pesticide occurrence "class" groups increased. Ancillary data include data collected during the study such as well depth and time of sample collection. The BPNN indicated that the ancillary data had more predictive power than the original data. The BPNN results will help researchers identify parameters to improve maps of aquifer sensitivity to pesticide contamination. ?? 2006 Elsevier B.V. All rights reserved.
Fuzzy/Neural Software Estimates Costs of Rocket-Engine Tests

NASA Technical Reports Server (NTRS)

Douglas, Freddie; Bourgeois, Edit Kaminsky

2005-01-01

The Highly Accurate Cost Estimating Model (HACEM) is a software system for estimating the costs of testing rocket engines and components at Stennis Space Center. HACEM is built on a foundation of adaptive-network-based fuzzy inference systems (ANFIS) a hybrid software concept that combines the adaptive capabilities of neural networks with the ease of development and additional benefits of fuzzy-logic-based systems. In ANFIS, fuzzy inference systems are trained by use of neural networks. HACEM includes selectable subsystems that utilize various numbers and types of inputs, various numbers of fuzzy membership functions, and various input-preprocessing techniques. The inputs to HACEM are parameters of specific tests or series of tests. These parameters include test type (component or engine test), number and duration of tests, and thrust level(s) (in the case of engine tests). The ANFIS in HACEM are trained by use of sets of these parameters, along with costs of past tests. Thereafter, the user feeds HACEM a simple input text file that contains the parameters of a planned test or series of tests, the user selects the desired HACEM subsystem, and the subsystem processes the parameters into an estimate of cost(s).
Comparisons of Solar Wind Coupling Parameters with Auroral Energy Deposition Rates

NASA Technical Reports Server (NTRS)

Elsen, R.; Brittnacher, M. J.; Fillingim, M. O.; Parks, G. K.; Germany G. A.; Spann, J. F., Jr.

1997-01-01

Measurement of the global rate of energy deposition in the ionosphere via auroral particle precipitation is one of the primary goals of the Polar UVI program and is an important component of the ISTP program. The instantaneous rate of energy deposition for the entire month of January 1997 has been calculated by applying models to the UVI images and is presented by Fillingim et al. In this session. A number of parameters that predict the rate of coupling of solar wind energy into the magnetosphere have been proposed in the last few decades. Some of these parameters, such as the epsilon parameter of Perrault and Akasofu, depend on the instantaneous values in the solar wind. Other parameters depend on the integrated values of solar wind parameters, especially IMF Bz, e.g. applied flux which predicts the net transfer of magnetic flux to the tail. While these parameters have often been used successfully with substorm studies, their validity in terms of global energy input has not yet been ascertained, largely because data such as that supplied by the ISTP program was lacking. We have calculated these and other energy coupling parameters for January 1997 using solar wind data provided by WIND and other solar wind monitors. The rates of energy input predicted by these parameters are compared to those measured through UVI data and correlations are sought. Whether these parameters are better at providing an instantaneous rate of energy input or an average input over some time period is addressed. We also study if either type of parameter may provide better correlations if a time delay is introduced; if so, this time delay may provide a characteristic time for energy transport in the coupled solar wind-magnetosphere-ionosphere system.
A Bayesian approach to model structural error and input variability in groundwater modeling

NASA Astrophysics Data System (ADS)

Xu, T.; Valocchi, A. J.; Lin, Y. F. F.; Liang, F.

2015-12-01

Effective water resource management typically relies on numerical models to analyze groundwater flow and solute transport processes. Model structural error (due to simplification and/or misrepresentation of the "true" environmental system) and input forcing variability (which commonly arises since some inputs are uncontrolled or estimated with high uncertainty) are ubiquitous in groundwater models. Calibration that overlooks errors in model structure and input data can lead to biased parameter estimates and compromised predictions. We present a fully Bayesian approach for a complete assessment of uncertainty for spatially distributed groundwater models. The approach explicitly recognizes stochastic input and uses data-driven error models based on nonparametric kernel methods to account for model structural error. We employ exploratory data analysis to assist in specifying informative prior for error models to improve identifiability. The inference is facilitated by an efficient sampling algorithm based on DREAM-ZS and a parameter subspace multiple-try strategy to reduce the required number of forward simulations of the groundwater model. We demonstrate the Bayesian approach through a synthetic case study of surface-ground water interaction under changing pumping conditions. It is found that explicit treatment of errors in model structure and input data (groundwater pumping rate) has substantial impact on the posterior distribution of groundwater model parameters. Using error models reduces predictive bias caused by parameter compensation. In addition, input variability increases parametric and predictive uncertainty. The Bayesian approach allows for a comparison among the contributions from various error sources, which could inform future model improvement and data collection efforts on how to best direct resources towards reducing predictive uncertainty.
Phase-matching of attosecond XUV supercontinuum

NASA Astrophysics Data System (ADS)

Gilbertson, Steve; Mashiko, Hiroki; Li, Chengquan; Khan, Sabih; Shakya, Mahendra; Moon, Eric; Chang, Zenghu

2008-05-01

Adding a weak second harmonic field to an ellipticity dependent polarization gating field allowed for the production of XUV supercontinua from longer (˜10 fs) input pulses in argon. The spectra support 200 as single isolated pulses. This technique, dubbed double optical gating (DOG), demonstrated a large enhancement of the harmonic yield as compared with polarization gating. These results can be attributed to the reduced depletion of the ground state of the target from the leading edge of the pulse and the increased intensity inside the polarization gate width. Through optimization of the harmonic generation process under the phase matching conditions, we were able to further increase the harmonic flux. The parameters included the target gas pressure, laser focus position, input pulse duration, and polarization gate width. By varying the CE phase of the pulse, we were able to verify that the results were indeed from DOG due to its unique 2 pi dependence on the harmonic spectrum. We were able to extend our results to neon. Its higher ionization potential allowed an extension of the harmonic cutoff for the production of even shorter pulses.
Development of a hydraulic model of the human systemic circulation

NASA Technical Reports Server (NTRS)

Sharp, M. K.; Dharmalingham, R. K.

1999-01-01

Physical and numeric models of the human circulation are constructed for a number of objectives, including studies and training in physiologic control, interpretation of clinical observations, and testing of prosthetic cardiovascular devices. For many of these purposes it is important to quantitatively validate the dynamic response of the models in terms of the input impedance (Z = oscillatory pressure/oscillatory flow). To address this need, the authors developed an improved physical model. Using a computer study, the authors first identified the configuration of lumped parameter elements in a model of the systemic circulation; the result was a good match with human aortic input impedance with a minimum number of elements. Design, construction, and testing of a hydraulic model analogous to the computer model followed. Numeric results showed that a three element model with two resistors and one compliance produced reasonable matching without undue complication. The subsequent analogous hydraulic model included adjustable resistors incorporating a sliding plate to vary the flow area through a porous material and an adjustable compliance consisting of a variable-volume air chamber. The response of the hydraulic model compared favorably with other circulation models.
Intelligent robust tracking control for a class of uncertain strict-feedback nonlinear systems.

PubMed

Chang, Yeong-Chan

2009-02-01

This paper addresses the problem of designing robust tracking controls for a large class of strict-feedback nonlinear systems involving plant uncertainties and external disturbances. The input and virtual input weighting matrices are perturbed by bounded time-varying uncertainties. An adaptive fuzzy-based (or neural-network-based) dynamic feedback tracking controller will be developed such that all the states and signals of the closed-loop system are bounded and the trajectory tracking error should be as small as possible. First, the adaptive approximators with linearly parameterized models are designed, and a partitioned procedure with respect to the developed adaptive approximators is proposed such that the implementation of the fuzzy (or neural network) basis functions depends only on the state variables but does not depend on the tuning approximation parameters. Furthermore, we extend to design the nonlinearly parameterized adaptive approximators. Consequently, the intelligent robust tracking control schemes developed in this paper possess the properties of computational simplicity and easy implementation. Finally, simulation examples are presented to demonstrate the effectiveness of the proposed control algorithms.
Hierarchical design of an electro-hydraulic actuator based on robust LPV methods

NASA Astrophysics Data System (ADS)

Németh, Balázs; Varga, Balázs; Gáspár, Péter

2015-08-01

The paper proposes a hierarchical control design of an electro-hydraulic actuator, which is used to improve the roll stability of vehicles. The purpose of the control system is to generate a reference torque, which is required by the vehicle dynamic control. The control-oriented model of the actuator is formulated in two subsystems. The high-level hydromotor is described in a linear form, while the low-level spool valve is a polynomial system. These subsystems require different control strategies. At the high level, a linear parameter-varying control is used to guarantee performance specifications. At the low level, a control Lyapunov-function-based algorithm, which creates discrete control input values of the valve, is proposed. The interaction between the two subsystems is guaranteed by the spool displacement, which is control input at the high level and must be tracked at the low-level control. The spool displacement has physical constraints, which must also be incorporated into the control design. The robust design of the high-level control incorporates the imprecision of the low-level control as an uncertainty of the system.
Knowledge system and method for simulating chemical controlled release device performance

DOEpatents

Cowan, Christina E.; Van Voris, Peter; Streile, Gary P.; Cataldo, Dominic A.; Burton, Frederick G.

1991-01-01

A knowledge system for simulating the performance of a controlled release device is provided. The system includes an input device through which the user selectively inputs one or more data parameters. The data parameters comprise first parameters including device parameters, media parameters, active chemical parameters and device release rate; and second parameters including the minimum effective inhibition zone of the device and the effective lifetime of the device. The system also includes a judgemental knowledge base which includes logic for 1) determining at least one of the second parameters from the release rate and the first parameters and 2) determining at least one of the first parameters from the other of the first parameters and the second parameters. The system further includes a device for displaying the results of the determinations to the user.
Method of validating measurement data of a process parameter from a plurality of individual sensor inputs

DOEpatents

Scarola, Kenneth; Jamison, David S.; Manazir, Richard M.; Rescorl, Robert L.; Harmon, Daryl L.

1998-01-01

A method for generating a validated measurement of a process parameter at a point in time by using a plurality of individual sensor inputs from a scan of said sensors at said point in time. The sensor inputs from said scan are stored and a first validation pass is initiated by computing an initial average of all stored sensor inputs. Each sensor input is deviation checked by comparing each input including a preset tolerance against the initial average input. If the first deviation check is unsatisfactory, the sensor which produced the unsatisfactory input is flagged as suspect. It is then determined whether at least two of the inputs have not been flagged as suspect and are therefore considered good inputs. If two or more inputs are good, a second validation pass is initiated by computing a second average of all the good sensor inputs, and deviation checking the good inputs by comparing each good input including a present tolerance against the second average. If the second deviation check is satisfactory, the second average is displayed as the validated measurement and the suspect sensor as flagged as bad. A validation fault occurs if at least two inputs are not considered good, or if the second deviation check is not satisfactory. In the latter situation the inputs from each of all the sensors are compared against the last validated measurement and the value from the sensor input that deviates the least from the last valid measurement is displayed.
Statistics of optimal information flow in ensembles of regulatory motifs

NASA Astrophysics Data System (ADS)

Crisanti, Andrea; De Martino, Andrea; Fiorentino, Jonathan

2018-02-01

Genetic regulatory circuits universally cope with different sources of noise that limit their ability to coordinate input and output signals. In many cases, optimal regulatory performance can be thought to correspond to configurations of variables and parameters that maximize the mutual information between inputs and outputs. Since the mid-2000s, such optima have been well characterized in several biologically relevant cases. Here we use methods of statistical field theory to calculate the statistics of the maximal mutual information (the "capacity") achievable by tuning the input variable only in an ensemble of regulatory motifs, such that a single controller regulates N targets. Assuming (i) sufficiently large N , (ii) quenched random kinetic parameters, and (iii) small noise affecting the input-output channels, we can accurately reproduce numerical simulations both for the mean capacity and for the whole distribution. Our results provide insight into the inherent variability in effectiveness occurring in regulatory systems with heterogeneous kinetic parameters.
Missing pulse detector for a variable frequency source

DOEpatents

Ingram, Charles B.; Lawhorn, John H.

1979-01-01

A missing pulse detector is provided which has the capability of monitoring a varying frequency pulse source to detect the loss of a single pulse or total loss of signal from the source. A frequency-to-current converter is used to program the output pulse width of a variable period retriggerable one-shot to maintain a pulse width slightly longer than one-half the present monitored pulse period. The retriggerable one-shot is triggered at twice the input pulse rate by employing a frequency doubler circuit connected between the one-shot input and the variable frequency source being monitored. The one-shot remains in the triggered or unstable state under normal conditions even though the source period is varying. A loss of an input pulse or single period of a fluctuating signal input will cause the one-shot to revert to its stable state, changing the output signal level to indicate a missing pulse or signal.
Parameter Identification Flight Test Maneuvers for Closed Loop Modeling of the F-18 High Alpha Research Vehicle (HARV)

NASA Technical Reports Server (NTRS)

Batterson, James G. (Technical Monitor); Morelli, E. A.

1996-01-01

Flight test maneuvers are specified for the F-18 High Alpha Research Vehicle (HARV). The maneuvers were designed for closed loop parameter identification purposes, specifically for longitudinal and lateral linear model parameter estimation at 5,20,30,45, and 60 degrees angle of attack, using the Actuated Nose Strakes for Enhanced Rolling (ANSER) control law in Thrust Vectoring (TV) mode. Each maneuver is to be realized by applying square wave inputs to specific pilot station controls using the On-Board Excitation System (OBES). Maneuver descriptions and complete specifications of the time / amplitude points defining each input are included, along with plots of the input time histories.
Fallon, Nevada FORGE Thermal-Hydrological-Mechanical Models

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

Blankenship, Doug; Sonnenthal, Eric

Archive contains thermal-mechanical simulation input/output files. Included are files which fall into the following categories: ( 1 ) Spreadsheets with various input parameter calculations ( 2 ) Final Simulation Inputs ( 3 ) Native-State Thermal-Hydrological Model Input File Folders ( 4 ) Native-State Thermal-Hydrological-Mechanical Model Input Files ( 5 ) THM Model Stimulation Cases See 'File Descriptions.xlsx' resource below for additional information on individual files.
Cell death, perfusion and electrical parameters are critical in models of hepatic radiofrequency ablation

PubMed Central

Hall, Sheldon K.; Ooi, Ean H.; Payne, Stephen J.

2015-01-01

Abstract Purpose: A sensitivity analysis has been performed on a mathematical model of radiofrequency ablation (RFA) in the liver. The purpose of this is to identify the most important parameters in the model, defined as those that produce the largest changes in the prediction. This is important in understanding the role of uncertainty and when comparing the model predictions to experimental data. Materials and methods: The Morris method was chosen to perform the sensitivity analysis because it is ideal for models with many parameters or that take a significant length of time to obtain solutions. A comprehensive literature review was performed to obtain ranges over which the model parameters are expected to vary, crucial input information. Results: The most important parameters in predicting the ablation zone size in our model of RFA are those representing the blood perfusion, electrical conductivity and the cell death model. The size of the 50 °C isotherm is sensitive to the electrical properties of tissue while the heat source is active, and to the thermal parameters during cooling. Conclusions: The parameter ranges chosen for the sensitivity analysis are believed to represent all that is currently known about their values in combination. The Morris method is able to compute global parameter sensitivities taking into account the interaction of all parameters, something that has not been done before. Research is needed to better understand the uncertainties in the cell death, electrical conductivity and perfusion models, but the other parameters are only of second order, providing a significant simplification. PMID:26000972
Estimating Fluctuating Pressures From Distorted Measurements

NASA Technical Reports Server (NTRS)

Whitmore, Stephen A.; Leondes, Cornelius T.

1994-01-01

Two algorithms extract estimates of time-dependent input (upstream) pressures from outputs of pressure sensors located at downstream ends of pneumatic tubes. Effect deconvolutions that account for distoring effects of tube upon pressure signal. Distortion of pressure measurements by pneumatic tubes also discussed in "Distortion of Pressure Signals in Pneumatic Tubes," (ARC-12868). Varying input pressure estimated from measured time-varying output pressure by one of two deconvolution algorithms that take account of measurement noise. Algorithms based on minimum-covariance (Kalman filtering) theory.
An analysis of input errors in precipitation-runoff models using regression with errors in the independent variables

USGS Publications Warehouse

Troutman, Brent M.

1982-01-01

Errors in runoff prediction caused by input data errors are analyzed by treating precipitation-runoff models as regression (conditional expectation) models. Independent variables of the regression consist of precipitation and other input measurements; the dependent variable is runoff. In models using erroneous input data, prediction errors are inflated and estimates of expected storm runoff for given observed input variables are biased. This bias in expected runoff estimation results in biased parameter estimates if these parameter estimates are obtained by a least squares fit of predicted to observed runoff values. The problems of error inflation and bias are examined in detail for a simple linear regression of runoff on rainfall and for a nonlinear U.S. Geological Survey precipitation-runoff model. Some implications for flood frequency analysis are considered. A case study using a set of data from Turtle Creek near Dallas, Texas illustrates the problems of model input errors.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Pal, Pinaki; Probst, Daniel; Pei, Yuanjiang

Fuels in the gasoline auto-ignition range (Research Octane Number (RON) > 60) have been demonstrated to be effective alternatives to diesel fuel in compression ignition engines. Such fuels allow more time for mixing with oxygen before combustion starts, owing to longer ignition delay. Moreover, by controlling fuel injection timing, it can be ensured that the in-cylinder mixture is “premixed enough” before combustion occurs to prevent soot formation while remaining “sufficiently inhomogeneous” in order to avoid excessive heat release rates. Gasoline compression ignition (GCI) has the potential to offer diesel-like efficiency at a lower cost and can be achieved with fuelsmore » such as low-octane straight run gasoline which require significantly less processing in the refinery compared to today’s fuels. To aid the design and optimization of a compression ignition (CI) combustion system using such fuels, a global sensitivity analysis (GSA) was conducted to understand the relative influence of various design parameters on efficiency, emissions and heat release rate. The design parameters included injection strategies, exhaust gas recirculation (EGR) fraction, temperature and pressure at intake valve closure and injector configuration. These were varied simultaneously to achieve various targets of ignition timing, combustion phasing, overall burn duration, emissions, fuel consumption, peak cylinder pressure and maximum pressure rise rate. The baseline case was a three-dimensional closed-cycle computational fluid dynamics (CFD) simulation with a sector mesh at medium load conditions. Eleven design parameters were considered and ranges of variation were prescribed to each of these. These input variables were perturbed in their respective ranges using the Monte Carlo (MC) method to generate a set of 256 CFD simulations and the targets were calculated from the simulation results. GSA was then applied as a screening tool to identify the input parameters having the most significant impact on each target. The results were further assessed by investigating the impact of individual parameter variations on the targets. Overall, it was demonstrated that GSA can be an effective tool in understanding parameters sensitive to a low temperature combustion concept with novel fuels.« less
Inverse optimal design of input-to-state stabilisation for affine nonlinear systems with input delays

NASA Astrophysics Data System (ADS)

Cai, Xiushan; Meng, Lingxin; Zhang, Wei; Liu, Leipo

2018-03-01

We establish robustness of the predictor feedback control law to perturbations appearing at the system input for affine nonlinear systems with time-varying input delay and additive disturbances. Furthermore, it is shown that it is inverse optimal with respect to a differential game problem. All of the stability and inverse optimality proofs are based on the infinite-dimensional backstepping transformation and an appropriate Lyapunov functional. A single-link manipulator subject to input delays and disturbances is given to illustrate the validity of the proposed method.
Reliability of system for precise cold forging

NASA Astrophysics Data System (ADS)

Krušič, Vid; Rodič, Tomaž

2017-07-01

The influence of scatter of principal input parameters of the forging system on the dimensional accuracy of product and on the tool life for closed-die forging process is presented in this paper. Scatter of the essential input parameters for the closed-die upsetting process was adjusted to the maximal values that enabled the reliable production of a dimensionally accurate product at optimal tool life. An operating window was created in which exists the maximal scatter of principal input parameters for the closed-die upsetting process that still ensures the desired dimensional accuracy of the product and the optimal tool life. Application of the adjustment of the process input parameters is shown on the example of making an inner race of homokinetic joint from mass production. High productivity in manufacture of elements by cold massive extrusion is often achieved by multiple forming operations that are performed simultaneously on the same press. By redesigning the time sequences of forming operations at multistage forming process of starter barrel during the working stroke the course of the resultant force is optimized.

Learning free energy landscapes using artificial neural networks.

PubMed

Sidky, Hythem; Whitmer, Jonathan K

2018-03-14

Existing adaptive bias techniques, which seek to estimate free energies and physical properties from molecular simulations, are limited by their reliance on fixed kernels or basis sets which hinder their ability to efficiently conform to varied free energy landscapes. Further, user-specified parameters are in general non-intuitive yet significantly affect the convergence rate and accuracy of the free energy estimate. Here we propose a novel method, wherein artificial neural networks (ANNs) are used to develop an adaptive biasing potential which learns free energy landscapes. We demonstrate that this method is capable of rapidly adapting to complex free energy landscapes and is not prone to boundary or oscillation problems. The method is made robust to hyperparameters and overfitting through Bayesian regularization which penalizes network weights and auto-regulates the number of effective parameters in the network. ANN sampling represents a promising innovative approach which can resolve complex free energy landscapes in less time than conventional approaches while requiring minimal user input.
The effects of parameter variation on MSET models of the Crystal River-3 feedwater flow system.

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

Miron, A.

1998-04-01

In this paper we develop further the results reported in Reference 1 to include a systematic study of the effects of varying MSET models and model parameters for the Crystal River-3 (CR) feedwater flow system The study used archived CR process computer files from November 1-December 15, 1993 that were provided by Florida Power Corporation engineers Fairman Bockhorst and Brook Julias. The results support the conclusion that an optimal MSET model, properly trained and deriving its inputs in real-time from no more than 25 of the sensor signals normally provided to a PWR plant process computer, should be able tomore » reliably detect anomalous variations in the feedwater flow venturis of less than 0.1% and in the absence of a venturi sensor signal should be able to generate a virtual signal that will be within 0.1% of the correct value of the missing signal.« less
LAI inversion from optical reflectance using a neural network trained with a multiple scattering model

NASA Technical Reports Server (NTRS)

Smith, James A.

1992-01-01

The inversion of the leaf area index (LAI) canopy parameter from optical spectral reflectance measurements is obtained using a backpropagation artificial neural network trained using input-output pairs generated by a multiple scattering reflectance model. The problem of LAI estimation over sparse canopies (LAI < 1.0) with varying soil reflectance backgrounds is particularly difficult. Standard multiple regression methods applied to canopies within a single homogeneous soil type yield good results but perform unacceptably when applied across soil boundaries, resulting in absolute percentage errors of >1000 percent for low LAI. Minimization methods applied to merit functions constructed from differences between measured reflectances and predicted reflectances using multiple-scattering models are unacceptably sensitive to a good initial guess for the desired parameter. In contrast, the neural network reported generally yields absolute percentage errors of <30 percent when weighting coefficients trained on one soil type were applied to predicted canopy reflectance at a different soil background.
Learning free energy landscapes using artificial neural networks

NASA Astrophysics Data System (ADS)

Sidky, Hythem; Whitmer, Jonathan K.

2018-03-01

Existing adaptive bias techniques, which seek to estimate free energies and physical properties from molecular simulations, are limited by their reliance on fixed kernels or basis sets which hinder their ability to efficiently conform to varied free energy landscapes. Further, user-specified parameters are in general non-intuitive yet significantly affect the convergence rate and accuracy of the free energy estimate. Here we propose a novel method, wherein artificial neural networks (ANNs) are used to develop an adaptive biasing potential which learns free energy landscapes. We demonstrate that this method is capable of rapidly adapting to complex free energy landscapes and is not prone to boundary or oscillation problems. The method is made robust to hyperparameters and overfitting through Bayesian regularization which penalizes network weights and auto-regulates the number of effective parameters in the network. ANN sampling represents a promising innovative approach which can resolve complex free energy landscapes in less time than conventional approaches while requiring minimal user input.
The future viability of algae-derived biodiesel under economic and technical uncertainties.

