Calculation of Optical Parameters of Liquid Crystals

NASA Astrophysics Data System (ADS)

Kumar, A.

2007-12-01

Validation of a modified four-parameter model describing temperature effect on liquid crystal refractive indices is being reported in the present article. This model is based upon the Vuks equation. Experimental data of ordinary and extraordinary refractive indices for two liquid crystal samples MLC-9200-000 and MLC-6608 are used to validate the above-mentioned theoretical model. Using these experimental data, birefringence, order parameter, normalized polarizabilities, and the temperature gradient of refractive indices are determined. Two methods: directly using birefringence measurements and using Haller's extrapolation procedure are adopted for the determination of order parameter. Both approches of order parameter calculation are compared. The temperature dependences of all these parameters are discussed. A close agreement between theory and experiment is obtained.

On determining firing delay time of transitions for Petri net based signaling pathways by introducing stochastic decision rules.

PubMed

Miwa, Yoshimasa; Li, Chen; Ge, Qi-Wei; Matsuno, Hiroshi; Miyano, Satoru

2010-01-01

Parameter determination is important in modeling and simulating biological pathways including signaling pathways. Parameters are determined according to biological facts obtained from biological experiments and scientific publications. However, such reliable data describing detailed reactions are not reported in most cases. This prompted us to develop a general methodology of determining the parameters of a model in the case of that no information of the underlying biological facts is provided. In this study, we use the Petri net approach for modeling signaling pathways, and propose a method to determine firing delay times of transitions for Petri net models of signaling pathways by introducing stochastic decision rules. Petri net technology provides a powerful approach to modeling and simulating various concurrent systems, and recently have been widely accepted as a description method for biological pathways. Our method enables to determine the range of firing delay time which realizes smooth token flows in the Petri net model of a signaling pathway. The availability of this method has been confirmed by the results of an application to the interleukin-1 induced signaling pathway.

On determining firing delay time of transitions for petri net based signaling pathways by introducing stochastic decision rules.

PubMed

Miwa, Yoshimasa; Li, Chen; Ge, Qi-Wei; Matsuno, Hiroshi; Miyano, Satoru

2011-01-01

Parameter determination is important in modeling and simulating biological pathways including signaling pathways. Parameters are determined according to biological facts obtained from biological experiments and scientific publications. However, such reliable data describing detailed reactions are not reported in most cases. This prompted us to develop a general methodology of determining the parameters of a model in the case of that no information of the underlying biological facts is provided. In this study, we use the Petri net approach for modeling signaling pathways, and propose a method to determine firing delay times of transitions for Petri net models of signaling pathways by introducing stochastic decision rules. Petri net technology provides a powerful approach to modeling and simulating various concurrent systems, and recently have been widely accepted as a description method for biological pathways. Our method enables to determine the range of firing delay time which realizes smooth token flows in the Petri net model of a signaling pathway. The availability of this method has been confirmed by the results of an application to the interleukin-1 induced signaling pathway.

Determining geometric error model parameters of a terrestrial laser scanner through Two-face, Length-consistency, and Network methods

PubMed Central

Wang, Ling; Muralikrishnan, Bala; Rachakonda, Prem; Sawyer, Daniel

2017-01-01

Terrestrial laser scanners (TLS) are increasingly used in large-scale manufacturing and assembly where required measurement uncertainties are on the order of few tenths of a millimeter or smaller. In order to meet these stringent requirements, systematic errors within a TLS are compensated in-situ through self-calibration. In the Network method of self-calibration, numerous targets distributed in the work-volume are measured from multiple locations with the TLS to determine parameters of the TLS error model. In this paper, we propose two new self-calibration methods, the Two-face method and the Length-consistency method. The Length-consistency method is proposed as a more efficient way of realizing the Network method where the length between any pair of targets from multiple TLS positions are compared to determine TLS model parameters. The Two-face method is a two-step process. In the first step, many model parameters are determined directly from the difference between front-face and back-face measurements of targets distributed in the work volume. In the second step, all remaining model parameters are determined through the Length-consistency method. We compare the Two-face method, the Length-consistency method, and the Network method in terms of the uncertainties in the model parameters, and demonstrate the validity of our techniques using a calibrated scale bar and front-face back-face target measurements. The clear advantage of these self-calibration methods is that a reference instrument or calibrated artifacts are not required, thus significantly lowering the cost involved in the calibration process. PMID:28890607

Analysis of sensitivity of simulated recharge to selected parameters for seven watersheds modeled using the precipitation-runoff modeling system

USGS Publications Warehouse

Ely, D. Matthew

2006-01-01

Recharge is a vital component of the ground-water budget and methods for estimating it range from extremely complex to relatively simple. The most commonly used techniques, however, are limited by the scale of application. One method that can be used to estimate ground-water recharge includes process-based models that compute distributed water budgets on a watershed scale. These models should be evaluated to determine which model parameters are the dominant controls in determining ground-water recharge. Seven existing watershed models from different humid regions of the United States were chosen to analyze the sensitivity of simulated recharge to model parameters. Parameter sensitivities were determined using a nonlinear regression computer program to generate a suite of diagnostic statistics. The statistics identify model parameters that have the greatest effect on simulated ground-water recharge and that compare and contrast the hydrologic system responses to those parameters. Simulated recharge in the Lost River and Big Creek watersheds in Washington State was sensitive to small changes in air temperature. The Hamden watershed model in west-central Minnesota was developed to investigate the relations that wetlands and other landscape features have with runoff processes. Excess soil moisture in the Hamden watershed simulation was preferentially routed to wetlands, instead of to the ground-water system, resulting in little sensitivity of any parameters to recharge. Simulated recharge in the North Fork Pheasant Branch watershed, Wisconsin, demonstrated the greatest sensitivity to parameters related to evapotranspiration. Three watersheds were simulated as part of the Model Parameter Estimation Experiment (MOPEX). Parameter sensitivities for the MOPEX watersheds, Amite River, Louisiana and Mississippi, English River, Iowa, and South Branch Potomac River, West Virginia, were similar and most sensitive to small changes in air temperature and a user-defined flow routing parameter. Although the primary objective of this study was to identify, by geographic region, the importance of the parameter value to the simulation of ground-water recharge, the secondary objectives proved valuable for future modeling efforts. The value of a rigorous sensitivity analysis can (1) make the calibration process more efficient, (2) guide additional data collection, (3) identify model limitations, and (4) explain simulated results.

Macromolecular refinement by model morphing using non-atomic parameterizations.

PubMed

Cowtan, Kevin; Agirre, Jon

2018-02-01

Refinement is a critical step in the determination of a model which explains the crystallographic observations and thus best accounts for the missing phase components. The scattering density is usually described in terms of atomic parameters; however, in macromolecular crystallography the resolution of the data is generally insufficient to determine the values of these parameters for individual atoms. Stereochemical and geometric restraints are used to provide additional information, but produce interrelationships between parameters which slow convergence, resulting in longer refinement times. An alternative approach is proposed in which parameters are not attached to atoms, but to regions of the electron-density map. These parameters can move the density or change the local temperature factor to better explain the structure factors. Varying the size of the region which determines the parameters at a particular position in the map allows the method to be applied at different resolutions without the use of restraints. Potential applications include initial refinement of molecular-replacement models with domain motions, and potentially the use of electron density from other sources such as electron cryo-microscopy (cryo-EM) as the refinement model.

Experiment Analysis and Modelling of Compaction Behaviour of Ag60Cu30Sn10 Mixed Metal Powders

NASA Astrophysics Data System (ADS)

Zhou, Mengcheng; Huang, Shangyu; Liu, Wei; Lei, Yu; Yan, Shiwei

2018-03-01

A novel process method combines powder compaction and sintering was employed to fabricate thin sheets of cadmium-free silver based filler metals, the compaction densification behaviour of Ag60Cu30Sn10 mixed metal powders was investigated experimentally. Based on the equivalent density method, the density-dependent Drucker-Prager Cap (DPC) model was introduced to model the powder compaction behaviour. Various experiment procedures were completed to determine the model parameters. The friction coefficients in lubricated and unlubricated die were experimentally determined. The determined material parameters were validated by experiments and numerical simulation of powder compaction process using a user subroutine (USDFLD) in ABAQUS/Standard. The good agreement between the simulated and experimental results indicates that the determined model parameters are able to describe the compaction behaviour of the multicomponent mixed metal powders, which can be further used for process optimization simulations.