PubMed

Brownbridge, George; Azadi, Pooya; Smallbone, Andrew; Bhave, Amit; Taylor, Benjamin; Kraft, Markus

2014-01-01

This study presents a techno-economic assessment of algae-derived biodiesel under economic and technical uncertainties associated with the development of algal biorefineries. A global sensitivity analysis was performed using a High Dimensional Model Representation (HDMR) method. It was found that, considering reasonable ranges over which each parameter can vary, the sensitivity of the biodiesel production cost to the key input parameters decreases in the following order: algae oil content>algae annual productivity per unit area>plant production capacity>carbon price increase rate. It was also found that the Return on Investment (ROI) is highly sensitive to the algae oil content, and to a lesser extent to the algae annual productivity, crude oil price and price increase rate, plant production capacity, and carbon price increase rate. For a large scale plant (100,000 tonnes of biodiesel per year) the production cost of biodiesel is likely to be £0.8-1.6 per kg. Copyright © 2013 Elsevier Ltd. All rights reserved.
Changing space and sound: Parametric design and variable acoustics

NASA Astrophysics Data System (ADS)

Norton, Christopher William

This thesis examines the potential for parametric design software to create performance based design using acoustic metrics as the design criteria. A former soundstage at the University of Southern California used by the Thornton School of Music is used as a case study for a multiuse space for orchestral, percussion, master class and recital use. The criteria used for each programmatic use include reverberation time, bass ratio, and the early energy ratios of the clarity index and objective support. Using a panelized ceiling as a design element to vary the parameters of volume, panel orientation and type of absorptive material, the relationships between these parameters and the design criteria are explored. These relationships and subsequently derived equations are applied to Grasshopper parametric modeling software for Rhino 3D (a NURBS modeling software). Using the target reverberation time and bass ratio for each programmatic use as input for the parametric model, the genomic optimization function of Grasshopper - Galapagos - is run to identify the optimum ceiling geometry and material distribution.
Spiking and Excitatory/Inhibitory Input Dynamics of Barrel Cells in Response to Whisker Deflections of Varying Velocity and Angular Direction.

PubMed

Patel, Mainak

2018-01-15

The spiking of barrel regular-spiking (RS) cells is tuned for both whisker deflection direction and velocity. Velocity tuning arises due to thalamocortical (TC) synchrony (but not spike quantity) varying with deflection velocity, coupled with feedforward inhibition, while direction selectivity is not fully understood, though may be due partly to direction tuning of TC spiking. Data show that as deflection direction deviates from the preferred direction of an RS cell, excitatory input to the RS cell diminishes minimally, but temporally shifts to coincide with the time-lagged inhibitory input. This work constructs a realistic large-scale model of a barrel; model RS cells exhibit velocity and direction selectivity due to TC input dynamics, with the experimentally observed sharpening of direction tuning with decreasing velocity. The model puts forth the novel proposal that RS→RS synapses can naturally and simply account for the unexplained direction dependence of RS cell inputs - as deflection direction deviates from the preferred direction of an RS cell, and TC input declines, RS→RS synaptic transmission buffers the decline in total excitatory input and causes a shift in timing of the excitatory input peak from the peak in TC input to the delayed peak in RS input. The model also provides several experimentally testable predictions on the velocity dependence of RS cell inputs. This model is the first, to my knowledge, to study the interaction of direction and velocity and propose physiological mechanisms for the stimulus dependence in the timing and amplitude of RS cell inputs. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.
Influence of speckle image reconstruction on photometric precision for large solar telescopes

NASA Astrophysics Data System (ADS)

Peck, C. L.; Wöger, F.; Marino, J.

2017-11-01

Context. High-resolution observations from large solar telescopes require adaptive optics (AO) systems to overcome image degradation caused by Earth's turbulent atmosphere. AO corrections are, however, only partial. Achieving near-diffraction limited resolution over a large field of view typically requires post-facto image reconstruction techniques to reconstruct the source image. Aims: This study aims to examine the expected photometric precision of amplitude reconstructed solar images calibrated using models for the on-axis speckle transfer functions and input parameters derived from AO control data. We perform a sensitivity analysis of the photometric precision under variations in the model input parameters for high-resolution solar images consistent with four-meter class solar telescopes. Methods: Using simulations of both atmospheric turbulence and partial compensation by an AO system, we computed the speckle transfer function under variations in the input parameters. We then convolved high-resolution numerical simulations of the solar photosphere with the simulated atmospheric transfer function, and subsequently deconvolved them with the model speckle transfer function to obtain a reconstructed image. To compute the resulting photometric precision, we compared the intensity of the original image with the reconstructed image. Results: The analysis demonstrates that high photometric precision can be obtained for speckle amplitude reconstruction using speckle transfer function models combined with AO-derived input parameters. Additionally, it shows that the reconstruction is most sensitive to the input parameter that characterizes the atmospheric distortion, and sub-2% photometric precision is readily obtained when it is well estimated.
Linking 1D coastal ocean modelling to environmental management: an ensemble approach

NASA Astrophysics Data System (ADS)

Mussap, Giulia; Zavatarelli, Marco; Pinardi, Nadia

2017-12-01

The use of a one-dimensional interdisciplinary numerical model of the coastal ocean as a tool contributing to the formulation of ecosystem-based management (EBM) is explored. The focus is on the definition of an experimental design based on ensemble simulations, integrating variability linked to scenarios (characterised by changes in the system forcing) and to the concurrent variation of selected, and poorly constrained, model parameters. The modelling system used was previously specifically designed for the use in "data-rich" areas, so that horizontal dynamics can be resolved by a diagnostic approach and external inputs can be parameterised by nudging schemes properly calibrated. Ensembles determined by changes in the simulated environmental (physical and biogeochemical) dynamics, under joint forcing and parameterisation variations, highlight the uncertainties associated to the application of specific scenarios that are relevant to EBM, providing an assessment of the reliability of the predicted changes. The work has been carried out by implementing the coupled modelling system BFM-POM1D in an area of Gulf of Trieste (northern Adriatic Sea), considered homogeneous from the point of view of hydrological properties, and forcing it by changing climatic (warming) and anthropogenic (reduction of the land-based nutrient input) pressure. Model parameters affected by considerable uncertainties (due to the lack of relevant observations) were varied jointly with the scenarios of change. The resulting large set of ensemble simulations provided a general estimation of the model uncertainties related to the joint variation of pressures and model parameters. The information of the model result variability aimed at conveying efficiently and comprehensibly the information on the uncertainties/reliability of the model results to non-technical EBM planners and stakeholders, in order to have the model-based information effectively contributing to EBM.
Hierarchical Bayesian modelling of mobility metrics for hazard model input calibration

NASA Astrophysics Data System (ADS)

Calder, Eliza; Ogburn, Sarah; Spiller, Elaine; Rutarindwa, Regis; Berger, Jim

2015-04-01

In this work we present a method to constrain flow mobility input parameters for pyroclastic flow models using hierarchical Bayes modeling of standard mobility metrics such as H/L and flow volume etc. The advantage of hierarchical modeling is that it can leverage the information in global dataset for a particular mobility metric in order to reduce the uncertainty in modeling of an individual volcano, especially important where individual volcanoes have only sparse datasets. We use compiled pyroclastic flow runout data from Colima, Merapi, Soufriere Hills, Unzen and Semeru volcanoes, presented in an open-source database FlowDat (https://vhub.org/groups/massflowdatabase). While the exact relationship between flow volume and friction varies somewhat between volcanoes, dome collapse flows originating from the same volcano exhibit similar mobility relationships. Instead of fitting separate regression models for each volcano dataset, we use a variation of the hierarchical linear model (Kass and Steffey, 1989). The model presents a hierarchical structure with two levels; all dome collapse flows and dome collapse flows at specific volcanoes. The hierarchical model allows us to assume that the flows at specific volcanoes share a common distribution of regression slopes, then solves for that distribution. We present comparisons of the 95% confidence intervals on the individual regression lines for the data set from each volcano as well as those obtained from the hierarchical model. The results clearly demonstrate the advantage of considering global datasets using this technique. The technique developed is demonstrated here for mobility metrics, but can be applied to many other global datasets of volcanic parameters. In particular, such methods can provide a means to better contain parameters for volcanoes for which we only have sparse data, a ubiquitous problem in volcanology.
Weighted Iterative Bayesian Compressive Sensing (WIBCS) for High Dimensional Polynomial Surrogate Construction

NASA Astrophysics Data System (ADS)

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

2016-12-01

Surrogate construction has become a routine procedure when facing computationally intensive studies requiring multiple evaluations of complex models. In particular, surrogate models, otherwise called emulators or response surfaces, replace complex models in uncertainty quantification (UQ) studies, including uncertainty propagation (forward UQ) and parameter estimation (inverse UQ). Further, surrogates based on Polynomial Chaos (PC) expansions are especially convenient for forward UQ and global sensitivity analysis, also known as variance-based decomposition. However, the PC surrogate construction strongly suffers from the curse of dimensionality. With a large number of input parameters, the number of model simulations required for accurate surrogate construction is prohibitively large. Relatedly, non-adaptive PC expansions typically include infeasibly large number of basis terms far exceeding the number of available model evaluations. We develop Weighted Iterative Bayesian Compressive Sensing (WIBCS) algorithm for adaptive basis growth and PC surrogate construction leading to a sparse, high-dimensional PC surrogate with a very few model evaluations. The surrogate is then readily employed for global sensitivity analysis leading to further dimensionality reduction. Besides numerical tests, we demonstrate the construction on the example of Accelerated Climate Model for Energy (ACME) Land Model for several output QoIs at nearly 100 FLUXNET sites covering multiple plant functional types and climates, varying 65 input parameters over broad ranges of possible values. This work is supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research, Accelerated Climate Modeling for Energy (ACME) project. 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.
Developing a calibrated CONUS-wide watershed-scale simulation platform for quantifying the influence of different sources of uncertainty on streamflow forecast skill

NASA Astrophysics Data System (ADS)

Newman, A. J.; Sampson, K. M.; Wood, A. W.; Hopson, T. M.; Brekke, L. D.; Arnold, J.; Raff, D. A.; Clark, M. P.

2013-12-01

Skill in model-based hydrologic forecasting depends on the ability to estimate a watershed's initial moisture and energy conditions, to forecast future weather and climate inputs, and on the quality of the hydrologic model's representation of watershed processes. The impact of these factors on prediction skill varies regionally, seasonally, and by model. We are investigating these influences using a watershed simulation platform that spans the continental US (CONUS), encompassing a broad range of hydroclimatic variation, and that uses the current simulation models of National Weather Service streamflow forecasting operations. The first phase of this effort centered on the implementation and calibration of the SNOW-17 and Sacramento soil moisture accounting (SAC-SMA) based hydrologic modeling system for a range of watersheds. The base configuration includes 630 basins in the United States Geological Survey's Hydro-Climatic Data Network 2009 (HCDN-2009, Lins 2012) conterminous U.S. basin subset. Retrospective model forcings were derived from Daymet (http://daymet.ornl.gov/), and where available, a priori parameter estimates were based on or compared with the operational NWS model parameters. Model calibration was accomplished by several objective, automated strategies, including the shuffled complex evolution (SCE) optimization approach developed within the NWS in the early 1990s (Duan et al. 1993). This presentation describes outcomes from this effort, including insights about measuring simulation skill, and on relationships between simulation skill and model parameters, basin characteristics (climate, topography, vegetation, soils), and the quality of forcing inputs. References: %Z Thornton, P.; Thornton, M.; Mayer, B.; Wilhelmi, N.; Wei, Y.; Devarakonda, R; Cook, R. Daymet: Daily Surface Weather on a 1 km Grid for North America. 1980-2008; Oak Ridge National Laboratory Distributed Active Archive Center: Oak Ridge, TN, USA, 2012; Volume 10.
Between-User Reliability of Tier 1 Exposure Assessment Tools Used Under REACH.

PubMed

Lamb, Judith; Galea, Karen S; Miller, Brian G; Hesse, Susanne; Van Tongeren, Martie

2017-10-01

When applying simple screening (Tier 1) tools to estimate exposure to chemicals in a given exposure situation under the Registration, Evaluation, Authorisation and restriction of CHemicals Regulation 2006 (REACH), users must select from several possible input parameters. Previous studies have suggested that results from exposure assessments using expert judgement and from the use of modelling tools can vary considerably between assessors. This study aimed to investigate the between-user reliability of Tier 1 tools. A remote-completion exercise and in person workshop were used to identify and evaluate tool parameters and factors such as user demographics that may be potentially associated with between-user variability. Participants (N = 146) generated dermal and inhalation exposure estimates (N = 4066) from specified workplace descriptions ('exposure situations') and Tier 1 tool combinations (N = 20). Interactions between users, tools, and situations were investigated and described. Systematic variation associated with individual users was minor compared with random between-user variation. Although variation was observed between choices made for the majority of input parameters, differing choices of Process Category ('PROC') code/activity descriptor and dustiness level impacted most on the resultant exposure estimates. Exposure estimates ranging over several orders of magnitude were generated for the same exposure situation by different tool users. Such unpredictable between-user variation will reduce consistency within REACH processes and could result in under-estimation or overestimation of exposure, risking worker ill-health or the implementation of unnecessary risk controls, respectively. Implementation of additional support and quality control systems for all tool users is needed to reduce between-assessor variation and so ensure both the protection of worker health and avoidance of unnecessary business risk management expenditure. © The Author 2017. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.
Blind Deconvolution for Distributed Parameter Systems with Unbounded Input and Output and Determining Blood Alcohol Concentration from Transdermal Biosensor Data.

PubMed

Rosen, I G; Luczak, Susan E; Weiss, Jordan

2014-03-15

We develop a blind deconvolution scheme for input-output systems described by distributed parameter systems with boundary input and output. An abstract functional analytic theory based on results for the linear quadratic control of infinite dimensional systems with unbounded input and output operators is presented. The blind deconvolution problem is then reformulated as a series of constrained linear and nonlinear optimization problems involving infinite dimensional dynamical systems. A finite dimensional approximation and convergence theory is developed. The theory is applied to the problem of estimating blood or breath alcohol concentration (respectively, BAC or BrAC) from biosensor-measured transdermal alcohol concentration (TAC) in the field. A distributed parameter model with boundary input and output is proposed for the transdermal transport of ethanol from the blood through the skin to the sensor. The problem of estimating BAC or BrAC from the TAC data is formulated as a blind deconvolution problem. A scheme to identify distinct drinking episodes in TAC data based on a Hodrick Prescott filter is discussed. Numerical results involving actual patient data are presented.
Method and apparatus for transfer function simulator for testing complex systems

NASA Technical Reports Server (NTRS)

Kavaya, M. J. (Inventor)

1985-01-01

A method and apparatus for testing the operation of a complex stabilization circuit in a closed loop system is presented. The method is comprised of a programmed analog or digital computing system for implementing the transfer function of a load thereby providing a predictable load. The digital computing system employs a table stored in a microprocessor in which precomputed values of the load transfer function are stored for values of input signal from the stabilization circuit over the range of interest. This technique may be used not only for isolating faults in the stabilization circuit, but also for analyzing a fault in a faulty load by so varying parameters of the computing system as to simulate operation of the actual load with the fault.
Steering of Frequency Standards by the Use of Linear Quadratic Gaussian Control Theory

NASA Technical Reports Server (NTRS)

Koppang, Paul; Leland, Robert

1996-01-01

Linear quadratic Gaussian control is a technique that uses Kalman filtering to estimate a state vector used for input into a control calculation. A control correction is calculated by minimizing a quadratic cost function that is dependent on both the state vector and the control amount. Different penalties, chosen by the designer, are assessed by the controller as the state vector and control amount vary from given optimal values. With this feature controllers can be designed to force the phase and frequency differences between two standards to zero either more or less aggressively depending on the application. Data will be used to show how using different parameters in the cost function analysis affects the steering and the stability of the frequency standards.
Evaluation of the Emergency Response Dose Assessment System(ERDAS)

NASA Technical Reports Server (NTRS)

Evans, Randolph J.; Lambert, Winifred C.; Manobianco, John T.; Taylor, Gregory E.; Wheeler, Mark M.; Yersavich, Ann M.

1996-01-01

The emergency response dose assessment system (ERDAS) is a protype software and hardware system configured to produce routine mesoscale meteorological forecasts and enhanced dispersion estimates on an operational basis for the Kennedy Space Center (KSC)/Cape Canaveral Air Station (CCAS) region. ERDAS provides emergency response guidance to operations at KSC/CCAS in the case of an accidental hazardous material release or an aborted vehicle launch. This report describes the evaluation of ERDAS including: evaluation of sea breeze predictions, comparison of launch plume location and concentration predictions, case study of a toxic release, evaluation of model sensitivity to varying input parameters, evaluation of the user interface, assessment of ERDA's operational capabilities, and a comparison of ERDAS models to the ocean breeze dry gultch diffusion model.
Hydrogen Financial Analysis Scenario Tool (H2FAST)

Science.gov Websites

basic financial performance metrics change by varying up to 20 user inputs. Enter your own input values or adjust the slider bars to see how the results change. Please note that currency is expressed in
Product design for energy reduction in concurrent engineering: An Inverted Pyramid Approach

NASA Astrophysics Data System (ADS)

Alkadi, Nasr M.

Energy factors in product design in concurrent engineering (CE) are becoming an emerging dimension for several reasons; (a) the rising interest in "green design and manufacturing", (b) the national energy security concerns and the dramatic increase in energy prices, (c) the global competition in the marketplace and global climate change commitments including carbon tax and emission trading systems, and (d) the widespread recognition of the need for sustainable development. This research presents a methodology for the intervention of energy factors in concurrent engineering product development process to significantly reduce the manufacturing energy requirement. The work presented here is the first attempt at integrating the design for energy in concurrent engineering framework. It adds an important tool to the DFX toolbox for evaluation of the impact of design decisions on the product manufacturing energy requirement early during the design phase. The research hypothesis states that "Product Manufacturing Energy Requirement is a Function of Design Parameters". The hypothesis was tested by conducting experimental work in machining and heat treating that took place at the manufacturing lab of the Industrial and Management Systems Engineering Department (IMSE) at West Virginia University (WVU) and at a major U.S steel manufacturing plant, respectively. The objective of the machining experiment was to study the effect of changing specific product design parameters (Material type and diameter) and process design parameters (metal removal rate) on a gear head lathe input power requirement through performing defined sets of machining experiments. The objective of the heat treating experiment was to study the effect of varying product charging temperature on the fuel consumption of a walking beams reheat furnace. The experimental work in both directions have revealed important insights into energy utilization in machining and heat-treating processes and its variance based on product, process, and system design parameters. In depth evaluation to how the design and manufacturing normally happen in concurrent engineering provided a framework to develop energy system levels in machining within the concurrent engineering environment using the method of "Inverted Pyramid Approach", (IPA). The IPA features varying levels of output energy based information depending on the input design parameters that is available during each stage (level) of the product design. The experimental work, the in-depth evaluation of design and manufacturing in CE, and the developed energy system levels in machining provided a solid base for the development of the model for the design for energy reduction in CE. The model was used to analyze an example part where 12 evolving designs were thoroughly reviewed to investigate the sensitivity of energy to design parameters in machining. The model allowed product design teams to address manufacturing energy concerns early during the design stage. As a result, ranges for energy sensitive design parameters impacting product manufacturing energy consumption were found in earlier levels. As designer proceeds to deeper levels in the model, this range tightens and results in significant energy reductions.
Uncertainty Analysis and Parameter Estimation For Nearshore Hydrodynamic Models

NASA Astrophysics Data System (ADS)

Ardani, S.; Kaihatu, J. M.

2012-12-01

Numerical models represent deterministic approaches used for the relevant physical processes in the nearshore. Complexity of the physics of the model and uncertainty involved in the model inputs compel us to apply a stochastic approach to analyze the robustness of the model. The Bayesian inverse problem is one powerful way to estimate the important input model parameters (determined by apriori sensitivity analysis) and can be used for uncertainty analysis of the outputs. Bayesian techniques can be used to find the range of most probable parameters based on the probability of the observed data and the residual errors. In this study, the effect of input data involving lateral (Neumann) boundary conditions, bathymetry and off-shore wave conditions on nearshore numerical models are considered. Monte Carlo simulation is applied to a deterministic numerical model (the Delft3D modeling suite for coupled waves and flow) for the resulting uncertainty analysis of the outputs (wave height, flow velocity, mean sea level and etc.). Uncertainty analysis of outputs is performed by random sampling from the input probability distribution functions and running the model as required until convergence to the consistent results is achieved. The case study used in this analysis is the Duck94 experiment, which was conducted at the U.S. Army Field Research Facility at Duck, North Carolina, USA in the fall of 1994. The joint probability of model parameters relevant for the Duck94 experiments will be found using the Bayesian approach. We will further show that, by using Bayesian techniques to estimate the optimized model parameters as inputs and applying them for uncertainty analysis, we can obtain more consistent results than using the prior information for input data which means that the variation of the uncertain parameter will be decreased and the probability of the observed data will improve as well. Keywords: Monte Carlo Simulation, Delft3D, uncertainty analysis, Bayesian techniques, MCMC

Distinct pattern separation related transfer functions in human CA3/dentate and CA1 revealed using high-resolution fMRI and variable mnemonic similarity

PubMed Central

Lacy, Joyce W.; Yassa, Michael A.; Stark, Shauna M.; Muftuler, L. Tugan; Stark, Craig E.L.

2011-01-01

Producing and maintaining distinct (orthogonal) neural representations for similar events is critical to avoiding interference in long-term memory. Recently, our laboratory provided the first evidence for separation-like signals in the human CA3/dentate. Here, we extended this by parametrically varying the change in input (similarity) while monitoring CA1 and CA3/dentate for separation and completion-like signals using high-resolution fMRI. In the CA1, activity varied in a graded fashion in response to increases in the change in input. In contrast, the CA3/dentate showed a stepwise transfer function that was highly sensitive to small changes in input. PMID:21164173
Using genetic algorithms with subjective input from human subjects: implications for fitting hearing aids and cochlear implants.

PubMed

Başkent, Deniz; Eiler, Cheryl L; Edwards, Brent

2007-06-01

To present a comprehensive analysis of the feasibility of genetic algorithms (GA) for finding the best fit of hearing aids or cochlear implants for individual users in clinical or research settings, where the algorithm is solely driven by subjective human input. Due to varying pathology, the best settings of an auditory device differ for each user. It is also likely that listening preferences vary at the same time. The settings of a device customized for a particular user can only be evaluated by the user. When optimization algorithms are used for fitting purposes, this situation poses a difficulty for a systematic and quantitative evaluation of the suitability of the fitting parameters produced by the algorithm. In the present study, an artificial listening environment was generated by distorting speech using a noiseband vocoder. The settings produced by the GA for this listening problem could objectively be evaluated by measuring speech recognition and comparing the performance to the best vocoder condition where speech was least distorted. Nine normal-hearing subjects participated in the study. The parameters to be optimized were the number of vocoder channels, the shift between the input frequency range and the synthesis frequency range, and the compression-expansion of the input frequency range over the synthesis frequency range. The subjects listened to pairs of sentences processed with the vocoder, and entered a preference for the sentence with better intelligibility. The GA modified the solutions iteratively according to the subject preferences. The program converged when the user ranked the same set of parameters as the best in three consecutive steps. The results produced by the GA were analyzed for quality by measuring speech intelligibility, for test-retest reliability by running the GA three times with each subject, and for convergence properties. Speech recognition scores averaged across subjects were similar for the best vocoder solution and for the solutions produced by the GA. The average number of iterations was 8 and the average convergence time was 25.5 minutes. The settings produced by different GA runs for the same subject were slightly different; however, speech recognition scores measured with these settings were similar. Individual data from subjects showed that in each run, a small number of GA solutions produced poorer speech intelligibility than for the best setting. This was probably a result of the combination of the inherent randomness of the GA, the convergence criterion used in the present study, and possible errors that the users might have made during the paired comparisons. On the other hand, the effect of these errors was probably small compared to the other two factors, as a comparison between subjective preferences and objective measures showed that for many subjects the two were in good agreement. The results showed that the GA was able to produce good solutions by using listener preferences in a relatively short time. For practical applications, the program can be made more robust by running the GA twice or by not using an automatic stopping criterion, and it can be made faster by optimizing the number of the paired comparisons completed in each iteration.
Large-eddy simulations of surface roughness parameter sensitivity to canopy-structure characteristics

DOE PAGES

Maurer, K. D.; Bohrer, G.; Kenny, W. T.; ...