Multivariate modelling of prostate cancer combining magnetic resonance derived T2, diffusion, dynamic contrast-enhanced and spectroscopic parameters.

PubMed

Riches, S F; Payne, G S; Morgan, V A; Dearnaley, D; Morgan, S; Partridge, M; Livni, N; Ogden, C; deSouza, N M

2015-05-01

The objectives are determine the optimal combination of MR parameters for discriminating tumour within the prostate using linear discriminant analysis (LDA) and to compare model accuracy with that of an experienced radiologist. Multiparameter MRIs in 24 patients before prostatectomy were acquired. Tumour outlines from whole-mount histology, T2-defined peripheral zone (PZ), and central gland (CG) were superimposed onto slice-matched parametric maps. T2, Apparent Diffusion Coefficient, initial area under the gadolinium curve, vascular parameters (K(trans),Kep,Ve), and (choline+polyamines+creatine)/citrate were compared between tumour and non-tumour tissues. Receiver operating characteristic (ROC) curves determined sensitivity and specificity at spectroscopic voxel resolution and per lesion, and LDA determined the optimal multiparametric model for identifying tumours. Accuracy was compared with an expert observer. Tumours were significantly different from PZ and CG for all parameters (all p < 0.001). Area under the ROC curve for discriminating tumour from non-tumour was significantly greater (p < 0.001) for the multiparametric model than for individual parameters; at 90 % specificity, sensitivity was 41 % (MRSI voxel resolution) and 59 % per lesion. At this specificity, an expert observer achieved 28 % and 49 % sensitivity, respectively. The model was more accurate when parameters from all techniques were included and performed better than an expert observer evaluating these data. • The combined model increases diagnostic accuracy in prostate cancer compared with individual parameters • The optimal combined model includes parameters from diffusion, spectroscopy, perfusion, and anatominal MRI • The computed model improves tumour detection compared to an expert viewing parametric maps.

A simplified method for determining reactive rate parameters for reaction ignition and growth in explosives

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

Miller, P.J.

1996-07-01

A simplified method for determining the reactive rate parameters for the ignition and growth model is presented. This simplified ignition and growth (SIG) method consists of only two adjustable parameters, the ignition (I) and growth (G) rate constants. The parameters are determined by iterating these variables in DYNA2D hydrocode simulations of the failure diameter and the gap test sensitivity until the experimental values are reproduced. Examples of four widely different explosives were evaluated using the SIG model. The observed embedded gauge stress-time profiles for these explosives are compared to those calculated by the SIG equation and the results are described.

Determining Kinetic Parameters for Isothermal Crystallization of Glasses

NASA Technical Reports Server (NTRS)

Ray, C. S.; Zhang, T.; Reis, S. T.; Brow, R. K.

2006-01-01

Non-isothermal crystallization techniques are frequently used to determine the kinetic parameters for crystallization in glasses. These techniques are experimentally simple and quick compared to the isothermal techniques. However, the analytical models used for non-isothermal data analysis, originally developed for describing isothermal transformation kinetics, are fundamentally flawed. The present paper describes a technique for determining the kinetic parameters for isothermal crystallization in glasses, which eliminates most of the common problems that generally make the studies of isothermal crystallization laborious and time consuming. In this technique, the volume fraction of glass that is crystallized as a function of time during an isothermal hold was determined using differential thermal analysis (DTA). The crystallization parameters for the lithium-disilicate (Li2O.2SiO2) model glass were first determined and compared to the same parameters determined by other techniques to establish the accuracy and usefulness of the present technique. This technique was then used to describe the crystallization kinetics of a complex Ca-Sr-Zn-silicate glass developed for sealing solid oxide fuel cells.

Development of a Screening Model for Design and Costing of an Innovative Tailored Granular Activated Carbon Technology to Treat Perchlorate-Contaminated Water

DTIC Science & Technology

2007-03-01

column experiments were used to obtain model parameters . Cost data used in the model were based on conventional GAC installations, as modified to...43 Calculation of Parameters ...66 Determination of Parameter Values

Assessing the accuracy of subject-specific, muscle-model parameters determined by optimizing to match isometric strength.

PubMed

DeSmitt, Holly J; Domire, Zachary J

2016-12-01

Biomechanical models are sensitive to the choice of model parameters. Therefore, determination of accurate subject specific model parameters is important. One approach to generate these parameters is to optimize the values such that the model output will match experimentally measured strength curves. This approach is attractive as it is inexpensive and should provide an excellent match to experimentally measured strength. However, given the problem of muscle redundancy, it is not clear that this approach generates accurate individual muscle forces. The purpose of this investigation is to evaluate this approach using simulated data to enable a direct comparison. It is hypothesized that the optimization approach will be able to recreate accurate muscle model parameters when information from measurable parameters is given. A model of isometric knee extension was developed to simulate a strength curve across a range of knee angles. In order to realistically recreate experimentally measured strength, random noise was added to the modeled strength. Parameters were solved for using a genetic search algorithm. When noise was added to the measurements the strength curve was reasonably recreated. However, the individual muscle model parameters and force curves were far less accurate. Based upon this examination, it is clear that very different sets of model parameters can recreate similar strength curves. Therefore, experimental variation in strength measurements has a significant influence on the results. Given the difficulty in accurately recreating individual muscle parameters, it may be more appropriate to perform simulations with lumped actuators representing similar muscles.

Technical Note: Using experimentally determined proton spot scanning timing parameters to accurately model beam delivery time.

PubMed

Shen, Jiajian; Tryggestad, Erik; Younkin, James E; Keole, Sameer R; Furutani, Keith M; Kang, Yixiu; Herman, Michael G; Bues, Martin

2017-10-01

To accurately model the beam delivery time (BDT) for a synchrotron-based proton spot scanning system using experimentally determined beam parameters. A model to simulate the proton spot delivery sequences was constructed, and BDT was calculated by summing times for layer switch, spot switch, and spot delivery. Test plans were designed to isolate and quantify the relevant beam parameters in the operation cycle of the proton beam therapy delivery system. These parameters included the layer switch time, magnet preparation and verification time, average beam scanning speeds in x- and y-directions, proton spill rate, and maximum charge and maximum extraction time for each spill. The experimentally determined parameters, as well as the nominal values initially provided by the vendor, served as inputs to the model to predict BDTs for 602 clinical proton beam deliveries. The calculated BDTs (T BDT ) were compared with the BDTs recorded in the treatment delivery log files (T Log ): ∆t = T Log -T BDT . The experimentally determined average layer switch time for all 97 energies was 1.91 s (ranging from 1.9 to 2.0 s for beam energies from 71.3 to 228.8 MeV), average magnet preparation and verification time was 1.93 ms, the average scanning speeds were 5.9 m/s in x-direction and 19.3 m/s in y-direction, the proton spill rate was 8.7 MU/s, and the maximum proton charge available for one acceleration is 2.0 ± 0.4 nC. Some of the measured parameters differed from the nominal values provided by the vendor. The calculated BDTs using experimentally determined parameters matched the recorded BDTs of 602 beam deliveries (∆t = -0.49 ± 1.44 s), which were significantly more accurate than BDTs calculated using nominal timing parameters (∆t = -7.48 ± 6.97 s). An accurate model for BDT prediction was achieved by using the experimentally determined proton beam therapy delivery parameters, which may be useful in modeling the interplay effect and patient throughput. The model may provide guidance on how to effectively reduce BDT and may be used to identifying deteriorating machine performance. © 2017 American Association of Physicists in Medicine.

A trade-off solution between model resolution and covariance in surface-wave inversion

USGS Publications Warehouse

Xia, J.; Xu, Y.; Miller, R.D.; Zeng, C.

2010-01-01

Regularization is necessary for inversion of ill-posed geophysical problems. Appraisal of inverse models is essential for meaningful interpretation of these models. Because uncertainties are associated with regularization parameters, extra conditions are usually required to determine proper parameters for assessing inverse models. Commonly used techniques for assessment of a geophysical inverse model derived (generally iteratively) from a linear system are based on calculating the model resolution and the model covariance matrices. Because the model resolution and the model covariance matrices of the regularized solutions are controlled by the regularization parameter, direct assessment of inverse models using only the covariance matrix may provide incorrect results. To assess an inverted model, we use the concept of a trade-off between model resolution and covariance to find a proper regularization parameter with singular values calculated in the last iteration. We plot the singular values from large to small to form a singular value plot. A proper regularization parameter is normally the first singular value that approaches zero in the plot. With this regularization parameter, we obtain a trade-off solution between model resolution and model covariance in the vicinity of a regularized solution. The unit covariance matrix can then be used to calculate error bars of the inverse model at a resolution level determined by the regularization parameter. We demonstrate this approach with both synthetic and real surface-wave data. ?? 2010 Birkh??user / Springer Basel AG.