2015-04-30

Surface roughness parameters, namely the roughness length and displacement height, are an integral input used to model surface fluxes. However, most models assume these parameters to be a fixed property of plant functional type and disregard the governing structural heterogeneity and dynamics. In this study, we use large-eddy simulations to explore, in silico, the effects of canopy-structure characteristics on surface roughness parameters. We performed a virtual experiment to test the sensitivity of resolved surface roughness to four axes of canopy structure: (1) leaf area index, (2) the vertical profile of leaf density, (3) canopy height, and (4) canopy gap fraction.more » We found roughness parameters to be highly variable, but uncovered positive relationships between displacement height and maximum canopy height, aerodynamic canopy height and maximum canopy height and leaf area index, and eddy-penetration depth and gap fraction. We also found negative relationships between aerodynamic canopy height and gap fraction, as well as between eddy-penetration depth and maximum canopy height and leaf area index. We generalized our model results into a virtual "biometric" parameterization that relates roughness length and displacement height to canopy height, leaf area index, and gap fraction. Using a decade of wind and canopy-structure observations in a site in Michigan, we tested the effectiveness of our model-driven biometric parameterization approach in predicting the friction velocity over heterogeneous and disturbed canopies. We compared the accuracy of these predictions with the friction-velocity predictions obtained from the common simple approximation related to canopy height, the values calculated with large-eddy simulations of the explicit canopy structure as measured by airborne and ground-based lidar, two other parameterization approaches that utilize varying canopy-structure inputs, and the annual and decadal means of the surface roughness parameters at the site from meteorological observations. We found that the classical representation of constant roughness parameters (in space and time) as a fraction of canopy height performed relatively well. Nonetheless, of the approaches we tested, most of the empirical approaches that incorporate seasonal and interannual variation of roughness length and displacement height as a function of the dynamics of canopy structure produced more precise and less biased estimates for friction velocity than models with temporally invariable parameters.« less
Large-eddy simulations of surface roughness parameter sensitivity to canopy-structure characteristics

NASA Astrophysics Data System (ADS)

Maurer, K. D.; Bohrer, G.; Kenny, W. T.; Ivanov, V. Y.

2015-04-01

Surface roughness parameters, namely the roughness length and displacement height, are an integral input used to model surface fluxes. However, most models assume these parameters to be a fixed property of plant functional type and disregard the governing structural heterogeneity and dynamics. In this study, we use large-eddy simulations to explore, in silico, the effects of canopy-structure characteristics on surface roughness parameters. We performed a virtual experiment to test the sensitivity of resolved surface roughness to four axes of canopy structure: (1) leaf area index, (2) the vertical profile of leaf density, (3) canopy height, and (4) canopy gap fraction. We found roughness parameters to be highly variable, but uncovered positive relationships between displacement height and maximum canopy height, aerodynamic canopy height and maximum canopy height and leaf area index, and eddy-penetration depth and gap fraction. We also found negative relationships between aerodynamic canopy height and gap fraction, as well as between eddy-penetration depth and maximum canopy height and leaf area index. We generalized our model results into a virtual "biometric" parameterization that relates roughness length and displacement height to canopy height, leaf area index, and gap fraction. Using a decade of wind and canopy-structure observations in a site in Michigan, we tested the effectiveness of our model-driven biometric parameterization approach in predicting the friction velocity over heterogeneous and disturbed canopies. We compared the accuracy of these predictions with the friction-velocity predictions obtained from the common simple approximation related to canopy height, the values calculated with large-eddy simulations of the explicit canopy structure as measured by airborne and ground-based lidar, two other parameterization approaches that utilize varying canopy-structure inputs, and the annual and decadal means of the surface roughness parameters at the site from meteorological observations. We found that the classical representation of constant roughness parameters (in space and time) as a fraction of canopy height performed relatively well. Nonetheless, of the approaches we tested, most of the empirical approaches that incorporate seasonal and interannual variation of roughness length and displacement height as a function of the dynamics of canopy structure produced more precise and less biased estimates for friction velocity than models with temporally invariable parameters.
Evaluation of five dry particle deposition parameterizations for incorporation into atmospheric transport models

NASA Astrophysics Data System (ADS)

Khan, Tanvir R.; Perlinger, Judith A.

2017-10-01

Despite considerable effort to develop mechanistic dry particle deposition parameterizations for atmospheric transport models, current knowledge has been inadequate to propose quantitative measures of the relative performance of available parameterizations. In this study, we evaluated the performance of five dry particle deposition parameterizations developed by Zhang et al. (2001) (Z01), Petroff and Zhang (2010) (PZ10), Kouznetsov and Sofiev (2012) (KS12), Zhang and He (2014) (ZH14), and Zhang and Shao (2014) (ZS14), respectively. The evaluation was performed in three dimensions: model ability to reproduce observed deposition velocities, Vd (accuracy); the influence of imprecision in input parameter values on the modeled Vd (uncertainty); and identification of the most influential parameter(s) (sensitivity). The accuracy of the modeled Vd was evaluated using observations obtained from five land use categories (LUCs): grass, coniferous and deciduous forests, natural water, and ice/snow. To ascertain the uncertainty in modeled Vd, and quantify the influence of imprecision in key model input parameters, a Monte Carlo uncertainty analysis was performed. The Sobol' sensitivity analysis was conducted with the objective to determine the parameter ranking from the most to the least influential. Comparing the normalized mean bias factors (indicators of accuracy), we find that the ZH14 parameterization is the most accurate for all LUCs except for coniferous forest, for which it is second most accurate. From Monte Carlo simulations, the estimated mean normalized uncertainties in the modeled Vd obtained for seven particle sizes (ranging from 0.005 to 2.5 µm) for the five LUCs are 17, 12, 13, 16, and 27 % for the Z01, PZ10, KS12, ZH14, and ZS14 parameterizations, respectively. From the Sobol' sensitivity results, we suggest that the parameter rankings vary by particle size and LUC for a given parameterization. Overall, for dp = 0.001 to 1.0 µm, friction velocity was one of the three most influential parameters in all parameterizations. For giant particles (dp = 10 µm), relative humidity was the most influential parameter. Because it is the least complex of the five parameterizations, and it has the greatest accuracy and least uncertainty, we propose that the ZH14 parameterization is currently superior for incorporation into atmospheric transport models.
A fast, robust algorithm for power line interference cancellation in neural recording.

PubMed

Keshtkaran, Mohammad Reza; Yang, Zhi

2014-04-01

Power line interference may severely corrupt neural recordings at 50/60 Hz and harmonic frequencies. The interference is usually non-stationary and can vary in frequency, amplitude and phase. To retrieve the gamma-band oscillations at the contaminated frequencies, it is desired to remove the interference without compromising the actual neural signals at the interference frequency bands. In this paper, we present a robust and computationally efficient algorithm for removing power line interference from neural recordings. The algorithm includes four steps. First, an adaptive notch filter is used to estimate the fundamental frequency of the interference. Subsequently, based on the estimated frequency, harmonics are generated by using discrete-time oscillators, and then the amplitude and phase of each harmonic are estimated by using a modified recursive least squares algorithm. Finally, the estimated interference is subtracted from the recorded data. The algorithm does not require any reference signal, and can track the frequency, phase and amplitude of each harmonic. When benchmarked with other popular approaches, our algorithm performs better in terms of noise immunity, convergence speed and output signal-to-noise ratio (SNR). While minimally affecting the signal bands of interest, the algorithm consistently yields fast convergence (<100 ms) and substantial interference rejection (output SNR >30 dB) in different conditions of interference strengths (input SNR from -30 to 30 dB), power line frequencies (45-65 Hz) and phase and amplitude drifts. In addition, the algorithm features a straightforward parameter adjustment since the parameters are independent of the input SNR, input signal power and the sampling rate. A hardware prototype was fabricated in a 65 nm CMOS process and tested. Software implementation of the algorithm has been made available for open access at https://github.com/mrezak/removePLI. The proposed algorithm features a highly robust operation, fast adaptation to interference variations, significant SNR improvement, low computational complexity and memory requirement and straightforward parameter adjustment. These features render the algorithm suitable for wearable and implantable sensor applications, where reliable and real-time cancellation of the interference is desired.
A fast, robust algorithm for power line interference cancellation in neural recording

NASA Astrophysics Data System (ADS)

Keshtkaran, Mohammad Reza; Yang, Zhi

2014-04-01

Objective. Power line interference may severely corrupt neural recordings at 50/60 Hz and harmonic frequencies. The interference is usually non-stationary and can vary in frequency, amplitude and phase. To retrieve the gamma-band oscillations at the contaminated frequencies, it is desired to remove the interference without compromising the actual neural signals at the interference frequency bands. In this paper, we present a robust and computationally efficient algorithm for removing power line interference from neural recordings. Approach. The algorithm includes four steps. First, an adaptive notch filter is used to estimate the fundamental frequency of the interference. Subsequently, based on the estimated frequency, harmonics are generated by using discrete-time oscillators, and then the amplitude and phase of each harmonic are estimated by using a modified recursive least squares algorithm. Finally, the estimated interference is subtracted from the recorded data. Main results. The algorithm does not require any reference signal, and can track the frequency, phase and amplitude of each harmonic. When benchmarked with other popular approaches, our algorithm performs better in terms of noise immunity, convergence speed and output signal-to-noise ratio (SNR). While minimally affecting the signal bands of interest, the algorithm consistently yields fast convergence (<100 ms) and substantial interference rejection (output SNR >30 dB) in different conditions of interference strengths (input SNR from -30 to 30 dB), power line frequencies (45-65 Hz) and phase and amplitude drifts. In addition, the algorithm features a straightforward parameter adjustment since the parameters are independent of the input SNR, input signal power and the sampling rate. A hardware prototype was fabricated in a 65 nm CMOS process and tested. Software implementation of the algorithm has been made available for open access at https://github.com/mrezak/removePLI. Significance. The proposed algorithm features a highly robust operation, fast adaptation to interference variations, significant SNR improvement, low computational complexity and memory requirement and straightforward parameter adjustment. These features render the algorithm suitable for wearable and implantable sensor applications, where reliable and real-time cancellation of the interference is desired.
Update on ɛK with lattice QCD inputs

NASA Astrophysics Data System (ADS)

Jang, Yong-Chull; Lee, Weonjong; Lee, Sunkyu; Leem, Jaehoon

2018-03-01

We report updated results for ɛK, the indirect CP violation parameter in neutral kaons, which is evaluated directly from the standard model with lattice QCD inputs. We use lattice QCD inputs to fix B\\hatk,|Vcb|,ξ0,ξ2,|Vus|, and mc(mc). Since Lattice 2016, the UTfit group has updated the Wolfenstein parameters in the angle-only-fit method, and the HFLAV group has also updated |Vcb|. Our results show that the evaluation of ɛK with exclusive |Vcb| (lattice QCD inputs) has 4.0σ tension with the experimental value, while that with inclusive |Vcb| (heavy quark expansion based on OPE and QCD sum rules) shows no tension.
Reduction of tablet weight variability by optimizing paddle speed in the forced feeder of a high-speed rotary tablet press.

PubMed

Peeters, Elisabeth; De Beer, Thomas; Vervaet, Chris; Remon, Jean-Paul

2015-04-01

Tableting is a complex process due to the large number of process parameters that can be varied. Knowledge and understanding of the influence of these parameters on the final product quality is of great importance for the industry, allowing economic efficiency and parametric release. The aim of this study was to investigate the influence of paddle speeds and fill depth at different tableting speeds on the weight and weight variability of tablets. Two excipients possessing different flow behavior, microcrystalline cellulose (MCC) and dibasic calcium phosphate dihydrate (DCP), were selected as model powders. Tablets were manufactured via a high-speed rotary tablet press using design of experiments (DoE). During each experiment also the volume of powder in the forced feeder was measured. Analysis of the DoE revealed that paddle speeds are of minor importance for tablet weight but significantly affect volume of powder inside the feeder in case of powders with excellent flowability (DCP). The opposite effect of paddle speed was observed for fairly flowing powders (MCC). Tableting speed played a role in weight and weight variability, whereas changing fill depth exclusively influenced tablet weight. The DoE approach allowed predicting the optimum combination of process parameters leading to minimum tablet weight variability. Monte Carlo simulations allowed assessing the probability to exceed the acceptable response limits if factor settings were varied around their optimum. This multi-dimensional combination and interaction of input variables leading to response criteria with acceptable probability reflected the design space.
Relationships between the structure of natural organic matter and its reactivity towards molecular ozone and hydroxyl radicals

USGS Publications Warehouse

Westerhoff, P.; Aiken, G.; Amy, G.; Debroux, J.

1999-01-01

Oxidation reaction rate parameters for molecular ozone (O3) and hydroxyl (HO) radicals with a variety of hydrophobic organic acids (HOAs) isolated from different geographic locations were determined from batch ozonation studies. Rate parameter values, obtained under equivalent dissolved organic carbon concentrations in both the presence and absence of non-NOM HO radical scavengers, varied as a function of NOM structure. First-order rate constants for O3 consumption (k(O3)) averaged 8.8 x 10-3 s-1, ranging from 3.9 x 10-3 s-1 for a groundwater HOA to > 16 x 10-3 s-1 for river HOAs with large terrestrial carbon inputs. The average second-order rate constant (k(HO,DOC) between HO radicals and NOM was 3.6 x 108 l (mol C)-1 s-1; a mass of 12 g C per mole C was used in all calculations. Specific ultraviolet absorbance (SUVA) at 254 or 280 nm of the HOAs correlated well (r > 0.9) with O3 consumption rate parameters, implying that organic ??-electrons strongly and selectively influence oxidative reactivity. HO radical reactions with NOM were less selective, although correlation between k(HO,DOC) and SUVA existed. Other physical-chemical properties of NOM, such as aromatic and aliphatic carbon content from 13C-NMR spectroscopy, proved less sensitive for predicting oxidation reactivity than SUVA. The implication of this study is that the structural nature of NOM varies temporally and spatially in a water source, and both the nature and amount of NOM will influence oxidation rates.
Simulating the behavior of volatiles belonging to the C-O-H-S system in silicate melts under magmatic conditions with the software D-Compress

NASA Astrophysics Data System (ADS)

Burgisser, Alain; Alletti, Marina; Scaillet, Bruno

2015-06-01

Modeling magmatic degassing, or how the volatile distribution between gas and melt changes at pressure varies, is a complex task that involves a large number of thermodynamical relationships and that requires dedicated software. This article presents the software D-Compress, which computes the gas and melt volatile composition of five element sets in magmatic systems (O-H, S-O-H, C-S-O-H, C-S-O-H-Fe, and C-O-H). It has been calibrated so as to simulate the volatiles coexisting with three common types of silicate melts (basalt, phonolite, and rhyolite). Operational temperatures depend on melt composition and range from 790 to 1400 °C. A specificity of D-Compress is the calculation of volatile composition as pressure varies along a (de)compression path between atmospheric and 3000 bars. This software was prepared so as to maximize versatility by proposing different sets of input parameters. In particular, whenever new solubility laws on specific melt compositions are available, the model parameters can be easily tuned to run the code on that composition. Parameter gaps were minimized by including sets of chemical species for which calibration data were available over a wide range of pressure, temperature, and melt composition. A brief description of the model rationale is followed by the presentation of the software capabilities. Examples of use are then presented with outputs comparisons between D-Compress and other currently available thermodynamical models. The compiled software and the source code are available as electronic supplementary materials.
Municipality Level Simulations of Dengue Fever Incidence in Puerto Rico Using Ground Based and Remotely Sensed Climate Data

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Morin, Cory

2015-01-01

Dengue fever (DF) is caused by a virus transmitted between humans and Aedes genus mosquitoes through blood feeding. In recent decades incidence of the disease has drastically increased in the tropical Americas, culminating with the Pan American outbreak in 2010 which resulted in 1.7 million reported cases. In Puerto Rico dengue is endemic, however, there is significant inter-annual, intraannual, and spatial variability in case loads. Variability in climate and the environment, herd immunity and virus genetics, and demographic characteristics may all contribute to differing patterns of transmission both spatially and temporally. Knowledge of climate influences on dengue incidence could facilitate development of early warning systems allowing public health workers to implement appropriate transmission intervention strategies. In this study, we simulate dengue incidence in several municipalities in Puerto Rico using population and meteorological data derived from ground based stations and remote sensing instruments. This data was used to drive a process based model of vector population development and virus transmission. Model parameter values for container composition, vector characteristics, and incubation period were chosen by employing a Monte Carlo approach. Multiple simulations were performed for each municipality and the results were compared with reported dengue cases. The best performing simulations were retained and their parameter values and meteorological input were compared between years and municipalities. Parameter values varied by municipality and year illustrating the complexity and sensitivity of the disease system. Local characteristics including the natural and built environment impact transmission dynamics and produce varying responses to meteorological conditions.
Modern control concepts in hydrology. [parameter identification in adaptive stochastic control approach

NASA Technical Reports Server (NTRS)

Duong, N.; Winn, C. B.; Johnson, G. R.

1975-01-01

Two approaches to an identification problem in hydrology are presented, based upon concepts from modern control and estimation theory. The first approach treats the identification of unknown parameters in a hydrologic system subject to noisy inputs as an adaptive linear stochastic control problem; the second approach alters the model equation to account for the random part in the inputs, and then uses a nonlinear estimation scheme to estimate the unknown parameters. Both approaches use state-space concepts. The identification schemes are sequential and adaptive and can handle either time-invariant or time-dependent parameters. They are used to identify parameters in the Prasad model of rainfall-runoff. The results obtained are encouraging and confirm the results from two previous studies; the first using numerical integration of the model equation along with a trial-and-error procedure, and the second using a quasi-linearization technique. The proposed approaches offer a systematic way of analyzing the rainfall-runoff process when the input data are imbedded in noise.
Nonlinear Dynamic Models in Advanced Life Support

NASA Technical Reports Server (NTRS)

Jones, Harry

2002-01-01

To facilitate analysis, ALS systems are often assumed to be linear and time invariant, but they usually have important nonlinear and dynamic aspects. Nonlinear dynamic behavior can be caused by time varying inputs, changes in system parameters, nonlinear system functions, closed loop feedback delays, and limits on buffer storage or processing rates. Dynamic models are usually cataloged according to the number of state variables. The simplest dynamic models are linear, using only integration, multiplication, addition, and subtraction of the state variables. A general linear model with only two state variables can produce all the possible dynamic behavior of linear systems with many state variables, including stability, oscillation, or exponential growth and decay. Linear systems can be described using mathematical analysis. Nonlinear dynamics can be fully explored only by computer simulations of models. Unexpected behavior is produced by simple models having only two or three state variables with simple mathematical relations between them. Closed loop feedback delays are a major source of system instability. Exceeding limits on buffer storage or processing rates forces systems to change operating mode. Different equilibrium points may be reached from different initial conditions. Instead of one stable equilibrium point, the system may have several equilibrium points, oscillate at different frequencies, or even behave chaotically, depending on the system inputs and initial conditions. The frequency spectrum of an output oscillation may contain harmonics and the sums and differences of input frequencies, but it may also contain a stable limit cycle oscillation not related to input frequencies. We must investigate the nonlinear dynamic aspects of advanced life support systems to understand and counter undesirable behavior.
Contribution of Anal Sex to HIV Prevalence Among Heterosexuals: A Modeling Analysis.

PubMed

O'Leary, Ann; DiNenno, Elizabeth; Honeycutt, Amanda; Allaire, Benjamin; Neuwahl, Simon; Hicks, Katherine; Sansom, Stephanie

2017-10-01

Anal intercourse is reported by many heterosexuals, and evidence suggests that its practice may be increasing. We estimated the proportion of the HIV burden attributable to anal sex in 2015 among heterosexual women and men in the United States. The HIV Optimization and Prevention Economics model was developed using parameter inputs from the literature for the sexually active U.S. population aged 13-64. The model uses differential equations to represent the progression of the population between compartments defined by HIV disease status and continuum-of-care stages from 2007 to 2015. For heterosexual women of all ages (who do not inject drugs), almost 28% of infections were associated with anal sex, whereas for women aged 18-34, nearly 40% of HIV infections were associated with anal sex. For heterosexual men, 20% of HIV infections were associated with insertive anal sex with women. Sensitivity analyses showed that varying any of 63 inputs by ±20% resulted in no more than a 13% change in the projected number of heterosexual infections in 2015, including those attributed to anal sex. Despite uncertainties in model inputs, a substantial portion of the HIV burden among heterosexuals appears to be attributable to anal sex. Providing information about the relative risk of anal sex compared with vaginal sex may help reduce HIV incidence in heterosexuals.
Uncertainty analysis in geospatial merit matrix–based hydropower resource assessment

DOE PAGES

Pasha, M. Fayzul K.; Yeasmin, Dilruba; Saetern, Sen; ...

2016-03-30

Hydraulic head and mean annual streamflow, two main input parameters in hydropower resource assessment, are not measured at every point along the stream. Translation and interpolation are used to derive these parameters, resulting in uncertainties. This study estimates the uncertainties and their effects on model output parameters: the total potential power and the number of potential locations (stream-reach). These parameters are quantified through Monte Carlo Simulation (MCS) linking with a geospatial merit matrix based hydropower resource assessment (GMM-HRA) Model. The methodology is applied to flat, mild, and steep terrains. Results show that the uncertainty associated with the hydraulic head ismore » within 20% for mild and steep terrains, and the uncertainty associated with streamflow is around 16% for all three terrains. Output uncertainty increases as input uncertainty increases. However, output uncertainty is around 10% to 20% of the input uncertainty, demonstrating the robustness of the GMM-HRA model. Hydraulic head is more sensitive to output parameters in steep terrain than in flat and mild terrains. Furthermore, mean annual streamflow is more sensitive to output parameters in flat terrain.« less
Dynamic modal estimation using instrumental variables

NASA Technical Reports Server (NTRS)

Salzwedel, H.

1980-01-01

A method to determine the modes of dynamical systems is described. The inputs and outputs of a system are Fourier transformed and averaged to reduce the error level. An instrumental variable method that estimates modal parameters from multiple correlations between responses of single input, multiple output systems is applied to estimate aircraft, spacecraft, and off-shore platform modal parameters.
Econometric analysis of fire suppression production functions for large wildland fires

Treesearch

Thomas P. Holmes; David E. Calkin

2013-01-01

In this paper, we use operational data collected for large wildland fires to estimate the parameters of economic production functions that relate the rate of fireline construction with the level of fire suppression inputs (handcrews, dozers, engines and helicopters). These parameter estimates are then used to evaluate whether the productivity of fire suppression inputs...
A mathematical model for predicting fire spread in wildland fuels

Treesearch

Richard C. Rothermel

1972-01-01

A mathematical fire model for predicting rate of spread and intensity that is applicable to a wide range of wildland fuels and environment is presented. Methods of incorporating mixtures of fuel sizes are introduced by weighting input parameters by surface area. The input parameters do not require a prior knowledge of the burning characteristics of the fuel.
The application of remote sensing to the development and formulation of hydrologic planning models

NASA Technical Reports Server (NTRS)

Castruccio, P. A.; Loats, H. L., Jr.; Fowler, T. R.

1976-01-01

A hydrologic planning model is developed based on remotely sensed inputs. Data from LANDSAT 1 are used to supply the model's quantitative parameters and coefficients. The use of LANDSAT data as information input to all categories of hydrologic models requiring quantitative surface parameters for their effects functioning is also investigated.

A data-input program (MFI2005) for the U.S. Geological Survey modular groundwater model (MODFLOW-2005) and parameter estimation program (UCODE_2005)

USGS Publications Warehouse

Harbaugh, Arien W.

2011-01-01

The MFI2005 data-input (entry) program was developed for use with the U.S. Geological Survey modular three-dimensional finite-difference groundwater model, MODFLOW-2005. MFI2005 runs on personal computers and is designed to be easy to use; data are entered interactively through a series of display screens. MFI2005 supports parameter estimation using the UCODE_2005 program for parameter estimation. Data for MODPATH, a particle-tracking program for use with MODFLOW-2005, also can be entered using MFI2005. MFI2005 can be used in conjunction with other data-input programs so that the different parts of a model dataset can be entered by using the most suitable program.
An FDTD-based computer simulation platform for shock wave propagation in electrohydraulic lithotripsy.

PubMed

Yılmaz, Bülent; Çiftçi, Emre

2013-06-01

Extracorporeal Shock Wave Lithotripsy (ESWL) is based on disintegration of the kidney stone by delivering high-energy shock waves that are created outside the body and transmitted through the skin and body tissues. Nowadays high-energy shock waves are also used in orthopedic operations and investigated to be used in the treatment of myocardial infarction and cancer. Because of these new application areas novel lithotriptor designs are needed for different kinds of treatment strategies. In this study our aim was to develop a versatile computer simulation environment which would give the device designers working on various medical applications that use shock wave principle a substantial amount of flexibility while testing the effects of new parameters such as reflector size, material properties of the medium, water temperature, and different clinical scenarios. For this purpose, we created a finite-difference time-domain (FDTD)-based computational model in which most of the physical system parameters were defined as an input and/or as a variable in the simulations. We constructed a realistic computational model of a commercial electrohydraulic lithotriptor and optimized our simulation program using the results that were obtained by the manufacturer in an experimental setup. We, then, compared the simulation results with the results from an experimental setup in which oxygen level in water was varied. Finally, we studied the effects of changing the input parameters like ellipsoid size and material, temperature change in the wave propagation media, and shock wave source point misalignment. The simulation results were consistent with the experimental results and expected effects of variation in physical parameters of the system. The results of this study encourage further investigation and provide adequate evidence that the numerical modeling of a shock wave therapy system is feasible and can provide a practical means to test novel ideas in new device design procedures. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.
Precise determination of anthropometric dimensions by means of image processing methods for estimating human body segment parameter values.

PubMed

Baca, A

1996-04-01

A method has been developed for the precise determination of anthropometric dimensions from the video images of four different body configurations. High precision is achieved by incorporating techniques for finding the location of object boundaries with sub-pixel accuracy, the implementation of calibration algorithms, and by taking into account the varying distances of the body segments from the recording camera. The system allows automatic segment boundary identification from the video image, if the boundaries are marked on the subject by black ribbons. In connection with the mathematical finite-mass-element segment model of Hatze, body segment parameters (volumes, masses, the three principal moments of inertia, the three local coordinates of the segmental mass centers etc.) can be computed by using the anthropometric data determined videometrically as input data. Compared to other, recently published video-based systems for the estimation of the inertial properties of body segments, the present algorithms reduce errors originating from optical distortions, inaccurate edge-detection procedures, and user-specified upper and lower segment boundaries or threshold levels for the edge-detection. The video-based estimation of human body segment parameters is especially useful in situations where ease of application and rapid availability of comparatively precise parameter values are of importance.
The Effects of Earth's Outer Core's Viscosity on Geodynamo Models

NASA Astrophysics Data System (ADS)

Dong, C.; Jiao, L.; Zhang, H.

2017-12-01

Geodynamo process is controlled by mathematic equations and input parameters. To study effects of parameters on geodynamo system, MoSST model has been used to simulate geodynamo outputs under different outer core's viscosity ν. With spanning ν for nearly three orders when other parameters fixed, we studied the variation of each physical field and its typical length scale. We find that variation of ν affects the velocity field intensely. The magnetic field almost decreases monotonically with increasing of ν, while the variation is no larger than 30%. The temperature perturbation increases monotonically with ν, but by a very small magnitude (6%). The averaged velocity field (u) of the liquid core increases with ν as a simple fitted scaling relation: u∝ν0.49. The phenomenon that u increases with ν is essentially that increasing of ν breaks the Taylor-Proudman constraint and drops the critical Rayleigh number, and thus u increases under the same thermal driving force. Forces balance is analyzed and balance mode shifts with variation of ν. When compared with former studies of scaling laws, this study supports the conclusion that in a certain parameter range, the magnetic field strength doesn't vary much with the viscosity, but opposes to the assumption that the velocity field has nothing to do with the outer core viscosity.
Adaptive control of Parkinson's state based on a nonlinear computational model with unknown parameters.

PubMed

Su, Fei; Wang, Jiang; Deng, Bin; Wei, Xi-Le; Chen, Ying-Yuan; Liu, Chen; Li, Hui-Yan

2015-02-01

The objective here is to explore the use of adaptive input-output feedback linearization method to achieve an improved deep brain stimulation (DBS) algorithm for closed-loop control of Parkinson's state. The control law is based on a highly nonlinear computational model of Parkinson's disease (PD) with unknown parameters. The restoration of thalamic relay reliability is formulated as the desired outcome of the adaptive control methodology, and the DBS waveform is the control input. The control input is adjusted in real time according to estimates of unknown parameters as well as the feedback signal. Simulation results show that the proposed adaptive control algorithm succeeds in restoring the relay reliability of the thalamus, and at the same time achieves accurate estimation of unknown parameters. Our findings point to the potential value of adaptive control approach that could be used to regulate DBS waveform in more effective treatment of PD.
Theoretic aspects of the identification of the parameters in the optimal control model

NASA Technical Reports Server (NTRS)

Vanwijk, R. A.; Kok, J. J.

1977-01-01

The identification of the parameters of the optimal control model from input-output data of the human operator is considered. Accepting the basic structure of the model as a cascade of a full-order observer and a feedback law, and suppressing the inherent optimality of the human controller, the parameters to be identified are the feedback matrix, the observer gain matrix, and the intensity matrices of the observation noise and the motor noise. The identification of the parameters is a statistical problem, because the system and output are corrupted by noise, and therefore the solution must be based on the statistics (probability density function) of the input and output data of the human operator. However, based on the statistics of the input-output data of the human operator, no distinction can be made between the observation and the motor noise, which shows that the model suffers from overparameterization.
Estimating unknown input parameters when implementing the NGA ground-motion prediction equations in engineering practice

USGS Publications Warehouse

Kaklamanos, James; Baise, Laurie G.; Boore, David M.

2011-01-01

The ground-motion prediction equations (GMPEs) developed as part of the Next Generation Attenuation of Ground Motions (NGA-West) project in 2008 are becoming widely used in seismic hazard analyses. However, these new models are considerably more complicated than previous GMPEs, and they require several more input parameters. When employing the NGA models, users routinely face situations in which some of the required input parameters are unknown. In this paper, we present a framework for estimating the unknown source, path, and site parameters when implementing the NGA models in engineering practice, and we derive geometrically-based equations relating the three distance measures found in the NGA models. Our intent is for the content of this paper not only to make the NGA models more accessible, but also to help with the implementation of other present or future GMPEs.
Dual side control for inductive power transfer

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

Wu, Hunter; Sealy, Kylee; Gilchrist, Aaron

An apparatus for dual side control includes a measurement module that measures a voltage and a current of an IPT system. The voltage includes an output voltage and/or an input voltage and the current includes an output current and/or an input current. The output voltage and the output current are measured at an output of the IPT system and the input voltage and the input current measured at an input of the IPT system. The apparatus includes a max efficiency module that determines a maximum efficiency for the IPT system. The max efficiency module uses parameters of the IPT systemmore » to iterate to a maximum efficiency. The apparatus includes an adjustment module that adjusts one or more parameters in the IPT system consistent with the maximum efficiency calculated by the max efficiency module.« less
[Tasseled cap triangle (TCT)-leaf area index (LAI)model of rice fields based on PROSAIL model and its application].

PubMed

Li, Ya Ni; Lu, Lei; Liu, Yong

2017-12-01

The tasseled cap triangle (TCT)-leaf area index (LAI) isoline is a model that reflects the distribution of LAI isoline in the spectral space constituted by reflectance of red and near-infrared (NIR) bands, and the LAI retrieval model developed on the basis of this is more accurate than the commonly used statistical relationship models. This study used ground-based measurements of the rice field, validated the applicability of PROSAIL model in simulating canopy reflectance of rice field, and calibrated the input parameters of the model. The ranges of values of PROSAIL input parameters for simulating rice canopy reflectance were determined. Based on this, the TCT-LAI isoline model of rice field was established, and a look-up table (LUT) required in remote sensing retrieval of LAI was developed. Then, the LUT was used for Landsat 8 and WorldView 3 data to retrieve LAI of rice field, respectively. The results showed that the LAI retrieved using the LUT developed from TCT-LAI isoline model had a good linear relationship with the measured LAI R 2 =0.76, RMSE=0.47. Compared with the LAI retrieved from Landsat 8, LAI values retrieved from WorldView 3 va-ried with wider range, and data distribution was more scattered. Resampling the Landsat 8 and WorldView 3 reflectance data to 1 km to retrieve LAI, the result of MODIS LAI product was significantly underestimated compared to that of retrieved LAI.
Estimation of effective connectivity using multi-layer perceptron artificial neural network.

PubMed

Talebi, Nasibeh; Nasrabadi, Ali Motie; Mohammad-Rezazadeh, Iman

2018-02-01

Studies on interactions between brain regions estimate effective connectivity, (usually) based on the causality inferences made on the basis of temporal precedence. In this study, the causal relationship is modeled by a multi-layer perceptron feed-forward artificial neural network, because of the ANN's ability to generate appropriate input-output mapping and to learn from training examples without the need of detailed knowledge of the underlying system. At any time instant, the past samples of data are placed in the network input, and the subsequent values are predicted at its output. To estimate the strength of interactions, the measure of " Causality coefficient " is defined based on the network structure, the connecting weights and the parameters of hidden layer activation function. Simulation analysis demonstrates that the method, called "CREANN" (Causal Relationship Estimation by Artificial Neural Network), can estimate time-invariant and time-varying effective connectivity in terms of MVAR coefficients. The method shows robustness with respect to noise level of data. Furthermore, the estimations are not significantly influenced by the model order (considered time-lag), and the different initial conditions (initial random weights and parameters of the network). CREANN is also applied to EEG data collected during a memory recognition task. The results implicate that it can show changes in the information flow between brain regions, involving in the episodic memory retrieval process. These convincing results emphasize that CREANN can be used as an appropriate method to estimate the causal relationship among brain signals.
Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering.

PubMed

Havlicek, Martin; Friston, Karl J; Jan, Jiri; Brazdil, Milan; Calhoun, Vince D

2011-06-15

This paper presents a new approach to inverting (fitting) models of coupled dynamical systems based on state-of-the-art (cubature) Kalman filtering. Crucially, this inversion furnishes posterior estimates of both the hidden states and parameters of a system, including any unknown exogenous input. Because the underlying generative model is formulated in continuous time (with a discrete observation process) it can be applied to a wide variety of models specified with either ordinary or stochastic differential equations. These are an important class of models that are particularly appropriate for biological time-series, where the underlying system is specified in terms of kinetics or dynamics (i.e., dynamic causal models). We provide comparative evaluations with generalized Bayesian filtering (dynamic expectation maximization) and demonstrate marked improvements in accuracy and computational efficiency. We compare the schemes using a series of difficult (nonlinear) toy examples and conclude with a special focus on hemodynamic models of evoked brain responses in fMRI. Our scheme promises to provide a significant advance in characterizing the functional architectures of distributed neuronal systems, even in the absence of known exogenous (experimental) input; e.g., resting state fMRI studies and spontaneous fluctuations in electrophysiological studies. Importantly, unlike current Bayesian filters (e.g. DEM), our scheme provides estimates of time-varying parameters, which we will exploit in future work on the adaptation and enabling of connections in the brain. Copyright © 2011 Elsevier Inc. All rights reserved.
A Singular Perturbation Approach for Time-Domain Assessment of Phase Margin

NASA Technical Reports Server (NTRS)

Zhu, J. Jim; Yang, Xiaojing; Hodel, A Scottedward

2010-01-01

This paper considers the problem of time-domain assessment of the Phase Margin (PM) of a Single Input Single Output (SISO) Linear Time-Invariant (LTI) system using a singular perturbation approach, where a SISO LTI fast loop system, whose phase lag increases monotonically with frequency, is introduced into the loop as a singular perturbation with a singular perturbation (time-scale separation) parameter Epsilon. First, a bijective relationship between the Singular Perturbation Margin (SPM) max and the PM of the nominal (slow) system is established with an approximation error on the order of Epsilon(exp 2). In proving this result, relationships between the singular perturbation parameter Epsilon, PM of the perturbed system, PM and SPM of the nominal system, and the (monotonically increasing) phase of the fast system are also revealed. These results make it possible to assess the PM of the nominal system in the time-domain for SISO LTI systems using the SPM with a standardized testing system called "PM-gauge," as demonstrated by examples. PM is a widely used stability margin for LTI control system design and certification. Unfortunately, it is not applicable to Linear Time-Varying (LTV) and Nonlinear Time-Varying (NLTV) systems. The approach developed here can be used to establish a theoretical as well as practical metric of stability margin for LTV and NLTV systems using a standardized SPM that is backward compatible with PM.
Combining nutation and surface gravity observations to estimate the Earth's core and inner core resonant frequencies

NASA Astrophysics Data System (ADS)

Ziegler, Yann; Lambert, Sébastien; Rosat, Séverine; Nurul Huda, Ibnu; Bizouard, Christian

2017-04-01

Nutation time series derived from very long baseline interferometry (VLBI) and time varying surface gravity data recorded by superconducting gravimeters (SG) have long been used separately to assess the Earth's interior via the estimation of the free core and inner core resonance effects on nutation or tidal gravity. The results obtained from these two techniques have been shown recently to be consistent, making relevant the combination of VLBI and SG observables and the estimation of Earth's interior parameters in a single inversion. We present here the intermediate results of the ongoing project of combining nutation and surface gravity time series to improve estimates of the Earth's core and inner core resonant frequencies. We use VLBI nutation time series spanning 1984-2016 derived by the International VLBI Service for geodesy and astrometry (IVS) as the result of a combination of inputs from various IVS analysis centers, and surface gravity data from about 15 SG stations. We address here the resonance model used for describing the Earth's interior response to tidal excitation, the data preparation consisting of the error recalibration and amplitude fitting for nutation data, and processing of SG time-varying gravity to remove any gaps, spikes, steps and other disturbances, followed by the tidal analysis with the ETERNA 3.4 software package, the preliminary estimates of the resonant periods, and the correlations between parameters.
Time-dependent Data System (TDDS); an interactive program to assemble, manage, and appraise input data and numerical output of flow/transport simulation models

USGS Publications Warehouse

Regan, R.S.; Schaffranek, R.W.; Baltzer, R.A.

1996-01-01

A system of functional utilities and computer routines, collectively identified as the Time-Dependent Data System CI DDS), has been developed and documented by the U.S. Geological Survey. The TDDS is designed for processing time sequences of discrete, fixed-interval, time-varying geophysical data--in particular, hydrologic data. Such data include various, dependent variables and related parameters typically needed as input for execution of one-, two-, and three-dimensional hydrodynamic/transport and associated water-quality simulation models. Such data can also include time sequences of results generated by numerical simulation models. Specifically, TDDS provides the functional capabilities to process, store, retrieve, and compile data in a Time-Dependent Data Base (TDDB) in response to interactive user commands or pre-programmed directives. Thus, the TDDS, in conjunction with a companion TDDB, provides a ready means for processing, preparation, and assembly of time sequences of data for input to models; collection, categorization, and storage of simulation results from models; and intercomparison of field data and simulation results. The TDDS can be used to edit and verify prototype, time-dependent data to affirm that selected sequences of data are accurate, contiguous, and appropriate for numerical simulation modeling. It can be used to prepare time-varying data in a variety of formats, such as tabular lists, sequential files, arrays, graphical displays, as well as line-printer plots of single or multiparameter data sets. The TDDB is organized and maintained as a direct-access data base by the TDDS, thus providing simple, yet efficient, data management and access. A single, easily used, program interface that provides all access to and from a particular TDDB is available for use directly within models, other user-provided programs, and other data systems. This interface, together with each major functional utility of the TDDS, is described and documented in this report.
Sensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and States.

PubMed

Vargas-Melendez, Leandro; Boada, Beatriz L; Boada, Maria Jesus L; Gauchia, Antonio; Diaz, Vicente

2017-04-29

Vehicles with a high center of gravity (COG), such as light trucks and heavy vehicles, are prone to rollover. This kind of accident causes nearly 33 % of all deaths from passenger vehicle crashes. Nowadays, these vehicles are incorporating roll stability control (RSC) systems to improve their safety. Most of the RSC systems require the vehicle roll angle as a known input variable to predict the lateral load transfer. The vehicle roll angle can be directly measured by a dual antenna global positioning system (GPS), but it is expensive. For this reason, it is important to estimate the vehicle roll angle from sensors installed onboard in current vehicles. On the other hand, the knowledge of the vehicle's parameters values is essential to obtain an accurate vehicle response. Some of vehicle parameters cannot be easily obtained and they can vary over time. In this paper, an algorithm for the simultaneous on-line estimation of vehicle's roll angle and parameters is proposed. This algorithm uses a probability density function (PDF)-based truncation method in combination with a dual Kalman filter (DKF), to guarantee that both vehicle's states and parameters are within bounds that have a physical meaning, using the information obtained from sensors mounted on vehicles. Experimental results show the effectiveness of the proposed algorithm.
Sensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and States

PubMed Central

Vargas-Melendez, Leandro; Boada, Beatriz L.; Boada, Maria Jesus L.; Gauchia, Antonio; Diaz, Vicente

2017-01-01

Vehicles with a high center of gravity (COG), such as light trucks and heavy vehicles, are prone to rollover. This kind of accident causes nearly 33% of all deaths from passenger vehicle crashes. Nowadays, these vehicles are incorporating roll stability control (RSC) systems to improve their safety. Most of the RSC systems require the vehicle roll angle as a known input variable to predict the lateral load transfer. The vehicle roll angle can be directly measured by a dual antenna global positioning system (GPS), but it is expensive. For this reason, it is important to estimate the vehicle roll angle from sensors installed onboard in current vehicles. On the other hand, the knowledge of the vehicle’s parameters values is essential to obtain an accurate vehicle response. Some of vehicle parameters cannot be easily obtained and they can vary over time. In this paper, an algorithm for the simultaneous on-line estimation of vehicle’s roll angle and parameters is proposed. This algorithm uses a probability density function (PDF)-based truncation method in combination with a dual Kalman filter (DKF), to guarantee that both vehicle’s states and parameters are within bounds that have a physical meaning, using the information obtained from sensors mounted on vehicles. Experimental results show the effectiveness of the proposed algorithm. PMID:28468252
Input design for identification of aircraft stability and control derivatives

NASA Technical Reports Server (NTRS)

Gupta, N. K.; Hall, W. E., Jr.

1975-01-01

An approach for designing inputs to identify stability and control derivatives from flight test data is presented. This approach is based on finding inputs which provide the maximum possible accuracy of derivative estimates. Two techniques of input specification are implemented for this objective - a time domain technique and a frequency domain technique. The time domain technique gives the control input time history and can be used for any allowable duration of test maneuver, including those where data lengths can only be of short duration. The frequency domain technique specifies the input frequency spectrum, and is best applied for tests where extended data lengths, much longer than the time constants of the modes of interest, are possible. These technqiues are used to design inputs to identify parameters in longitudinal and lateral linear models of conventional aircraft. The constraints of aircraft response limits, such as on structural loads, are realized indirectly through a total energy constraint on the input. Tests with simulated data and theoretical predictions show that the new approaches give input signals which can provide more accurate parameter estimates than can conventional inputs of the same total energy. Results obtained indicate that the approach has been brought to the point where it should be used on flight tests for further evaluation.
Computational solution verification and validation applied to a thermal model of a ruggedized instrumentation package

DOE PAGES

Scott, Sarah Nicole; Templeton, Jeremy Alan; Hough, Patricia Diane; ...

2014-01-01

This study details a methodology for quantification of errors and uncertainties of a finite element heat transfer model applied to a Ruggedized Instrumentation Package (RIP). The proposed verification and validation (V&V) process includes solution verification to examine errors associated with the code's solution techniques, and model validation to assess the model's predictive capability for quantities of interest. The model was subjected to mesh resolution and numerical parameters sensitivity studies to determine reasonable parameter values and to understand how they change the overall model response and performance criteria. To facilitate quantification of the uncertainty associated with the mesh, automatic meshing andmore » mesh refining/coarsening algorithms were created and implemented on the complex geometry of the RIP. Automated software to vary model inputs was also developed to determine the solution’s sensitivity to numerical and physical parameters. The model was compared with an experiment to demonstrate its accuracy and determine the importance of both modelled and unmodelled physics in quantifying the results' uncertainty. An emphasis is placed on automating the V&V process to enable uncertainty quantification within tight development schedules.« less
Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network.

PubMed

De Filippis, Luigi Alberto Ciro; Serio, Livia Maria; Facchini, Francesco; Mummolo, Giovanni; Ludovico, Antonio Domenico

2016-11-10

A simulation model was developed for the monitoring, controlling and optimization of the Friction Stir Welding (FSW) process. This approach, using the FSW technique, allows identifying the correlation between the process parameters (input variable) and the mechanical properties (output responses) of the welded AA5754 H111 aluminum plates. The optimization of technological parameters is a basic requirement for increasing the seam quality, since it promotes a stable and defect-free process. Both the tool rotation and the travel speed, the position of the samples extracted from the weld bead and the thermal data, detected with thermographic techniques for on-line control of the joints, were varied to build the experimental plans. The quality of joints was evaluated through destructive and non-destructive tests (visual tests, macro graphic analysis, tensile tests, indentation Vickers hardness tests and t thermographic controls). The simulation model was based on the adoption of the Artificial Neural Networks (ANNs) characterized by back-propagation learning algorithm with different types of architecture, which were able to predict with good reliability the FSW process parameters for the welding of the AA5754 H111 aluminum plates in Butt-Joint configuration.
Modal parameter estimation and monitoring for on-line flight flutter analysis

NASA Astrophysics Data System (ADS)

Verboven, P.; Cauberghe, B.; Guillaume, P.; Vanlanduit, S.; Parloo, E.

2004-05-01

The clearance of the flight envelope of a new airplane by means of flight flutter testing is time consuming and expensive. Most common approach is to track the modal damping ratios during a number of flight conditions, and hence the accuracy of the damping estimates plays a crucial role. However, aircraft manufacturers desire to decrease the flight flutter testing time for practical, safety and economical reasons by evolving from discrete flight test points to a more continuous flight test pattern. Therefore, this paper presents an approach that provides modal parameter estimation and monitoring for an aircraft with a slowly time-varying structural behaviour that will be observed during a faster and more continuous exploration of the flight envelope. The proposed identification approach estimates the modal parameters directly from input/output Fourier data. This avoids the need for an averaging-based pre-processing of the data, which becomes inapplicable in the case that only short data records are measured. Instead of using a Hanning window to reduce effects of leakage, these transient effects are modelled simultaneously with the dynamical behaviour of the airplane. The method is validated for the monitoring of the system poles during flight flutter testing.

Quantitative analysis of the anti-noise performance of an m-sequence in an electromagnetic method

NASA Astrophysics Data System (ADS)

Yuan, Zhe; Zhang, Yiming; Zheng, Qijia

2018-02-01

An electromagnetic method with a transmitted waveform coded by an m-sequence achieved better anti-noise performance compared to the conventional manner with a square-wave. The anti-noise performance of the m-sequence varied with multiple coding parameters; hence, a quantitative analysis of the anti-noise performance for m-sequences with different coding parameters was required to optimize them. This paper proposes the concept of an identification system, with the identified Earth impulse response obtained by measuring the system output with the input of the voltage response. A quantitative analysis of the anti-noise performance of the m-sequence was achieved by analyzing the amplitude-frequency response of the corresponding identification system. The effects of the coding parameters on the anti-noise performance are summarized by numerical simulation, and their optimization is further discussed in our conclusions; the validity of the conclusions is further verified by field experiment. The quantitative analysis method proposed in this paper provides a new insight into the anti-noise mechanism of the m-sequence, and could be used to evaluate the anti-noise performance of artificial sources in other time-domain exploration methods, such as the seismic method.
Monte Carlo simulation of electrothermal atomization on a desktop personal computer

NASA Astrophysics Data System (ADS)

Histen, Timothy E.; Güell, Oscar A.; Chavez, Iris A.; Holcombea, James A.

1996-07-01

Monte Carlo simulations have been applied to electrothermal atomization (ETA) using a tubular atomizer (e.g. graphite furnace) because of the complexity in the geometry, heating, molecular interactions, etc. The intense computational time needed to accurately model ETA often limited its effective implementation to the use of supercomputers. However, with the advent of more powerful desktop processors, this is no longer the case. A C-based program has been developed and can be used under Windows TM or DOS. With this program, basic parameters such as furnace dimensions, sample placement, furnace heating and kinetic parameters such as activation energies for desorption and adsorption can be varied to show the absorbance profile dependence on these parameters. Even data such as time-dependent spatial distribution of analyte inside the furnace can be collected. The DOS version also permits input of external temperaturetime data to permit comparison of simulated profiles with experimentally obtained absorbance data. The run-time versions are provided along with the source code. This article is an electronic publication in Spectrochimica Acta Electronica (SAE), the electronic section of Spectrochimica Acta Part B (SAB). The hardcopy text is accompanied by a diskette with a program (PC format), data files and text files.
Consistency relations for sharp features in the primordial spectra

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

Mooij, Sander; Palma, Gonzalo A.; Panotopoulos, Grigoris

We study the generation of sharp features in the primordial spectra within the framework of effective field theory of inflation, wherein curvature perturbations are the consequence of the dynamics of a single scalar degree of freedom. We identify two sources in the generation of features: rapid variations of the sound speed c{sub s} (at which curvature fluctuations propagate) and rapid variations of the expansion rate H during inflation. With this in mind, we propose a non-trivial relation linking these two quantities that allows us to study the generation of sharp features in realistic scenarios where features are the result ofmore » the simultaneous occurrence of these two sources. This relation depends on a single parameter with a value determined by the particular model (and its numerical input) responsible for the rapidly varying background. As a consequence, we find a one-parameter consistency relation between the shape and size of features in the bispectrum and features in the power spectrum. To substantiate this result, we discuss several examples of models for which this one-parameter relation (between c{sub s} and H) holds, including models in which features in the spectra are both sudden and resonant.« less
High-Tech Versus High-Touch: Components of Hospital Costs Vary Widely.

PubMed

Song, Paula H; Reiter, Kristin L; Yi Xu, Wendy

The recent release by the Centers for Medicare & Medicaid Services of hospital charge and payment data to the public has renewed a national dialogue on hospital costs and prices. However, to better understand the driving force of hospital pricing and to develop strategies for controlling expenditures, it is important to understand the underlying costs of providing hospital services. We use Medicare Provider and Analysis Review inpatient claims data and Medicare cost report data for fiscal years 2008 and 2012 to examine variations in the contribution of "high-tech" resources (i.e., technology/medical device-intensive resources) versus "high-touch" resources (i.e., labor-intensive resources) to the total costs of providing two common services, as well as assess how these costs have changed over time. We found that high-tech inputs accounted for a greater proportion of the total costs of surgical service, whereas medical service costs were primarily attributable to high-touch inputs. Although the total costs of services did not change significantly over time, the distribution of high-tech, high-touch, and other costs for each service varied considerably across hospitals. Understanding resource inputs and the varying contribution of these inputs by clinical condition is an important first step in developing effective cost control strategies.
Optimal Control for Aperiodic Dual-Rate Systems With Time-Varying Delays

PubMed Central

Salt, Julián; Guinaldo, María; Chacón, Jesús

2018-01-01

In this work, we consider a dual-rate scenario with slow input and fast output. Our objective is the maximization of the decay rate of the system through the suitable choice of the n-input signals between two measures (periodic sampling) and their times of application. The optimization algorithm is extended for time-varying delays in order to make possible its implementation in networked control systems. We provide experimental results in an air levitation system to verify the validity of the algorithm in a real plant. PMID:29747441
Optimal Control for Aperiodic Dual-Rate Systems With Time-Varying Delays.

PubMed

Aranda-Escolástico, Ernesto; Salt, Julián; Guinaldo, María; Chacón, Jesús; Dormido, Sebastián

2018-05-09

In this work, we consider a dual-rate scenario with slow input and fast output. Our objective is the maximization of the decay rate of the system through the suitable choice of the n -input signals between two measures (periodic sampling) and their times of application. The optimization algorithm is extended for time-varying delays in order to make possible its implementation in networked control systems. We provide experimental results in an air levitation system to verify the validity of the algorithm in a real plant.
Measurement of myocardial blood flow by cardiovascular magnetic resonance perfusion: comparison of distributed parameter and Fermi models with single and dual bolus.

PubMed

Papanastasiou, Giorgos; Williams, Michelle C; Kershaw, Lucy E; Dweck, Marc R; Alam, Shirjel; Mirsadraee, Saeed; Connell, Martin; Gray, Calum; MacGillivray, Tom; Newby, David E; Semple, Scott Ik

2015-02-17

Mathematical modeling of cardiovascular magnetic resonance perfusion data allows absolute quantification of myocardial blood flow. Saturation of left ventricle signal during standard contrast administration can compromise the input function used when applying these models. This saturation effect is evident during application of standard Fermi models in single bolus perfusion data. Dual bolus injection protocols have been suggested to eliminate saturation but are much less practical in the clinical setting. The distributed parameter model can also be used for absolute quantification but has not been applied in patients with coronary artery disease. We assessed whether distributed parameter modeling might be less dependent on arterial input function saturation than Fermi modeling in healthy volunteers. We validated the accuracy of each model in detecting reduced myocardial blood flow in stenotic vessels versus gold-standard invasive methods. Eight healthy subjects were scanned using a dual bolus cardiac perfusion protocol at 3T. We performed both single and dual bolus analysis of these data using the distributed parameter and Fermi models. For the dual bolus analysis, a scaled pre-bolus arterial input function was used. In single bolus analysis, the arterial input function was extracted from the main bolus. We also performed analysis using both models of single bolus data obtained from five patients with coronary artery disease and findings were compared against independent invasive coronary angiography and fractional flow reserve. Statistical significance was defined as two-sided P value < 0.05. Fermi models overestimated myocardial blood flow in healthy volunteers due to arterial input function saturation in single bolus analysis compared to dual bolus analysis (P < 0.05). No difference was observed in these volunteers when applying distributed parameter-myocardial blood flow between single and dual bolus analysis. In patients, distributed parameter modeling was able to detect reduced myocardial blood flow at stress (<2.5 mL/min/mL of tissue) in all 12 stenotic vessels compared to only 9 for Fermi modeling. Comparison of single bolus versus dual bolus values suggests that distributed parameter modeling is less dependent on arterial input function saturation than Fermi modeling. Distributed parameter modeling showed excellent accuracy in detecting reduced myocardial blood flow in all stenotic vessels.
Modeling Coronal Mass Ejections with EUHFORIA: A Parameter Study of the Gibson-Low Flux Rope Model using Multi-Viewpoint Observations

NASA Astrophysics Data System (ADS)

Verbeke, C.; Asvestari, E.; Scolini, C.; Pomoell, J.; Poedts, S.; Kilpua, E.

2017-12-01

Coronal Mass Ejections (CMEs) are one of the big influencers on the coronal and interplanetary dynamics. Understanding their origin and evolution from the Sun to the Earth is crucial in order to determine the impact on our Earth and society. One of the key parameters that determine the geo-effectiveness of the coronal mass ejection is its internal magnetic configuration. We present a detailed parameter study of the Gibson-Low flux rope model. We focus on changes in the input parameters and how these changes affect the characteristics of the CME at Earth. Recently, the Gibson-Low flux rope model has been implemented into the inner heliosphere model EUHFORIA, a magnetohydrodynamics forecasting model of large-scale dynamics from 0.1 AU up to 2 AU. Coronagraph observations can be used to constrain the kinematics and morphology of the flux rope. One of the key parameters, the magnetic field, is difficult to determine directly from observations. In this work, we approach the problem by conducting a parameter study in which flux ropes with varying magnetic configurations are simulated. We then use the obtained dataset to look for signatures in imaging observations and in-situ observations in order to find an empirical way of constraining the parameters related to the magnetic field of the flux rope. In particular, we focus on events observed by at least two spacecraft (STEREO + L1) in order to discuss the merits of using observations from multiple viewpoints in constraining the parameters.
6 DOF synchronized control for spacecraft formation flying with input constraint and parameter uncertainties.

PubMed

Lv, Yueyong; Hu, Qinglei; Ma, Guangfu; Zhou, Jiakang

2011-10-01

This paper treats the problem of synchronized control of spacecraft formation flying (SFF) in the presence of input constraint and parameter uncertainties. More specifically, backstepping based robust control is first developed for the total 6 DOF dynamic model of SFF with parameter uncertainties, in which the model consists of relative translation and attitude rotation. Then this controller is redesigned to deal with the input constraint problem by incorporating a command filter such that the generated control could be implementable even under physical or operating constraints on the control input. The convergence of the proposed control algorithms is proved by the Lyapunov stability theorem. Compared with conventional methods, illustrative simulations of spacecraft formation flying are conducted to verify the effectiveness of the proposed approach to achieve the spacecraft track the desired attitude and position trajectories in a synchronized fashion even in the presence of uncertainties, external disturbances and control saturation constraint. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.
Performances estimation of a rotary traveling wave ultrasonic motor based on two-dimension analytical model.

PubMed

Ming, Y; Peiwen, Q

2001-03-01

The understanding of ultrasonic motor performances as a function of input parameters, such as the voltage amplitude, driving frequency, the preload on the rotor, is a key to many applications and control of ultrasonic motor. This paper presents performances estimation of the piezoelectric rotary traveling wave ultrasonic motor as a function of input voltage amplitude and driving frequency and preload. The Love equation is used to derive the traveling wave amplitude on the stator surface. With the contact model of the distributed spring-rigid body between the stator and rotor, a two-dimension analytical model of the rotary traveling wave ultrasonic motor is constructed. Then the performances of stead rotation speed and stall torque are deduced. With MATLAB computational language and iteration algorithm, we estimate the performances of rotation speed and stall torque versus input parameters respectively. The same experiments are completed with the optoelectronic tachometer and stand weight. Both estimation and experiment results reveal the pattern of performance variation as a function of its input parameters.
Desktop Application Program to Simulate Cargo-Air-Drop Tests

NASA Technical Reports Server (NTRS)

Cuthbert, Peter

2009-01-01

The DSS Application is a computer program comprising a Windows version of the UNIX-based Decelerator System Simulation (DSS) coupled with an Excel front end. The DSS is an executable code that simulates the dynamics of airdropped cargo from first motion in an aircraft through landing. The bare DSS is difficult to use; the front end makes it easy to use. All inputs to the DSS, control of execution of the DSS, and postprocessing and plotting of outputs are handled in the front end. The front end is graphics-intensive. The Excel software provides the graphical elements without need for additional programming. Categories of input parameters are divided into separate tabbed windows. Pop-up comments describe each parameter. An error-checking software component evaluates combinations of parameters and alerts the user if an error results. Case files can be created from inputs, making it possible to build cases from previous ones. Simulation output is plotted in 16 charts displayed on a separate worksheet, enabling plotting of multiple DSS cases with flight-test data. Variables assigned to each plot can be changed. Selected input parameters can be edited from the plot sheet for quick sensitivity studies.
Automated method for the systematic interpretation of resonance peaks in spectrum data

DOEpatents

Damiano, B.; Wood, R.T.

1997-04-22

A method is described for spectral signature interpretation. The method includes the creation of a mathematical model of a system or process. A neural network training set is then developed based upon the mathematical model. The neural network training set is developed by using the mathematical model to generate measurable phenomena of the system or process based upon model input parameter that correspond to the physical condition of the system or process. The neural network training set is then used to adjust internal parameters of a neural network. The physical condition of an actual system or process represented by the mathematical model is then monitored by extracting spectral features from measured spectra of the actual process or system. The spectral features are then input into said neural network to determine the physical condition of the system or process represented by the mathematical model. More specifically, the neural network correlates the spectral features (i.e. measurable phenomena) of the actual process or system with the corresponding model input parameters. The model input parameters relate to specific components of the system or process, and, consequently, correspond to the physical condition of the process or system. 1 fig.
Automated method for the systematic interpretation of resonance peaks in spectrum data

DOEpatents

Damiano, Brian; Wood, Richard T.

1997-01-01

A method for spectral signature interpretation. The method includes the creation of a mathematical model of a system or process. A neural network training set is then developed based upon the mathematical model. The neural network training set is developed by using the mathematical model to generate measurable phenomena of the system or process based upon model input parameter that correspond to the physical condition of the system or process. The neural network training set is then used to adjust internal parameters of a neural network. The physical condition of an actual system or process represented by the mathematical model is then monitored by extracting spectral features from measured spectra of the actual process or system. The spectral features are then input into said neural network to determine the physical condition of the system or process represented by the mathematical. More specifically, the neural network correlates the spectral features (i.e. measurable phenomena) of the actual process or system with the corresponding model input parameters. The model input parameters relate to specific components of the system or process, and, consequently, correspond to the physical condition of the process or system.
Ring rolling process simulation for microstructure optimization

NASA Astrophysics Data System (ADS)

Franchi, Rodolfo; Del Prete, Antonio; Donatiello, Iolanda; Calabrese, Maurizio

2017-10-01

Metal undergoes complicated microstructural evolution during Hot Ring Rolling (HRR), which determines the quality, mechanical properties and life of the ring formed. One of the principal microstructure properties which mostly influences the structural performances of forged components, is the value of the average grain size. In the present paper a ring rolling process has been studied and optimized in order to obtain anular components to be used in aerospace applications. In particular, the influence of process input parameters (feed rate of the mandrel and angular velocity of driver roll) on microstructural and on geometrical features of the final ring has been evaluated. For this purpose, a three-dimensional finite element model for HRR has been developed in SFTC DEFORM V11, taking into account also microstructural development of the material used (the nickel superalloy Waspalloy). The Finite Element (FE) model has been used to formulate a proper optimization problem. The optimization procedure has been developed in order to find the combination of process parameters which allows to minimize the average grain size. The Response Surface Methodology (RSM) has been used to find the relationship between input and output parameters, by using the exact values of output parameters in the control points of a design space explored through FEM simulation. Once this relationship is known, the values of the output parameters can be calculated for each combination of the input parameters. Then, an optimization procedure based on Genetic Algorithms has been applied. At the end, the minimum value of average grain size with respect to the input parameters has been found.
VizieR Online Data Catalog: Planetary atmosphere radiative transport code (Garcia Munoz+ 2015)

NASA Astrophysics Data System (ADS)

Garcia Munoz, A.; Mills, F. P.

2014-08-01

Files are: * readme.txt * Input files: INPUThazeL.txt, INPUTL13.txt, INPUT_L60.txt; they contain explanations to the input parameters. Copy INPUT_XXXX.txt into INPUT.dat to execute some of the examples described in the reference. * Files with scattering matrix properties: phFhazeL.txt, phFL13.txt, phF_L60.txt * Script for compilation in GFortran (myscript) (10 data files).
Robust Blind Learning Algorithm for Nonlinear Equalization Using Input Decision Information.

PubMed

Xu, Lu; Huang, Defeng David; Guo, Yingjie Jay

2015-12-01

In this paper, we propose a new blind learning algorithm, namely, the Benveniste-Goursat input-output decision (BG-IOD), to enhance the convergence performance of neural network-based equalizers for nonlinear channel equalization. In contrast to conventional blind learning algorithms, where only the output of the equalizer is employed for updating system parameters, the BG-IOD exploits a new type of extra information, the input decision information obtained from the input of the equalizer, to mitigate the influence of the nonlinear equalizer structure on parameters learning, thereby leading to improved convergence performance. We prove that, with the input decision information, a desirable convergence capability that the output symbol error rate (SER) is always less than the input SER if the input SER is below a threshold, can be achieved. Then, the BG soft-switching technique is employed to combine the merits of both input and output decision information, where the former is used to guarantee SER convergence and the latter is to improve SER performance. Simulation results show that the proposed algorithm outperforms conventional blind learning algorithms, such as stochastic quadratic distance and dual mode constant modulus algorithm, in terms of both convergence performance and SER performance, for nonlinear equalization.
Rapid Airplane Parametric Input Design (RAPID)

NASA Technical Reports Server (NTRS)

Smith, Robert E.

1995-01-01

RAPID is a methodology and software system to define a class of airplane configurations and directly evaluate surface grids, volume grids, and grid sensitivity on and about the configurations. A distinguishing characteristic which separates RAPID from other airplane surface modellers is that the output grids and grid sensitivity are directly applicable in CFD analysis. A small set of design parameters and grid control parameters govern the process which is incorporated into interactive software for 'real time' visual analysis and into batch software for the application of optimization technology. The computed surface grids and volume grids are suitable for a wide range of Computational Fluid Dynamics (CFD) simulation. The general airplane configuration has wing, fuselage, horizontal tail, and vertical tail components. The double-delta wing and tail components are manifested by solving a fourth order partial differential equation (PDE) subject to Dirichlet and Neumann boundary conditions. The design parameters are incorporated into the boundary conditions and therefore govern the shapes of the surfaces. The PDE solution yields a smooth transition between boundaries. Surface grids suitable for CFD calculation are created by establishing an H-type topology about the configuration and incorporating grid spacing functions in the PDE equation for the lifting components and the fuselage definition equations. User specified grid parameters govern the location and degree of grid concentration. A two-block volume grid about a configuration is calculated using the Control Point Form (CPF) technique. The interactive software, which runs on Silicon Graphics IRIS workstations, allows design parameters to be continuously varied and the resulting surface grid to be observed in real time. The batch software computes both the surface and volume grids and also computes the sensitivity of the output grid with respect to the input design parameters by applying the precompiler tool ADIFOR to the grid generation program. The output of ADIFOR is a new source code containing the old code plus expressions for derivatives of specified dependent variables (grid coordinates) with respect to specified independent variables (design parameters). The RAPID methodology and software provide a means of rapidly defining numerical prototypes, grids, and grid sensitivity of a class of airplane configurations. This technology and software is highly useful for CFD research for preliminary design and optimization processes.
COSP for Windows: Strategies for Rapid Analyses of Cyclic Oxidation Behavior

NASA Technical Reports Server (NTRS)

Smialek, James L.; Auping, Judith V.

2002-01-01

COSP is a publicly available computer program that models the cyclic oxidation weight gain and spallation process. Inputs to the model include the selection of an oxidation growth law and a spalling geometry, plus oxide phase, growth rate, spall constant, and cycle duration parameters. Output includes weight change, the amounts of retained and spalled oxide, the total oxygen and metal consumed, and the terminal rates of weight loss and metal consumption. The present version is Windows based and can accordingly be operated conveniently while other applications remain open for importing experimental weight change data, storing model output data, or plotting model curves. Point-and-click operating features include multiple drop-down menus for input parameters, data importing, and quick, on-screen plots showing one selection of the six output parameters for up to 10 models. A run summary text lists various characteristic parameters that are helpful in describing cyclic behavior, such as the maximum weight change, the number of cycles to reach the maximum weight gain or zero weight change, the ratio of these, and the final rate of weight loss. The program includes save and print options as well as a help file. Families of model curves readily show the sensitivity to various input parameters. The cyclic behaviors of nickel aluminide (NiAl) and a complex superalloy are shown to be properly fitted by model curves. However, caution is always advised regarding the uniqueness claimed for any specific set of input parameters,
Development of advanced techniques for rotorcraft state estimation and parameter identification

NASA Technical Reports Server (NTRS)

Hall, W. E., Jr.; Bohn, J. G.; Vincent, J. H.

1980-01-01

An integrated methodology for rotorcraft system identification consists of rotorcraft mathematical modeling, three distinct data processing steps, and a technique for designing inputs to improve the identifiability of the data. These elements are as follows: (1) a Kalman filter smoother algorithm which estimates states and sensor errors from error corrupted data. Gust time histories and statistics may also be estimated; (2) a model structure estimation algorithm for isolating a model which adequately explains the data; (3) a maximum likelihood algorithm for estimating the parameters and estimates for the variance of these estimates; and (4) an input design algorithm, based on a maximum likelihood approach, which provides inputs to improve the accuracy of parameter estimates. Each step is discussed with examples to both flight and simulated data cases.
Emissions-critical charge cooling using an organic rankine cycle

DOEpatents

Ernst, Timothy C.; Nelson, Christopher R.

2014-07-15

The disclosure provides a system including a Rankine power cycle cooling subsystem providing emissions-critical charge cooling of an input charge flow. The system includes a boiler fluidly coupled to the input charge flow, an energy conversion device fluidly coupled to the boiler, a condenser fluidly coupled to the energy conversion device, a pump fluidly coupled to the condenser and the boiler, an adjuster that adjusts at least one parameter of the Rankine power cycle subsystem to change a temperature of the input charge exiting the boiler, and a sensor adapted to sense a temperature characteristic of the vaporized input charge. The system includes a controller that can determine a target temperature of the input charge sufficient to meet or exceed predetermined target emissions and cause the adjuster to adjust at least one parameter of the Rankine power cycle to achieve the predetermined target emissions.

TU-FG-209-04: Testing of Digital Image Receptors Using AAPM TG-150’s Draft Recommendations - Investigating the Impact of Different Processing Parameters

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

Finley, C; Dave, J

Purpose: To evaluate implementation of AAPM TG-150’s draft recommendations via a parameter study for testing the performance of digital image receptors. Methods: Flat field images were acquired from 9 calibrated digital image receptors associated with 9 new portable digital radiography systems (Carestream Health, Inc.) based on the draft recommendations and manufacturer-specified calibration conditions (set of 4 images at input detector air kerma ranging from 1 to 25 µGy). Effects of exposure response function (linearized and logarithmic), ‘Presentation Intent Type’ (‘For Processing’ and ‘For Presentation’), detector orientation with respect to the anode-cathode axis (4 orientations; 900 rotations per iteration), different ROImore » sizes (5×5–40×40 mm{sup 2}) and elimination of varying dimensions of image border (0 mm i.e., without boundary elimination to 150 mm) on signal, noise, signal-to-noise ratio (SNR) and the associated nonuniformities were evaluated. Images were analyzed in Matlab and quantities were compared using ANOVA. Results: Signal, noise and SNR values averaged over 9 systems with default parameter values in draft recommendations were 4837.2±139.4, 19.7±0.9 and 246.4±10.1 (mean ± standard deviation), respectively (at input detector air kerma: 12.5 µGy). Signal, noise and SNR showed characteristic dependency on exposure response function and on ‘Presentation Intent Type’. These values were not affected by ROI size and detector orientation, but analysis showed that eliminating the edge pixels along the boundary was required for the noise parameter (coefficient of variation range for noise: 72%–106% and 3%–4% without and with boundary elimination; respectively). Local and global nonuniformities showed a similar dependence on the need for boundary elimination. Interestingly, computed non-uniformities showed agreement with manufacturer-reported values except for noise non-uniformities in two units; artifacts were seen in images from these two units highlighting the importance of independent evaluations. Conclusion: The effect of different parameters on performance characterization of digital image receptors was evaluated based on TG-150’s draft recommendations.« less
Applying an orographic precipitation model to improve mass balance modeling of the Juneau Icefield, AK

NASA Astrophysics Data System (ADS)

Roth, A. C.; Hock, R.; Schuler, T.; Bieniek, P.; Aschwanden, A.

2017-12-01

Mass loss from glaciers in Southeast Alaska is expected to alter downstream ecological systems as runoff patterns change. To investigate these potential changes under future climate scenarios, distributed glacier mass balance modeling is required. However, the spatial resolution gap between global or regional climate models and the requirements for glacier mass balance modeling studies must be addressed first. We have used a linear theory of orographic precipitation model to downscale precipitation from both the Weather Research and Forecasting (WRF) model and ERA-Interim to the Juneau Icefield region over the period 1979-2013. This implementation of the LT model is a unique parameterization that relies on the specification of snow fall speed and rain fall speed as tuning parameters to calculate the cloud time delay, τ. We assessed the LT model results by considering winter precipitation so the effect of melt was minimized. The downscaled precipitation pattern produced by the LT model captures the orographic precipitation pattern absent from the coarse resolution WRF and ERA-Interim precipitation fields. Observational data constraints limited our ability to determine a unique parameter combination and calibrate the LT model to glaciological observations. We established a reference run of parameter values based on literature and performed a sensitivity analysis of the LT model parameters, horizontal resolution, and climate input data on the average winter precipitation. The results of the reference run showed reasonable agreement with the available glaciological measurements. The precipitation pattern produced by the LT model was consistent regardless of parameter combination, horizontal resolution, and climate input data, but the precipitation amount varied strongly with these factors. Due to the consistency of the winter precipitation pattern and the uncertainty in precipitation amount, we suggest a precipitation index map approach to be used in combination with a distributed mass balance model for future mass balance modeling studies of the Juneau Icefield. The LT model has potential to be used in other regions in Alaska and elsewhere with strong orographic effects for improved glacier mass balance modeling and/or hydrological modeling.
Master control data handling program uses automatic data input

NASA Technical Reports Server (NTRS)

Alliston, W.; Daniel, J.

1967-01-01

General purpose digital computer program is applicable for use with analysis programs that require basic data and calculated parameters as input. It is designed to automate input data preparation for flight control computer programs, but it is general enough to permit application in other areas.
Variation in active and passive resource inputs to experimental pools: mechanisms and possible consequences for food webs

USGS Publications Warehouse

Kraus, Johanna M.; Pletcher, Leanna T.; Vonesh, James R.

2010-01-01

1. Cross-ecosystem movements of resources, including detritus, nutrients and living prey, can strongly influence food web dynamics in recipient habitats. Variation in resource inputs is thought to be driven by factors external to the recipient habitat (e.g. donor habitat productivity and boundary conditions). However, inputs of or by ‘active’ living resources may be strongly influenced by recipient habitat quality when organisms exhibit behavioural habitat selection when crossing ecosystem boundaries. 2. To examine whether behavioural responses to recipient habitat quality alter the relative inputs of ‘active’ living and ‘passive’ detrital resources to recipient food webs, we manipulated the presence of caged predatory fish and measured biomass, energy and organic content of inputs to outdoor experimental pools of adult aquatic insects, frog eggs, terrestrial plant matter and terrestrial arthropods. 3. Caged fish reduced the biomass, energy and organic matter donated to pools by tree frog eggs by ∼70%, but did not alter insect colonisation or passive allochthonous inputs of terrestrial arthropods and plant material. Terrestrial plant matter and adult aquatic insects provided the most energy and organic matter inputs to the pools (40–50%), while terrestrial arthropods provided the least (7%). Inputs of frog egg were relatively small but varied considerably among pools and over time (3%, range = 0–20%). Absolute and proportional amounts varied by input type. 4. Aquatic predators can strongly affect the magnitude of active, but not passive, inputs and that the effect of recipient habitat quality on active inputs is variable. Furthermore, some active inputs (i.e. aquatic insect colonists) can provide similar amounts of energy and organic matter as passive inputs of terrestrial plant matter, which are well known to be important. Because inputs differ in quality and the trophic level they subsidise, proportional changes in input type could have strong effects on recipient food webs. 5. Cross-ecosystem resource inputs have previously been characterised as donor-controlled. However, control by the recipient food web could lead to greater feedback between resource flow and consumer dynamics than has been appreciated so far.
Potential Occupant Injury Reduction in Pre-Crash System Equipped Vehicles in the Striking Vehicle of Rear-end Crashes.

PubMed

Kusano, Kristofer D; Gabler, Hampton C

2010-01-01

To mitigate the severity of rear-end and other collisions, Pre-Crash Systems (PCS) are being developed. These active safety systems utilize radar and/or video cameras to determine when a frontal crash, such as a front-to-back rear-end collisions, is imminent and can brake autonomously, even with no driver input. Of these PCS features, the effects of autonomous pre-crash braking are estimated. To estimate the maximum potential for injury reduction due to autonomous pre-crash braking in the striking vehicle of rear-end crashes, a methodology is presented for determining 1) the reduction in vehicle crash change in velocity (ΔV) due to PCS braking and 2) the number of injuries that could be prevented due to the reduction in collision severity. Injury reduction was only performed for belted drivers, as unbelted drivers have an unknown risk of being thrown out of position. The study was based on 1,406 rear-end striking vehicles from NASS / CDS years 1993 to 2008. PCS parameters were selected from realistic values and varied to examine the effect on system performance. PCS braking authority was varied from 0.5 G's to 0.8 G's while time to collision (TTC) was held at 0.45 seconds. TTC was then varied from 0.3 second to 0.6 seconds while braking authority was held constant at 0.6 G's. A constant braking pulse (step function) and ramp-up braking pulse were used. The study found that automated PCS braking could reduce the crash ΔV in rear-end striking vehicles by an average of 12% - 50% and avoid 0% - 14% of collisions, depending on PCS parameters. Autonomous PCS braking could potentially reduce the number of injured drivers who are belted by 19% to 57%.
Program for User-Friendly Management of Input and Output Data Sets

NASA Technical Reports Server (NTRS)

Klimeck, Gerhard

2003-01-01

A computer program manages large, hierarchical sets of input and output (I/O) parameters (typically, sequences of alphanumeric data) involved in computational simulations in a variety of technological disciplines. This program represents sets of parameters as structures coded in object-oriented but otherwise standard American National Standards Institute C language. Each structure contains a group of I/O parameters that make sense as a unit in the simulation program with which this program is used. The addition of options and/or elements to sets of parameters amounts to the addition of new elements to data structures. By association of child data generated in response to a particular user input, a hierarchical ordering of input parameters can be achieved. Associated with child data structures are the creation and description mechanisms within the parent data structures. Child data structures can spawn further child data structures. In this program, the creation and representation of a sequence of data structures is effected by one line of code that looks for children of a sequence of structures until there are no more children to be found. A linked list of structures is created dynamically and is completely represented in the data structures themselves. Such hierarchical data presentation can guide users through otherwise complex setup procedures and it can be integrated within a variety of graphical representations.
Computing the structural influence matrix for biological systems.

PubMed

Giordano, Giulia; Cuba Samaniego, Christian; Franco, Elisa; Blanchini, Franco

2016-06-01

We consider the problem of identifying structural influences of external inputs on steady-state outputs in a biological network model. We speak of a structural influence if, upon a perturbation due to a constant input, the ensuing variation of the steady-state output value has the same sign as the input (positive influence), the opposite sign (negative influence), or is zero (perfect adaptation), for any feasible choice of the model parameters. All these signs and zeros can constitute a structural influence matrix, whose (i, j) entry indicates the sign of steady-state influence of the jth system variable on the ith variable (the output caused by an external persistent input applied to the jth variable). Each entry is structurally determinate if the sign does not depend on the choice of the parameters, but is indeterminate otherwise. In principle, determining the influence matrix requires exhaustive testing of the system steady-state behaviour in the widest range of parameter values. Here we show that, in a broad class of biological networks, the influence matrix can be evaluated with an algorithm that tests the system steady-state behaviour only at a finite number of points. This algorithm also allows us to assess the structural effect of any perturbation, such as variations of relevant parameters. Our method is applied to nontrivial models of biochemical reaction networks and population dynamics drawn from the literature, providing a parameter-free insight into the system dynamics.
Simulating the epidemiological and economic effects of an African swine fever epidemic in industrialized swine populations.

PubMed

Halasa, Tariq; Bøtner, Anette; Mortensen, Sten; Christensen, Hanne; Toft, Nils; Boklund, Anette

2016-09-25

African swine fever (ASF) is a notifiable infectious disease with a considerable impact on animal health and is currently one of the most important emerging diseases of domestic pigs. ASF was introduced into Georgia in 2007 and subsequently spread to the Russian Federation and several Eastern European countries. Consequently, there is a non-negligible risk of ASF spread towards Western Europe. Therefore it is important to develop tools to improve our understanding of the spread and control of ASF for contingency planning. A stochastic and dynamic spatial spread model (DTU-DADS) was adjusted to simulate the spread of ASF virus between domestic swine herds exemplified by the Danish swine population. ASF was simulated to spread via animal movement, low- or medium-risk contacts and local spread. Each epidemic was initiated in a randomly selected herd - either in a nucleus herd, a sow herd, a randomly selected herd or in multiple herds simultaneously. A sensitivity analysis was conducted on input parameters. Given the inputs and assumptions of the model, epidemics of ASF in Denmark are predicted to be small, affecting about 14 herds in the worst-case scenario. The duration of an epidemic is predicted to vary from 1 to 76days. Substantial economic damages are predicted, with median direct costs and export losses of €12 and €349 million, respectively, when epidemics were initiated in multiple herds. Each infectious herd resulted in 0 to 2 new infected herds varying from 0 to 5 new infected herds, depending on the index herd type. Copyright © 2016 Elsevier B.V. All rights reserved.
Mass Fluxes of Ice and Oxygen Across the Entire Lid of Lake Vostok from Observations of Englacial Radiowave Attenuation

NASA Astrophysics Data System (ADS)

Winebrenner, D. P.; Kintner, P. M. S.; MacGregor, J. A.

2017-12-01

Over deep Antarctic subglacial lakes, spatially varying ice thickness and the pressure-dependent melting point of ice result in areas of melting and accretion at the ice-water interface, i.e., the lake lid. These ice mass fluxes drive lake circulation and, because basal Antarctic ice contains air-clathrate, affect the input of oxygen to the lake, with implications for subglacial life. Inferences of melting and accretion from radar-layer tracking and geodesy are limited in spatial coverage and resolution. Here we develop a new method to estimate rates of accretion, melting, and the resulting oxygen input at a lake lid, using airborne radar data over Lake Vostok together with ice-temperature and chemistry data from the Vostok ice core. Because the lake lid is a coherent reflector of known reflectivity (at our radar frequency), we can infer depth-averaged radiowave attenuation in the ice, with spatial resolution 1 km along flight lines. Spatial variation in attenuation depends mostly on variation in ice temperature near the lid, which in turn varies strongly with ice mass flux at the lid. We model ice temperature versus depth with ice mass flux as a parameter, thus linking that flux to (observed) depth-averaged attenuation. The resulting map of melt- and accretion-rates independently reproduces features known from earlier studies, but now covers the entire lid. We find that accretion is dominant when integrated over the lid, with an ice imbalance of 0.05 to 0.07 km3 a-1, which is robust against uncertainties.
Estimation of spatially varying heat transfer coefficient from a flat plate with flush mounted heat sources using Bayesian inference

NASA Astrophysics Data System (ADS)

Jakkareddy, Pradeep S.; Balaji, C.

2016-09-01

This paper employs the Bayesian based Metropolis Hasting - Markov Chain Monte Carlo algorithm to solve inverse heat transfer problem of determining the spatially varying heat transfer coefficient from a flat plate with flush mounted discrete heat sources with measured temperatures at the bottom of the plate. The Nusselt number is assumed to be of the form Nu = aReb(x/l)c . To input reasonable values of ’a’ and ‘b’ into the inverse problem, first limited two dimensional conjugate convection simulations were done with Comsol. Based on the guidance from this different values of ‘a’ and ‘b’ are input to a computationally less complex problem of conjugate conduction in the flat plate (15mm thickness) and temperature distributions at the bottom of the plate which is a more convenient location for measuring the temperatures without disturbing the flow were obtained. Since the goal of this work is to demonstrate the eficiacy of the Bayesian approach to accurately retrieve ‘a’ and ‘b’, numerically generated temperatures with known values of ‘a’ and ‘b’ are treated as ‘surrogate’ experimental data. The inverse problem is then solved by repeatedly using the forward solutions together with the MH-MCMC aprroach. To speed up the estimation, the forward model is replaced by an artificial neural network. The mean, maximum-a-posteriori and standard deviation of the estimated parameters ‘a’ and ‘b’ are reported. The robustness of the proposed method is examined, by synthetically adding noise to the temperatures.
Theoretical effect of modifications to the upper surface of two NACA airfoils using smooth polynomial additional thickness distributions which emphasize leading edge profile and which vary quadratically at the trailing edge. [using flow equations and a CDC 7600 computer

NASA Technical Reports Server (NTRS)

Merz, A. W.; Hague, D. S.

1975-01-01

An investigation was conducted on a CDC 7600 digital computer to determine the effects of additional thickness distributions to the upper surface of the NACA 64-206 and 64 sub 1 - 212 airfoils. The additional thickness distribution had the form of a continuous mathematical function which disappears at both the leading edge and the trailing edge. The function behaves as a polynomial of order epsilon sub 1 at the leading edge, and a polynomial of order epsilon sub 2 at the trailing edge. Epsilon sub 2 is a constant and epsilon sub 1 is varied over a range of practical interest. The magnitude of the additional thickness, y, is a second input parameter, and the effect of varying epsilon sub 1 and y on the aerodynamic performance of the airfoil was investigated. Results were obtained at a Mach number of 0.2 with an angle-of-attack of 6 degrees on the basic airfoils, and all calculations employ the full potential flow equations for two dimensional flow. The relaxation method of Jameson was employed for solution of the potential flow equations.
Modeling uncertainty and correlation in soil properties using Restricted Pairing and implications for ensemble-based hillslope-scale soil moisture and temperature estimation

NASA Astrophysics Data System (ADS)

Flores, A. N.; Entekhabi, D.; Bras, R. L.

2007-12-01

Soil hydraulic and thermal properties (SHTPs) affect both the rate of moisture redistribution in the soil column and the volumetric soil water capacity. Adequately constraining these properties through field and lab analysis to parameterize spatially-distributed hydrology models is often prohibitively expensive. Because SHTPs vary significantly at small spatial scales individual soil samples are also only reliably indicative of local conditions, and these properties remain a significant source of uncertainty in soil moisture and temperature estimation. In ensemble-based soil moisture data assimilation, uncertainty in the model-produced prior estimate due to associated uncertainty in SHTPs must be taken into account to avoid under-dispersive ensembles. To treat SHTP uncertainty for purposes of supplying inputs to a distributed watershed model we use the restricted pairing (RP) algorithm, an extension of Latin Hypercube (LH) sampling. The RP algorithm generates an arbitrary number of SHTP combinations by sampling the appropriate marginal distributions of the individual soil properties using the LH approach, while imposing a target rank correlation among the properties. A previously-published meta- database of 1309 soils representing 12 textural classes is used to fit appropriate marginal distributions to the properties and compute the target rank correlation structure, conditioned on soil texture. Given categorical soil textures, our implementation of the RP algorithm generates an arbitrarily-sized ensemble of realizations of the SHTPs required as input to the TIN-based Realtime Integrated Basin Simulator with vegetation dynamics (tRIBS+VEGGIE) distributed parameter ecohydrology model. Soil moisture ensembles simulated with RP- generated SHTPs exhibit less variance than ensembles simulated with SHTPs generated by a scheme that neglects correlation among properties. Neglecting correlation among SHTPs can lead to physically unrealistic combinations of parameters that exhibit implausible hydrologic behavior when input to the tRIBS+VEGGIE model.
Global dynamics of a stochastic neuronal oscillator

NASA Astrophysics Data System (ADS)

Yamanobe, Takanobu

2013-11-01

Nonlinear oscillators have been used to model neurons that fire periodically in the absence of input. These oscillators, which are called neuronal oscillators, share some common response structures with other biological oscillations such as cardiac cells. In this study, we analyze the dependence of the global dynamics of an impulse-driven stochastic neuronal oscillator on the relaxation rate to the limit cycle, the strength of the intrinsic noise, and the impulsive input parameters. To do this, we use a Markov operator that both reflects the density evolution of the oscillator and is an extension of the phase transition curve, which describes the phase shift due to a single isolated impulse. Previously, we derived the Markov operator for the finite relaxation rate that describes the dynamics of the entire phase plane. Here, we construct a Markov operator for the infinite relaxation rate that describes the stochastic dynamics restricted to the limit cycle. In both cases, the response of the stochastic neuronal oscillator to time-varying impulses is described by a product of Markov operators. Furthermore, we calculate the number of spikes between two consecutive impulses to relate the dynamics of the oscillator to the number of spikes per unit time and the interspike interval density. Specifically, we analyze the dynamics of the number of spikes per unit time based on the properties of the Markov operators. Each Markov operator can be decomposed into stationary and transient components based on the properties of the eigenvalues and eigenfunctions. This allows us to evaluate the difference in the number of spikes per unit time between the stationary and transient responses of the oscillator, which we show to be based on the dependence of the oscillator on past activity. Our analysis shows how the duration of the past neuronal activity depends on the relaxation rate, the noise strength, and the impulsive input parameters.
Global dynamics of a stochastic neuronal oscillator.

PubMed

Yamanobe, Takanobu

2013-11-01

Nonlinear oscillators have been used to model neurons that fire periodically in the absence of input. These oscillators, which are called neuronal oscillators, share some common response structures with other biological oscillations such as cardiac cells. In this study, we analyze the dependence of the global dynamics of an impulse-driven stochastic neuronal oscillator on the relaxation rate to the limit cycle, the strength of the intrinsic noise, and the impulsive input parameters. To do this, we use a Markov operator that both reflects the density evolution of the oscillator and is an extension of the phase transition curve, which describes the phase shift due to a single isolated impulse. Previously, we derived the Markov operator for the finite relaxation rate that describes the dynamics of the entire phase plane. Here, we construct a Markov operator for the infinite relaxation rate that describes the stochastic dynamics restricted to the limit cycle. In both cases, the response of the stochastic neuronal oscillator to time-varying impulses is described by a product of Markov operators. Furthermore, we calculate the number of spikes between two consecutive impulses to relate the dynamics of the oscillator to the number of spikes per unit time and the interspike interval density. Specifically, we analyze the dynamics of the number of spikes per unit time based on the properties of the Markov operators. Each Markov operator can be decomposed into stationary and transient components based on the properties of the eigenvalues and eigenfunctions. This allows us to evaluate the difference in the number of spikes per unit time between the stationary and transient responses of the oscillator, which we show to be based on the dependence of the oscillator on past activity. Our analysis shows how the duration of the past neuronal activity depends on the relaxation rate, the noise strength, and the impulsive input parameters.
Multi-response optimization of process parameters for GTAW process in dissimilar welding of Incoloy 800HT and P91 steel by using grey relational analysis

NASA Astrophysics Data System (ADS)

vellaichamy, Lakshmanan; Paulraj, Sathiya

2018-02-01

The dissimilar welding of Incoloy 800HT and P91 steel using Gas Tungsten arc welding process (GTAW) This material is being used in the Nuclear Power Plant and Aerospace Industry based application because Incoloy 800HT possess good corrosion and oxidation resistance and P91 possess high temperature strength and creep resistance. This work discusses on multi-objective optimization using gray relational analysis (GRA) using 9CrMoV-N filler materials. The experiment conducted L9 orthogonal array. The input parameter are current, voltage, speed. The output response are Tensile strength, Hardness and Toughness. To optimize the input parameter and multiple output variable by using GRA. The optimal parameter is combination was determined as A2B1C1 so given input parameter welding current at 120 A, voltage at 16 V and welding speed at 0.94 mm/s. The output of the mechanical properties for best and least grey relational grade was validated by the metallurgical characteristics.
Calibration of discrete element model parameters: soybeans

NASA Astrophysics Data System (ADS)

Ghodki, Bhupendra M.; Patel, Manish; Namdeo, Rohit; Carpenter, Gopal

2018-05-01

Discrete element method (DEM) simulations are broadly used to get an insight of flow characteristics of granular materials in complex particulate systems. DEM input parameters for a model are the critical prerequisite for an efficient simulation. Thus, the present investigation aims to determine DEM input parameters for Hertz-Mindlin model using soybeans as a granular material. To achieve this aim, widely acceptable calibration approach was used having standard box-type apparatus. Further, qualitative and quantitative findings such as particle profile, height of kernels retaining the acrylic wall, and angle of repose of experiments and numerical simulations were compared to get the parameters. The calibrated set of DEM input parameters includes the following (a) material properties: particle geometric mean diameter (6.24 mm); spherical shape; particle density (1220 kg m^{-3} ), and (b) interaction parameters such as particle-particle: coefficient of restitution (0.17); coefficient of static friction (0.26); coefficient of rolling friction (0.08), and particle-wall: coefficient of restitution (0.35); coefficient of static friction (0.30); coefficient of rolling friction (0.08). The results may adequately be used to simulate particle scale mechanics (grain commingling, flow/motion, forces, etc) of soybeans in post-harvest machinery and devices.
Intelligent Control for Drag Reduction on the X-48B Vehicle

NASA Technical Reports Server (NTRS)

Griffin, Brian Joseph; Brown, Nelson Andrew; Yoo, Seung Yeun

2011-01-01

This paper focuses on the development of an intelligent control technology for in-flight drag reduction. The system is integrated with and demonstrated on the full X-48B nonlinear simulation. The intelligent control system utilizes a peak-seeking control method implemented with a time-varying Kalman filter. Performance functional coordinate and magnitude measurements, or independent and dependent parameters respectively, are used by the Kalman filter to provide the system with gradient estimates of the designed performance function which is used to drive the system toward a local minimum in a steepestdescent approach. To ensure ease of integration and algorithm performance, a single-input single-output approach was chosen. The framework, specific implementation considerations, simulation results, and flight feasibility issues related to this platform are discussed.
Local facet approximation for image stitching

NASA Astrophysics Data System (ADS)

Li, Jing; Lai, Shiming; Liu, Yu; Wang, Zhengming; Zhang, Maojun

2018-01-01

Image stitching aims at eliminating multiview parallax and generating a seamless panorama given a set of input images. This paper proposes a local adaptive stitching method, which could achieve both accurate and robust image alignments across the whole panorama. A transformation estimation model is introduced by approximating the scene as a combination of neighboring facets. Then, the local adaptive stitching field is constructed using a series of linear systems of the facet parameters, which enables the parallax handling in three-dimensional space. We also provide a concise but effective global projectivity preserving technique that smoothly varies the transformations from local adaptive to global planar. The proposed model is capable of stitching both normal images and fisheye images. The efficiency of our method is quantitatively demonstrated in the comparative experiments on several challenging cases.
Robot trajectory tracking with self-tuning predicted control

NASA Technical Reports Server (NTRS)

Cui, Xianzhong; Shin, Kang G.

1988-01-01

A controller that combines self-tuning prediction and control is proposed for robot trajectory tracking. The controller has two feedback loops: one is used to minimize the prediction error, and the other is designed to make the system output track the set point input. Because the velocity and position along the desired trajectory are given and the future output of the system is predictable, a feedforward loop can be designed for robot trajectory tracking with self-tuning predicted control (STPC). Parameters are estimated online to account for the model uncertainty and the time-varying property of the system. The authors describe the principle of STPC, analyze the system performance, and discuss the simplification of the robot dynamic equations. To demonstrate its utility and power, the controller is simulated for a Stanford arm.
Eye and Head Movement Characteristics in Free Visual Search of Flight-Simulator Imagery

DTIC Science & Technology

2010-03-01

conspicuity. However, only gaze amplitude varied significantly with IFOV. A two- parameter (scale and exponent) power function was fitted to the...main-sequence amplitude-duration data. Both parameters varied significantly with target conspicuity, but in opposite directions. Neither parameter ...IFOV. A two- parameter (scale and exponent) power function was fitted to the main-sequence amplitude-duration data. Both parameters varied

AIRCRAFT REACTOR CONTROL SYSTEM APPLICABLE TO TURBOJET AND TURBOPROP POWER PLANTS

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

Gorker, G.E.

1955-07-19

Control systems proposed for direct cycle nuclear powered aircraft commonly involve control of engine speed, nuclear energy input, and chcmical energy input. A system in which these parameters are controlled by controlling the total energy input, the ratio of nuclear and chemical energy input, and the engine speed is proposed. The system is equally applicable to turbojet or turboprop applications. (auth)
Evaluation of Advanced Stirling Convertor Net Heat Input Correlation Methods Using a Thermal Standard

NASA Technical Reports Server (NTRS)

Briggs, Maxwell; Schifer, Nicholas

2011-01-01

Test hardware used to validate net heat prediction models. Problem: Net Heat Input cannot be measured directly during operation. Net heat input is a key parameter needed in prediction of efficiency for convertor performance. Efficiency = Electrical Power Output (Measured) divided by Net Heat Input (Calculated). Efficiency is used to compare convertor designs and trade technology advantages for mission planning.
Experiments for practical education in process parameter optimization for selective laser sintering to increase workpiece quality

NASA Astrophysics Data System (ADS)

Reutterer, Bernd; Traxler, Lukas; Bayer, Natascha; Drauschke, Andreas

2016-04-01

Selective Laser Sintering (SLS) is considered as one of the most important additive manufacturing processes due to component stability and its broad range of usable materials. However the influence of the different process parameters on mechanical workpiece properties is still poorly studied, leading to the fact that further optimization is necessary to increase workpiece quality. In order to investigate the impact of various process parameters, laboratory experiments are implemented to improve the understanding of the SLS limitations and advantages on an educational level. Experiments are based on two different workstations, used to teach students the fundamentals of SLS. First of all a 50 W CO2 laser workstation is used to investigate the interaction of the laser beam with the used material in accordance with varied process parameters to analyze a single-layered test piece. Second of all the FORMIGA P110 laser sintering system from EOS is used to print different 3D test pieces in dependence on various process parameters. Finally quality attributes are tested including warpage, dimension accuracy or tensile strength. For dimension measurements and evaluation of the surface structure a telecentric lens in combination with a camera is used. A tensile test machine allows testing of the tensile strength and the interpreting of stress-strain curves. The developed laboratory experiments are suitable to teach students the influence of processing parameters. In this context they will be able to optimize the input parameters depending on the component which has to be manufactured and to increase the overall quality of the final workpiece.
Effect of Heat Input on Geometry of Austenitic Stainless Steel Weld Bead on Low Carbon Steel

NASA Astrophysics Data System (ADS)

Saha, Manas Kumar; Hazra, Ritesh; Mondal, Ajit; Das, Santanu

2018-05-01

Among different weld cladding processes, gas metal arc welding (GMAW) cladding becomes a cost effective, user friendly, versatile method for protecting the surface of relatively lower grade structural steels from corrosion and/or erosion wear by depositing high grade stainless steels onto them. The quality of cladding largely depends upon the bead geometry of the weldment deposited. Weld bead geometry parameters, like bead width, reinforcement height, depth of penetration, and ratios like reinforcement form factor (RFF) and penetration shape factor (PSF) determine the quality of the weld bead geometry. Various process parameters of gas metal arc welding like heat input, current, voltage, arc travel speed, mode of metal transfer, etc. influence formation of bead geometry. In the current experimental investigation, austenite stainless steel (316) weld beads are formed on low alloy structural steel (E350) by GMAW using 100% CO2 as the shielding gas. Different combinations of current, voltage and arc travel speed are chosen so that heat input increases from 0.35 to 0.75 kJ/mm. Nine number of weld beads are deposited and replicated twice. The observations show that weld bead width increases linearly with increase in heat input, whereas reinforcement height and depth of penetration do not increase with increase in heat input. Regression analysis is done to establish the relationship between heat input and different geometrical parameters of weld bead. The regression models developed agrees well with the experimental data. Within the domain of the present experiment, it is observed that at higher heat input, the weld bead gets wider having little change in penetration and reinforcement; therefore, higher heat input may be recommended for austenitic stainless steel cladding on low alloy steel.
Prediction of AL and Dst Indices from ACE Measurements Using Hybrid Physics/Black-Box Techniques

NASA Astrophysics Data System (ADS)

Spencer, E.; Rao, A.; Horton, W.; Mays, L.

2008-12-01

ACE measurements of the solar wind velocity, IMF and proton density is used to drive a hybrid Physics/Black- Box model of the nightside magnetosphere. The core physics is contained in a low order nonlinear dynamical model of the nightside magnetosphere called WINDMI. The model is augmented by wavelet based nonlinear mappings between the solar wind quantities and the input into the physics model, followed by further wavelet based mappings of the model output field aligned currents onto the ground based magnetometer measurements of the AL index and Dst index. The black box mappings are introduced at the input stage to account for uncertainties in the way the solar wind quantities are transported from the ACE spacecraft at L1 to the magnetopause. Similar mappings are introduced at the output stage to account for a spatially and temporally varying westward auroral electrojet geometry. The parameters of the model are tuned using a genetic algorithm, and trained using the large geomagnetic storm dataset of October 3-7 2000. It's predictive performance is then evaluated on subsequent storm datasets, in particular the April 15-24 2002 storm. This work is supported by grant NSF 7020201
Neural feedback for instantaneous spatiotemporal modulation of afferent pathways in bi-directional brain-machine interfaces.

PubMed

Liu, Jianbo; Khalil, Hassan K; Oweiss, Karim G

2011-10-01

In bi-directional brain-machine interfaces (BMIs), precisely controlling the delivery of microstimulation, both in space and in time, is critical to continuously modulate the neural activity patterns that carry information about the state of the brain-actuated device to sensory areas in the brain. In this paper, we investigate the use of neural feedback to control the spatiotemporal firing patterns of neural ensembles in a model of the thalamocortical pathway. Control of pyramidal (PY) cells in the primary somatosensory cortex (S1) is achieved based on microstimulation of thalamic relay cells through multiple-input multiple-output (MIMO) feedback controllers. This closed loop feedback control mechanism is achieved by simultaneously varying the stimulation parameters across multiple stimulation electrodes in the thalamic circuit based on continuous monitoring of the difference between reference patterns and the evoked responses of the cortical PY cells. We demonstrate that it is feasible to achieve a desired level of performance by controlling the firing activity pattern of a few "key" neural elements in the network. Our results suggest that neural feedback could be an effective method to facilitate the delivery of information to the cortex to substitute lost sensory inputs in cortically controlled BMIs.
A matlab framework for estimation of NLME models using stochastic differential equations: applications for estimation of insulin secretion rates.

PubMed

Mortensen, Stig B; Klim, Søren; Dammann, Bernd; Kristensen, Niels R; Madsen, Henrik; Overgaard, Rune V

2007-10-01

The non-linear mixed-effects model based on stochastic differential equations (SDEs) provides an attractive residual error model, that is able to handle serially correlated residuals typically arising from structural mis-specification of the true underlying model. The use of SDEs also opens up for new tools for model development and easily allows for tracking of unknown inputs and parameters over time. An algorithm for maximum likelihood estimation of the model has earlier been proposed, and the present paper presents the first general implementation of this algorithm. The implementation is done in Matlab and also demonstrates the use of parallel computing for improved estimation times. The use of the implementation is illustrated by two examples of application which focus on the ability of the model to estimate unknown inputs facilitated by the extension to SDEs. The first application is a deconvolution-type estimation of the insulin secretion rate based on a linear two-compartment model for C-peptide measurements. In the second application the model is extended to also give an estimate of the time varying liver extraction based on both C-peptide and insulin measurements.
Application of wavelet-based multi-model Kalman filters to real-time flood forecasting

NASA Astrophysics Data System (ADS)

Chou, Chien-Ming; Wang, Ru-Yih

2004-04-01

This paper presents the application of a multimodel method using a wavelet-based Kalman filter (WKF) bank to simultaneously estimate decomposed state variables and unknown parameters for real-time flood forecasting. Applying the Haar wavelet transform alters the state vector and input vector of the state space. In this way, an overall detail plus approximation describes each new state vector and input vector, which allows the WKF to simultaneously estimate and decompose state variables. The wavelet-based multimodel Kalman filter (WMKF) is a multimodel Kalman filter (MKF), in which the Kalman filter has been substituted for a WKF. The WMKF then obtains M estimated state vectors. Next, the M state-estimates, each of which is weighted by its possibility that is also determined on-line, are combined to form an optimal estimate. Validations conducted for the Wu-Tu watershed, a small watershed in Taiwan, have demonstrated that the method is effective because of the decomposition of wavelet transform, the adaptation of the time-varying Kalman filter and the characteristics of the multimodel method. Validation results also reveal that the resulting method enhances the accuracy of the runoff prediction of the rainfall-runoff process in the Wu-Tu watershed.
Effects of uncertainties in hydrological modelling. A case study of a mountainous catchment in Southern Norway

NASA Astrophysics Data System (ADS)

Engeland, Kolbjørn; Steinsland, Ingelin; Johansen, Stian Solvang; Petersen-Øverleir, Asgeir; Kolberg, Sjur

2016-05-01

In this study, we explore the effect of uncertainty and poor observation quality on hydrological model calibration and predictions. The Osali catchment in Western Norway was selected as case study and an elevation distributed HBV-model was used. We systematically evaluated the effect of accounting for uncertainty in parameters, precipitation input, temperature input and streamflow observations. For precipitation and temperature we accounted for the interpolation uncertainty, and for streamflow we accounted for rating curve uncertainty. Further, the effects of poorer quality of precipitation input and streamflow observations were explored. Less information about precipitation was obtained by excluding the nearest precipitation station from the analysis, while reduced information about the streamflow was obtained by omitting the highest and lowest streamflow observations when estimating the rating curve. The results showed that including uncertainty in the precipitation and temperature inputs has a negligible effect on the posterior distribution of parameters and for the Nash-Sutcliffe (NS) efficiency for the predicted flows, while the reliability and the continuous rank probability score (CRPS) improves. Less information in precipitation input resulted in a shift in the water balance parameter Pcorr, a model producing smoother streamflow predictions, giving poorer NS and CRPS, but higher reliability. The effect of calibrating the hydrological model using streamflow observations based on different rating curves is mainly seen as variability in the water balance parameter Pcorr. When evaluating predictions, the best evaluation scores were not achieved for the rating curve used for calibration, but for rating curves giving smoother streamflow observations. Less information in streamflow influenced the water balance parameter Pcorr, and increased the spread in evaluation scores by giving both better and worse scores.
Ignoring correlation in uncertainty and sensitivity analysis in life cycle assessment: what is the risk?

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

Groen, E.A., E-mail: Evelyne.Groen@gmail.com; Heijungs, R.; Leiden University, Einsteinweg 2, Leiden 2333 CC

Life cycle assessment (LCA) is an established tool to quantify the environmental impact of a product. A good assessment of uncertainty is important for making well-informed decisions in comparative LCA, as well as for correctly prioritising data collection efforts. Under- or overestimation of output uncertainty (e.g. output variance) will lead to incorrect decisions in such matters. The presence of correlations between input parameters during uncertainty propagation, can increase or decrease the the output variance. However, most LCA studies that include uncertainty analysis, ignore correlations between input parameters during uncertainty propagation, which may lead to incorrect conclusions. Two approaches to include correlationsmore » between input parameters during uncertainty propagation and global sensitivity analysis were studied: an analytical approach and a sampling approach. The use of both approaches is illustrated for an artificial case study of electricity production. Results demonstrate that both approaches yield approximately the same output variance and sensitivity indices for this specific case study. Furthermore, we demonstrate that the analytical approach can be used to quantify the risk of ignoring correlations between input parameters during uncertainty propagation in LCA. We demonstrate that: (1) we can predict if including correlations among input parameters in uncertainty propagation will increase or decrease output variance; (2) we can quantify the risk of ignoring correlations on the output variance and the global sensitivity indices. Moreover, this procedure requires only little data. - Highlights: • Ignoring correlation leads to under- or overestimation of the output variance. • We demonstrated that the risk of ignoring correlation can be quantified. • The procedure proposed is generally applicable in life cycle assessment. • In some cases, ignoring correlation has a minimal effect on decision-making tools.« less
Identification of modal parameters including unmeasured forces and transient effects

NASA Astrophysics Data System (ADS)

Cauberghe, B.; Guillaume, P.; Verboven, P.; Parloo, E.

2003-08-01

In this paper, a frequency-domain method to estimate modal parameters from short data records with known input (measured) forces and unknown input forces is presented. The method can be used for an experimental modal analysis, an operational modal analysis (output-only data) and the combination of both. A traditional experimental and operational modal analysis in the frequency domain starts respectively, from frequency response functions and spectral density functions. To estimate these functions accurately sufficient data have to be available. The technique developed in this paper estimates the modal parameters directly from the Fourier spectra of the outputs and the known input. Instead of using Hanning windows on these short data records the transient effects are estimated simultaneously with the modal parameters. The method is illustrated, tested and validated by Monte Carlo simulations and experiments. The presented method to process short data sequences leads to unbiased estimates with a small variance in comparison to the more traditional approaches.
Modern control concepts in hydrology

NASA Technical Reports Server (NTRS)

Duong, N.; Johnson, G. R.; Winn, C. B.

1974-01-01

Two approaches to an identification problem in hydrology are presented based upon concepts from modern control and estimation theory. The first approach treats the identification of unknown parameters in a hydrologic system subject to noisy inputs as an adaptive linear stochastic control problem; the second approach alters the model equation to account for the random part in the inputs, and then uses a nonlinear estimation scheme to estimate the unknown parameters. Both approaches use state-space concepts. The identification schemes are sequential and adaptive and can handle either time invariant or time dependent parameters. They are used to identify parameters in the Prasad model of rainfall-runoff. The results obtained are encouraging and conform with results from two previous studies; the first using numerical integration of the model equation along with a trial-and-error procedure, and the second, by using a quasi-linearization technique. The proposed approaches offer a systematic way of analyzing the rainfall-runoff process when the input data are imbedded in noise.
49 CFR 178.337-4 - Joints.

Code of Federal Regulations, 2014 CFR

2014-10-01

... must be considered as essential variables: Number of passes; thickness of plate; heat input per pass... not be used. The number of passes, thickness of plate, and heat input per pass may not vary more than... machine heat processes, provided such surfaces are remelted in the subsequent welding process. Where there...
49 CFR 178.337-4 - Joints.

Code of Federal Regulations, 2013 CFR

2013-10-01

... must be considered as essential variables: Number of passes; thickness of plate; heat input per pass... not be used. The number of passes, thickness of plate, and heat input per pass may not vary more than... machine heat processes, provided such surfaces are remelted in the subsequent welding process. Where there...
49 CFR 178.337-4 - Joints.

Code of Federal Regulations, 2012 CFR

2012-10-01

... must be considered as essential variables: Number of passes; thickness of plate; heat input per pass... not be used. The number of passes, thickness of plate, and heat input per pass may not vary more than... machine heat processes, provided such surfaces are remelted in the subsequent welding process. Where there...
49 CFR 178.337-4 - Joints.

Code of Federal Regulations, 2011 CFR

2011-10-01

... must be considered as essential variables: Number of passes; thickness of plate; heat input per pass... not be used. The number of passes, thickness of plate, and heat input per pass may not vary more than... machine heat processes, provided such surfaces are remelted in the subsequent welding process. Where there...
Verification Techniques for Parameter Selection and Bayesian Model Calibration Presented for an HIV Model

NASA Astrophysics Data System (ADS)

Wentworth, Mami Tonoe

Uncertainty quantification plays an important role when making predictive estimates of model responses. In this context, uncertainty quantification is defined as quantifying and reducing uncertainties, and the objective is to quantify uncertainties in parameter, model and measurements, and propagate the uncertainties through the model, so that one can make a predictive estimate with quantified uncertainties. Two of the aspects of uncertainty quantification that must be performed prior to propagating uncertainties are model calibration and parameter selection. There are several efficient techniques for these processes; however, the accuracy of these methods are often not verified. This is the motivation for our work, and in this dissertation, we present and illustrate verification frameworks for model calibration and parameter selection in the context of biological and physical models. First, HIV models, developed and improved by [2, 3, 8], describe the viral infection dynamics of an HIV disease. These are also used to make predictive estimates of viral loads and T-cell counts and to construct an optimal control for drug therapy. Estimating input parameters is an essential step prior to uncertainty quantification. However, not all the parameters are identifiable, implying that they cannot be uniquely determined by the observations. These unidentifiable parameters can be partially removed by performing parameter selection, a process in which parameters that have minimal impacts on the model response are determined. We provide verification techniques for Bayesian model calibration and parameter selection for an HIV model. As an example of a physical model, we employ a heat model with experimental measurements presented in [10]. A steady-state heat model represents a prototypical behavior for heat conduction and diffusion process involved in a thermal-hydraulic model, which is a part of nuclear reactor models. We employ this simple heat model to illustrate verification techniques for model calibration. For Bayesian model calibration, we employ adaptive Metropolis algorithms to construct densities for input parameters in the heat model and the HIV model. To quantify the uncertainty in the parameters, we employ two MCMC algorithms: Delayed Rejection Adaptive Metropolis (DRAM) [33] and Differential Evolution Adaptive Metropolis (DREAM) [66, 68]. The densities obtained using these methods are compared to those obtained through the direct numerical evaluation of the Bayes' formula. We also combine uncertainties in input parameters and measurement errors to construct predictive estimates for a model response. A significant emphasis is on the development and illustration of techniques to verify the accuracy of sampling-based Metropolis algorithms. We verify the accuracy of DRAM and DREAM by comparing chains, densities and correlations obtained using DRAM, DREAM and the direct evaluation of Bayes formula. We also perform similar analysis for credible and prediction intervals for responses. Once the parameters are estimated, we employ energy statistics test [63, 64] to compare the densities obtained by different methods for the HIV model. The energy statistics are used to test the equality of distributions. We also consider parameter selection and verification techniques for models having one or more parameters that are noninfluential in the sense that they minimally impact model outputs. We illustrate these techniques for a dynamic HIV model but note that the parameter selection and verification framework is applicable to a wide range of biological and physical models. To accommodate the nonlinear input to output relations, which are typical for such models, we focus on global sensitivity analysis techniques, including those based on partial correlations, Sobol indices based on second-order model representations, and Morris indices, as well as a parameter selection technique based on standard errors. A significant objective is to provide verification strategies to assess the accuracy of those techniques, which we illustrate in the context of the HIV model. Finally, we examine active subspace methods as an alternative to parameter subset selection techniques. The objective of active subspace methods is to determine the subspace of inputs that most strongly affect the model response, and to reduce the dimension of the input space. The major difference between active subspace methods and parameter selection techniques is that parameter selection identifies influential parameters whereas subspace selection identifies a linear combination of parameters that impacts the model responses significantly. We employ active subspace methods discussed in [22] for the HIV model and present a verification that the active subspace successfully reduces the input dimensions.
Experimental Validation of Strategy for the Inverse Estimation of Mechanical Properties and Coefficient of Friction in Flat Rolling

NASA Astrophysics Data System (ADS)

Yadav, Vinod; Singh, Arbind Kumar; Dixit, Uday Shanker

2017-08-01

Flat rolling is one of the most widely used metal forming processes. For proper control and optimization of the process, modelling of the process is essential. Modelling of the process requires input data about material properties and friction. In batch production mode of rolling with newer materials, it may be difficult to determine the input parameters offline. In view of it, in the present work, a methodology to determine these parameters online by the measurement of exit temperature and slip is verified experimentally. It is observed that the inverse prediction of input parameters could be done with a reasonable accuracy. It was also assessed experimentally that there is a correlation between micro-hardness and flow stress of the material; however the correlation between surface roughness and reduction is not that obvious.
Robustness of controllability and observability of linear time-varying systems with application to the emergency control of power systems

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

Sastry, S. S.; Desoer, C. A.

1980-01-01

Fixed point methods from nonlinear anaysis are used to establish conditions under which the uniform complete controllability of linear time-varying systems is preserved under non-linear perturbations in the state dynamics and the zero-input uniform complete observability of linear time-varying systems is preserved under non-linear perturbation in the state dynamics and output read out map. Algorithms for computing the specific input to steer the perturbed systems from a given initial state to a given final state are also presented. As an application, a very specific emergency control of an interconnected power system is formulated as a steering problem and it ismore » shown that this emergency control is indeed possible in finite time.« less
Quantification of the effect of cross-shear and applied nominal contact pressure on the wear of moderately cross-linked polyethylene.

PubMed

Abdelgaied, Abdellatif; Brockett, Claire L; Liu, Feng; Jennings, Louise M; Fisher, John; Jin, Zhongmin

2013-01-01

Polyethylene wear is a great concern in total joint replacement. It is now considered a major limiting factor to the long life of such prostheses. Cross-linking has been introduced to reduce the wear of ultra-high-molecular-weight polyethylene (UHMWPE). Computational models have been used extensively for wear prediction and optimization of artificial knee designs. However, in order to be independent and have general applicability and predictability, computational wear models should be based on inputs from independent experimentally determined wear parameters (wear factors or wear coefficients). The objective of this study was to investigate moderately cross-linked UHMWPE, using a multidirectional pin-on-plate wear test machine, under a wide range of applied nominal contact pressure (from 1 to 11 MPa) and under five different kinematic inputs, varying from a purely linear track to a maximum rotation of +/- 55 degrees. A computational model, based on a direct simulation of the multidirectional pin-on-plate wear tester, was developed to quantify the degree of cross-shear (CS) of the polyethylene pins articulating against the metallic plates. The moderately cross-linked UHMWPE showed wear factors less than half of that reported in the literature for, the conventional UHMWPE, under the same loading and kinematic inputs. In addition, under high applied nominal contact stress, the moderately crosslinked UHMWPE wear showed lower dependence on the degree of CS compared to that under low applied nominal contact stress. The calculated wear coefficients were found to be independent of the applied nominal contact stress, in contrast to the wear factors that were shown to be highly pressure dependent. This study provided independent wear data for inputs into computational models for moderately cross-linked polyethylene and supported the application of wear coefficient-based computational wear models.

Solar Radiation Patterns and Glaciers in the Western Himalaya

NASA Astrophysics Data System (ADS)

Dobreva, I. D.; Bishop, M. P.

2013-12-01

Glacier dynamics in the Himalaya are poorly understood, in part due to variations in topography and climate. It is well known that solar radiation is the dominant surface-energy component governing ablation, although the spatio-temporal patterns of surface irradiance have not been thoroughly investigated given modeling limitations and topographic variations including altitude, relief, and topographic shielding. Glaciation and topographic conditions may greatly influence supraglacial characteristics and glacial dynamics. Consequently, our research objectives were to develop a GIS-based solar radiation model that accounts for Earth's orbital, spectral, atmospheric and topographic dependencies, in order to examine the spatio-temporal surface irradiance patterns on glaciers in the western Himalaya. We specifically compared irradiance patterns to supraglacial characteristics and ice-flow velocity fields. Shuttle Radar Mapping Mission (SRTM) 90 m data were used to compute geomorphometric parameters that were input into the solar radiation model. Simulations results for 2013 were produced for the summer ablation season. Direct irradiance, diffuse-skylight, and total irradiance variations were compared and related to glacier altitude profiles of ice velocity and land-surface topographic parameters. Velocity and surface information were derived from analyses of ASTER satellite data. Results indicate that the direct irradiance significantly varies across the surface of glaciers given local topography and meso-scale relief conditions. Furthermore, the magnitude of the diffuse-skylight irradiance varies with altitude and as a result, glaciers in different topographic settings receive different amounts of surface irradiance. Spatio-temporal irradiance patterns appear to be related to glacier surface conditions including supraglacial lakes, and are spatially coincident with ice-flow velocity conditions on some glaciers. Collectively, our results demonstrate that glacier sensitivity to climate change is also locally controlled by numerous multi-scale topographic parameters.
Spatial variability of chlorophyll and nitrogen content of rice from hyperspectral imagery

NASA Astrophysics Data System (ADS)

Moharana, Shreedevi; Dutta, Subashisa

2016-12-01

Chlorophyll and nitrogen are the most essential parameters for paddy crop growth. Spectroradiometric measurements were collected at canopy level during critical growth period of rice. Chemical analysis was performed to quantify the total leaf content. By exploiting the ground based measurements, regression models were established for chlorophyll and nitrogen aimed indices with their corresponding crop growth variables. Vegetation index models were developed for mapping these parameters from Hyperion imagery in an agriculture system. It was inferred that the present Simple Ratio (SR) and Leaf Nitrogen Concentration (LNC) indices, which followed a linear and nonlinear relationship respectively, were completely different from published Tian et al. (2011). The nitrogen content varied widely from 1 to 4% and only 2 to 3% for paddy crop using present modified index models and Tian et al. (2011) respectively. The modified LNC index model performed better than the established Tian et al. (2011) model as far as estimated nitrogen content from Hyperion imagery was concerned. Furthermore, within the observed chlorophyll range obtained from the studied rice varieties grown in the rice agriculture system, the index models (LNC, OASVI, Gitelson, mSR and MTCI) performed well in the spatial distribution of rice chlorophyll content from Hyperion imagery. Spatial distribution of total chlorophyll content varied widely from 1.77 to 5.81 mg/g (LNC), 3.0 to 13 mg/g (OASVI), 0.5 to 10.43 mg/g (Gitelson), 2.18 to 10.61 mg/g (mSR) and 2.90 to 5.40 mg/g (MTCI). The spatial information of these parameters will help in proper nutrient management, yield forecasting, and will serve as inputs for crop growth and forecasting models for a precision rice agriculture system.
Step-control of electromechanical systems

DOEpatents

Lewis, Robert N.

1979-01-01

The response of an automatic control system to a general input signal is improved by applying a test input signal, observing the response to the test input signal and determining correctional constants necessary to provide a modified input signal to be added to the input to the system. A method is disclosed for determining correctional constants. The modified input signal, when applied in conjunction with an operating signal, provides a total system output exhibiting an improved response. This method is applicable to open-loop or closed-loop control systems. The method is also applicable to unstable systems, thus allowing controlled shut-down before dangerous or destructive response is achieved and to systems whose characteristics vary with time, thus resulting in improved adaptive systems.
BIREFRINGENT FILTER MODEL

NASA Technical Reports Server (NTRS)

Cross, P. L.

1994-01-01

Birefringent filters are often used as line-narrowing components in solid state lasers. The Birefringent Filter Model program generates a stand-alone model of a birefringent filter for use in designing and analyzing a birefringent filter. It was originally developed to aid in the design of solid state lasers to be used on aircraft or spacecraft to perform remote sensing of the atmosphere. The model is general enough to allow the user to address problems such as temperature stability requirements, manufacturing tolerances, and alignment tolerances. The input parameters for the program are divided into 7 groups: 1) general parameters which refer to all elements of the filter; 2) wavelength related parameters; 3) filter, coating and orientation parameters; 4) input ray parameters; 5) output device specifications; 6) component related parameters; and 7) transmission profile parameters. The program can analyze a birefringent filter with up to 12 different components, and can calculate the transmission and summary parameters for multiple passes as well as a single pass through the filter. The Jones matrix, which is calculated from the input parameters of Groups 1 through 4, is used to calculate the transmission. Output files containing the calculated transmission or the calculated Jones' matrix as a function of wavelength can be created. These output files can then be used as inputs for user written programs. For example, to plot the transmission or to calculate the eigen-transmittances and the corresponding eigen-polarizations for the Jones' matrix, write the appropriate data to a file. The Birefringent Filter Model is written in Microsoft FORTRAN 2.0. The program format is interactive. It was developed on an IBM PC XT equipped with an 8087 math coprocessor, and has a central memory requirement of approximately 154K. Since Microsoft FORTRAN 2.0 does not support complex arithmetic, matrix routines for addition, subtraction, and multiplication of complex, double precision variables are included. The Birefringent Filter Model was written in 1987.
Numerically accurate computational techniques for optimal estimator analyses of multi-parameter models

NASA Astrophysics Data System (ADS)

Berger, Lukas; Kleinheinz, Konstantin; Attili, Antonio; Bisetti, Fabrizio; Pitsch, Heinz; Mueller, Michael E.

2018-05-01

Modelling unclosed terms in partial differential equations typically involves two steps: First, a set of known quantities needs to be specified as input parameters for a model, and second, a specific functional form needs to be defined to model the unclosed terms by the input parameters. Both steps involve a certain modelling error, with the former known as the irreducible error and the latter referred to as the functional error. Typically, only the total modelling error, which is the sum of functional and irreducible error, is assessed, but the concept of the optimal estimator enables the separate analysis of the total and the irreducible errors, yielding a systematic modelling error decomposition. In this work, attention is paid to the techniques themselves required for the practical computation of irreducible errors. Typically, histograms are used for optimal estimator analyses, but this technique is found to add a non-negligible spurious contribution to the irreducible error if models with multiple input parameters are assessed. Thus, the error decomposition of an optimal estimator analysis becomes inaccurate, and misleading conclusions concerning modelling errors may be drawn. In this work, numerically accurate techniques for optimal estimator analyses are identified and a suitable evaluation of irreducible errors is presented. Four different computational techniques are considered: a histogram technique, artificial neural networks, multivariate adaptive regression splines, and an additive model based on a kernel method. For multiple input parameter models, only artificial neural networks and multivariate adaptive regression splines are found to yield satisfactorily accurate results. Beyond a certain number of input parameters, the assessment of models in an optimal estimator analysis even becomes practically infeasible if histograms are used. The optimal estimator analysis in this paper is applied to modelling the filtered soot intermittency in large eddy simulations using a dataset of a direct numerical simulation of a non-premixed sooting turbulent flame.
Analysis of Artificial Neural Network in Erosion Modeling: A Case Study of Serang Watershed

NASA Astrophysics Data System (ADS)

Arif, N.; Danoedoro, P.; Hartono

2017-12-01

Erosion modeling is an important measuring tool for both land users and decision makers to evaluate land cultivation and thus it is necessary to have a model to represent the actual reality. Erosion models are a complex model because of uncertainty data with different sources and processing procedures. Artificial neural networks can be relied on for complex and non-linear data processing such as erosion data. The main difficulty in artificial neural network training is the determination of the value of each network input parameters, i.e. hidden layer, momentum, learning rate, momentum, and RMS. This study tested the capability of artificial neural network application in the prediction of erosion risk with some input parameters through multiple simulations to get good classification results. The model was implemented in Serang Watershed, Kulonprogo, Yogyakarta which is one of the critical potential watersheds in Indonesia. The simulation results showed the number of iterations that gave a significant effect on the accuracy compared to other parameters. A small number of iterations can produce good accuracy if the combination of other parameters was right. In this case, one hidden layer was sufficient to produce good accuracy. The highest training accuracy achieved in this study was 99.32%, occurred in ANN 14 simulation with combination of network input parameters of 1 HL; LR 0.01; M 0.5; RMS 0.0001, and the number of iterations of 15000. The ANN training accuracy was not influenced by the number of channels, namely input dataset (erosion factors) as well as data dimensions, rather it was determined by changes in network parameters.
Hearing AIDS and music.

PubMed

Chasin, Marshall; Russo, Frank A

2004-01-01

Historically, the primary concern for hearing aid design and fitting is optimization for speech inputs. However, increasingly other types of inputs are being investigated and this is certainly the case for music. Whether the hearing aid wearer is a musician or merely someone who likes to listen to music, the electronic and electro-acoustic parameters described can be optimized for music as well as for speech. That is, a hearing aid optimally set for music can be optimally set for speech, even though the converse is not necessarily true. Similarities and differences between speech and music as inputs to a hearing aid are described. Many of these lead to the specification of a set of optimal electro-acoustic characteristics. Parameters such as the peak input-limiting level, compression issues-both compression ratio and knee-points-and number of channels all can deleteriously affect music perception through hearing aids. In other cases, it is not clear how to set other parameters such as noise reduction and feedback control mechanisms. Regardless of the existence of a "music program,'' unless the various electro-acoustic parameters are available in a hearing aid, music fidelity will almost always be less than optimal. There are many unanswered questions and hypotheses in this area. Future research by engineers, researchers, clinicians, and musicians will aid in the clarification of these questions and their ultimate solutions.
Optimal simulations of ultrasonic fields produced by large thermal therapy arrays using the angular spectrum approach

PubMed Central

Zeng, Xiaozheng; McGough, Robert J.

2009-01-01

The angular spectrum approach is evaluated for the simulation of focused ultrasound fields produced by large thermal therapy arrays. For an input pressure or normal particle velocity distribution in a plane, the angular spectrum approach rapidly computes the output pressure field in a three dimensional volume. To determine the optimal combination of simulation parameters for angular spectrum calculations, the effect of the size, location, and the numerical accuracy of the input plane on the computed output pressure is evaluated. Simulation results demonstrate that angular spectrum calculations performed with an input pressure plane are more accurate than calculations with an input velocity plane. Results also indicate that when the input pressure plane is slightly larger than the array aperture and is located approximately one wavelength from the array, angular spectrum simulations have very small numerical errors for two dimensional planar arrays. Furthermore, the root mean squared error from angular spectrum simulations asymptotically approaches a nonzero lower limit as the error in the input plane decreases. Overall, the angular spectrum approach is an accurate and robust method for thermal therapy simulations of large ultrasound phased arrays when the input pressure plane is computed with the fast nearfield method and an optimal combination of input parameters. PMID:19425640
Probabilistic Density Function Method for Stochastic ODEs of Power Systems with Uncertain Power Input

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

Wang, Peng; Barajas-Solano, David A.; Constantinescu, Emil

Wind and solar power generators are commonly described by a system of stochastic ordinary differential equations (SODEs) where random input parameters represent uncertainty in wind and solar energy. The existing methods for SODEs are mostly limited to delta-correlated random parameters (white noise). Here we use the Probability Density Function (PDF) method for deriving a closed-form deterministic partial differential equation (PDE) for the joint probability density function of the SODEs describing a power generator with time-correlated power input. The resulting PDE is solved numerically. A good agreement with Monte Carlo Simulations shows accuracy of the PDF method.
Explicit least squares system parameter identification for exact differential input/output models

NASA Technical Reports Server (NTRS)

Pearson, A. E.

1993-01-01

The equation error for a class of systems modeled by input/output differential operator equations has the potential to be integrated exactly, given the input/output data on a finite time interval, thereby opening up the possibility of using an explicit least squares estimation technique for system parameter identification. The paper delineates the class of models for which this is possible and shows how the explicit least squares cost function can be obtained in a way that obviates dealing with unknown initial and boundary conditions. The approach is illustrated by two examples: a second order chemical kinetics model and a third order system of Lorenz equations.
Femtosecond soliton source with fast and broad spectral tunability.

PubMed

Masip, Martin E; Rieznik, A A; König, Pablo G; Grosz, Diego F; Bragas, Andrea V; Martinez, Oscar E

2009-03-15

We present a complete set of measurements and numerical simulations of a femtosecond soliton source with fast and broad spectral tunability and nearly constant pulse width and average power. Solitons generated in a photonic crystal fiber, at the low-power coupling regime, can be tuned in a broad range of wavelengths, from 850 to 1200 nm using the input power as the control parameter. These solitons keep almost constant time duration (approximately 40 fs) and spectral widths (approximately 20 nm) over the entire measured spectra regardless of input power. Our numerical simulations agree well with measurements and predict a wide working wavelength range and robustness to input parameters.
Uncertainty analyses of CO2 plume expansion subsequent to wellbore CO2 leakage into aquifers

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

Hou, Zhangshuan; Bacon, Diana H.; Engel, David W.

2014-08-01

In this study, we apply an uncertainty quantification (UQ) framework to CO2 sequestration problems. In one scenario, we look at the risk of wellbore leakage of CO2 into a shallow unconfined aquifer in an urban area; in another scenario, we study the effects of reservoir heterogeneity on CO2 migration. We combine various sampling approaches (quasi-Monte Carlo, probabilistic collocation, and adaptive sampling) in order to reduce the number of forward calculations while trying to fully explore the input parameter space and quantify the input uncertainty. The CO2 migration is simulated using the PNNL-developed simulator STOMP-CO2e (the water-salt-CO2 module). For computationally demandingmore » simulations with 3D heterogeneity fields, we combined the framework with a scalable version module, eSTOMP, as the forward modeling simulator. We built response curves and response surfaces of model outputs with respect to input parameters, to look at the individual and combined effects, and identify and rank the significance of the input parameters.« less
Vastly accelerated linear least-squares fitting with numerical optimization for dual-input delay-compensated quantitative liver perfusion mapping.

PubMed

Jafari, Ramin; Chhabra, Shalini; Prince, Martin R; Wang, Yi; Spincemaille, Pascal

2018-04-01

To propose an efficient algorithm to perform dual input compartment modeling for generating perfusion maps in the liver. We implemented whole field-of-view linear least squares (LLS) to fit a delay-compensated dual-input single-compartment model to very high temporal resolution (four frames per second) contrast-enhanced 3D liver data, to calculate kinetic parameter maps. Using simulated data and experimental data in healthy subjects and patients, whole-field LLS was compared with the conventional voxel-wise nonlinear least-squares (NLLS) approach in terms of accuracy, performance, and computation time. Simulations showed good agreement between LLS and NLLS for a range of kinetic parameters. The whole-field LLS method allowed generating liver perfusion maps approximately 160-fold faster than voxel-wise NLLS, while obtaining similar perfusion parameters. Delay-compensated dual-input liver perfusion analysis using whole-field LLS allows generating perfusion maps with a considerable speedup compared with conventional voxel-wise NLLS fitting. Magn Reson Med 79:2415-2421, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.
FAST: Fitting and Assessment of Synthetic Templates

NASA Astrophysics Data System (ADS)

Kriek, Mariska; van Dokkum, Pieter G.; Labbé, Ivo; Franx, Marijn; Illingworth, Garth D.; Marchesini, Danilo; Quadri, Ryan F.; Aird, James; Coil, Alison L.; Georgakakis, Antonis

2018-03-01

FAST (Fitting and Assessment of Synthetic Templates) fits stellar population synthesis templates to broadband photometry and/or spectra. FAST is compatible with the photometric redshift code EAzY (ascl:1010.052) when fitting broadband photometry; it uses the photometric redshifts derived by EAzY, and the input files (for examply, photometric catalog and master filter file) are the same. FAST fits spectra in combination with broadband photometric data points or simultaneously fits two components, allowing for an AGN contribution in addition to the host galaxy light. Depending on the input parameters, FAST outputs the best-fit redshift, age, dust content, star formation timescale, metallicity, stellar mass, star formation rate (SFR), and their confidence intervals. Though some of FAST's functions overlap with those of HYPERZ (ascl:1108.010), it differs by fitting fluxes instead of magnitudes, allows the user to completely define the grid of input stellar population parameters and easily input photometric redshifts and their confidence intervals, and calculates calibrated confidence intervals for all parameters. Note that FAST is not a photometric redshift code, though it can be used as one.
Simulations of Brady's-Type Fault Undergoing CO2 Push-Pull: Pressure-Transient and Sensitivity Analysis

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

Jung, Yoojin; Doughty, Christine

Input and output files used for fault characterization through numerical simulation using iTOUGH2. The synthetic data for the push period are generated by running a forward simulation (input parameters are provided in iTOUGH2 Brady GF6 Input Parameters.txt [InvExt6i.txt]). In general, the permeability of the fault gouge, damage zone, and matrix are assumed to be unknown. The input and output files are for the inversion scenario where only pressure transients are available at the monitoring well located 200 m above the injection well and only the fault gouge permeability is estimated. The input files are named InvExt6i, INPUT.tpl, FOFT.ins, CO2TAB, andmore » the output files are InvExt6i.out, pest.fof, and pest.sav (names below are display names). The table graphic in the data files below summarizes the inversion results, and indicates the fault gouge permeability can be estimated even if imperfect guesses are used for matrix and damage zone permeabilities, and permeability anisotropy is not taken into account.« less
Input-dependent frequency modulation of cortical gamma oscillations shapes spatial synchronization and enables phase coding.

PubMed

Lowet, Eric; Roberts, Mark; Hadjipapas, Avgis; Peter, Alina; van der Eerden, Jan; De Weerd, Peter

2015-02-01

Fine-scale temporal organization of cortical activity in the gamma range (∼25-80Hz) may play a significant role in information processing, for example by neural grouping ('binding') and phase coding. Recent experimental studies have shown that the precise frequency of gamma oscillations varies with input drive (e.g. visual contrast) and that it can differ among nearby cortical locations. This has challenged theories assuming widespread gamma synchronization at a fixed common frequency. In the present study, we investigated which principles govern gamma synchronization in the presence of input-dependent frequency modulations and whether they are detrimental for meaningful input-dependent gamma-mediated temporal organization. To this aim, we constructed a biophysically realistic excitatory-inhibitory network able to express different oscillation frequencies at nearby spatial locations. Similarly to cortical networks, the model was topographically organized with spatially local connectivity and spatially-varying input drive. We analyzed gamma synchronization with respect to phase-locking, phase-relations and frequency differences, and quantified the stimulus-related information represented by gamma phase and frequency. By stepwise simplification of our models, we found that the gamma-mediated temporal organization could be reduced to basic synchronization principles of weakly coupled oscillators, where input drive determines the intrinsic (natural) frequency of oscillators. The gamma phase-locking, the precise phase relation and the emergent (measurable) frequencies were determined by two principal factors: the detuning (intrinsic frequency difference, i.e. local input difference) and the coupling strength. In addition to frequency coding, gamma phase contained complementary stimulus information. Crucially, the phase code reflected input differences, but not the absolute input level. This property of relative input-to-phase conversion, contrasting with latency codes or slower oscillation phase codes, may resolve conflicting experimental observations on gamma phase coding. Our modeling results offer clear testable experimental predictions. We conclude that input-dependency of gamma frequencies could be essential rather than detrimental for meaningful gamma-mediated temporal organization of cortical activity.
Input-Dependent Frequency Modulation of Cortical Gamma Oscillations Shapes Spatial Synchronization and Enables Phase Coding

PubMed Central

Lowet, Eric; Roberts, Mark; Hadjipapas, Avgis; Peter, Alina; van der Eerden, Jan; De Weerd, Peter

2015-01-01

Fine-scale temporal organization of cortical activity in the gamma range (∼25–80Hz) may play a significant role in information processing, for example by neural grouping (‘binding’) and phase coding. Recent experimental studies have shown that the precise frequency of gamma oscillations varies with input drive (e.g. visual contrast) and that it can differ among nearby cortical locations. This has challenged theories assuming widespread gamma synchronization at a fixed common frequency. In the present study, we investigated which principles govern gamma synchronization in the presence of input-dependent frequency modulations and whether they are detrimental for meaningful input-dependent gamma-mediated temporal organization. To this aim, we constructed a biophysically realistic excitatory-inhibitory network able to express different oscillation frequencies at nearby spatial locations. Similarly to cortical networks, the model was topographically organized with spatially local connectivity and spatially-varying input drive. We analyzed gamma synchronization with respect to phase-locking, phase-relations and frequency differences, and quantified the stimulus-related information represented by gamma phase and frequency. By stepwise simplification of our models, we found that the gamma-mediated temporal organization could be reduced to basic synchronization principles of weakly coupled oscillators, where input drive determines the intrinsic (natural) frequency of oscillators. The gamma phase-locking, the precise phase relation and the emergent (measurable) frequencies were determined by two principal factors: the detuning (intrinsic frequency difference, i.e. local input difference) and the coupling strength. In addition to frequency coding, gamma phase contained complementary stimulus information. Crucially, the phase code reflected input differences, but not the absolute input level. This property of relative input-to-phase conversion, contrasting with latency codes or slower oscillation phase codes, may resolve conflicting experimental observations on gamma phase coding. Our modeling results offer clear testable experimental predictions. We conclude that input-dependency of gamma frequencies could be essential rather than detrimental for meaningful gamma-mediated temporal organization of cortical activity. PMID:25679780
Differential Contribution of Bilateral Supplementary Motor Area to the Effective Connectivity Networks Induced by Task Conditions Using Dynamic Causal Modeling

PubMed Central

Tao, Zhongping; Zhang, Mu

2014-01-01

Abstract Functional imaging studies have indicated hemispheric asymmetry of activation in bilateral supplementary motor area (SMA) during unimanual motor tasks. However, the hemispherically special roles of bilateral SMAs on primary motor cortex (M1) in the effective connectivity networks (ECN) during lateralized tasks remain unclear. Aiming to study the differential contribution of bilateral SMAs during the motor execution and motor imagery tasks, and the hemispherically asymmetric patterns of ECN among regions involved, the present study used dynamic causal modeling to analyze the functional magnetic resonance imaging data of the unimanual motor execution/imagery tasks in 12 right-handed subjects. Our results demonstrated that distributions of network parameters underlying motor execution and motor imagery were significantly different. The variation was mainly induced by task condition modulations of intrinsic coupling. Particularly, regardless of the performing hand, the task input modulations of intrinsic coupling from the contralateral SMA to contralateral M1 were positive during motor execution, while varied to be negative during motor imagery. The results suggested that the inhibitive modulation suppressed the overt movement during motor imagery. In addition, the left SMA also helped accomplishing left hand tasks through task input modulation of left SMA→right SMA connection, implying that hemispheric recruitment occurred when performing nondominant hand tasks. The results specified differential and altered contributions of bilateral SMAs to the ECN during unimanual motor execution and motor imagery, and highlighted the contributions induced by the task input of motor execution/imagery. PMID:24606178
Roles for Coincidence Detection in Coding Amplitude-Modulated Sounds

PubMed Central

Ashida, Go; Kretzberg, Jutta; Tollin, Daniel J.

2016-01-01

Many sensory neurons encode temporal information by detecting coincident arrivals of synaptic inputs. In the mammalian auditory brainstem, binaural neurons of the medial superior olive (MSO) are known to act as coincidence detectors, whereas in the lateral superior olive (LSO) roles of coincidence detection have remained unclear. LSO neurons receive excitatory and inhibitory inputs driven by ipsilateral and contralateral acoustic stimuli, respectively, and vary their output spike rates according to interaural level differences. In addition, LSO neurons are also sensitive to binaural phase differences of low-frequency tones and envelopes of amplitude-modulated (AM) sounds. Previous physiological recordings in vivo found considerable variations in monaural AM-tuning across neurons. To investigate the underlying mechanisms of the observed temporal tuning properties of LSO and their sources of variability, we used a simple coincidence counting model and examined how specific parameters of coincidence detection affect monaural and binaural AM coding. Spike rates and phase-locking of evoked excitatory and spontaneous inhibitory inputs had only minor effects on LSO output to monaural AM inputs. In contrast, the coincidence threshold of the model neuron affected both the overall spike rates and the half-peak positions of the AM-tuning curve, whereas the width of the coincidence window merely influenced the output spike rates. The duration of the refractory period affected only the low-frequency portion of the monaural AM-tuning curve. Unlike monaural AM coding, temporal factors, such as the coincidence window and the effective duration of inhibition, played a major role in determining the trough positions of simulated binaural phase-response curves. In addition, empirically-observed level-dependence of binaural phase-coding was reproduced in the framework of our minimalistic coincidence counting model. These modeling results suggest that coincidence detection of excitatory and inhibitory synaptic inputs is essential for LSO neurons to encode both monaural and binaural AM sounds. PMID:27322612
Homeostasis in a feed forward loop gene regulatory motif.

PubMed

Antoneli, Fernando; Golubitsky, Martin; Stewart, Ian

2018-05-14

The internal state of a cell is affected by inputs from the extra-cellular environment such as external temperature. If some output, such as the concentration of a target protein, remains approximately constant as inputs vary, the system exhibits homeostasis. Special sub-networks called motifs are unusually common in gene regulatory networks (GRNs), suggesting that they may have a significant biological function. Potentially, one such function is homeostasis. In support of this hypothesis, we show that the feed-forward loop GRN produces homeostasis. Here the inputs are subsumed into a single parameter that affects only the first node in the motif, and the output is the concentration of a target protein. The analysis uses the notion of infinitesimal homeostasis, which occurs when the input-output map has a critical point (zero derivative). In model equations such points can be located using implicit differentiation. If the second derivative of the input-output map also vanishes, the critical point is a chair: the output rises roughly linearly, then flattens out (the homeostasis region or plateau), and then starts to rise again. Chair points are a common cause of homeostasis. In more complicated equations or networks, numerical exploration would have to augment analysis. Thus, in terms of finding chairs, this paper presents a proof of concept. We apply this method to a standard family of differential equations modeling the feed-forward loop GRN, and deduce that chair points occur. This function determines the production of a particular mRNA and the resulting chair points are found analytically. The same method can potentially be used to find homeostasis regions in other GRNs. In the discussion and conclusion section, we also discuss why homeostasis in the motif may persist even when the rest of the network is taken into account. Copyright © 2018 Elsevier Ltd. All rights reserved.

Some links on this page may take you to non-federal websites. Their policies may differ from this site.