Calibration of infiltration parameters on hydrological tank model using runoff coefficient of rational method

NASA Astrophysics Data System (ADS)

Suryoputro, Nugroho; Suhardjono, Soetopo, Widandi; Suhartanto, Ery

2017-09-01

In calibrating hydrological models, there are generally two stages of activity: 1) determining realistic model initial parameters in representing natural component physical processes, 2) entering initial parameter values which are then processed by trial error or automatically to obtain optimal values. To determine a realistic initial value, it takes experience and user knowledge of the model. This is a problem for beginner model users. This paper will present another approach to estimate the infiltration parameters in the tank model. The parameters will be approximated by the runoff coefficient of rational method. The value approach of infiltration parameter is simply described as the result of the difference in the percentage of total rainfall minus the percentage of runoff. It is expected that the results of this research will accelerate the calibration process of tank model parameters. The research was conducted on the sub-watershed Kali Bango in Malang Regency with an area of 239,71 km2. Infiltration measurements were carried out in January 2017 to March 2017. Analysis of soil samples at Soil Physics Laboratory, Department of Soil Science, Faculty of Agriculture, Universitas Brawijaya. Rainfall and discharge data were obtained from UPT PSAWS Bango Gedangan in Malang. Temperature, evaporation, relative humidity, wind speed data was obtained from BMKG station of Karang Ploso, Malang. The results showed that the infiltration coefficient at the top tank outlet can be determined its initial value by using the approach of the coefficient of runoff rational method with good result.

Determination of the atrazine migration parameters in Vertisol

NASA Astrophysics Data System (ADS)

Raymundo-Raymundo, E.; Hernandez-Vargas, J.; Nikol'Skii, Yu. N.; Guber, A. K.; Gavi-Reyes, F.; Prado-Pano, B. L.; Figueroa-Sandoval, B.; Mendosa-Hernandez, J. R.

2010-05-01

The parameters of the atrazine migration in columns with undisturbed Vertisol sampled from an irrigated plot in Guanajuato, Mexico were determined. A model of the convection-dispersion transport of the chemical compounds accounting for the decomposition and equilibrium adsorption, which is widely applied for assessing the risk of contamination of natural waters with pesticides, was used. The model parameters were obtained by solving the inverse problem of the transport equation on the basis of laboratory experiments on the transport of the 18O isotope and atrazine in soil columns with an undisturbed structure at three filtration velocities. The model adequately described the experimental data at the individual selection of the parameters for each output curve. Physically unsubstantiated parameters of the atrazine adsorption and degradation were obtained when the parameter of the hydrodynamic dispersion was determined from the data on the 18O migration. The simulation also showed that the use of parameters obtained at water content close to saturation in the calculations for an unsaturated soil resulted in the overestimation of the leaching rate and the maximum concentration of atrazine in the output curve compared to the experimental data.

Statistical sensitivity analysis of a simple nuclear waste repository model

NASA Astrophysics Data System (ADS)

Ronen, Y.; Lucius, J. L.; Blow, E. M.

1980-06-01

A preliminary step in a comprehensive sensitivity analysis of the modeling of a nuclear waste repository. The purpose of the complete analysis is to determine which modeling parameters and physical data are most important in determining key design performance criteria and then to obtain the uncertainty in the design for safety considerations. The theory for a statistical screening design methodology is developed for later use in the overall program. The theory was applied to the test case of determining the relative importance of the sensitivity of near field temperature distribution in a single level salt repository to modeling parameters. The exact values of the sensitivities to these physical and modeling parameters were then obtained using direct methods of recalculation. The sensitivity coefficients found to be important for the sample problem were thermal loading, distance between the spent fuel canisters and their radius. Other important parameters were those related to salt properties at a point of interest in the repository.

Determination of Complex Microcalorimeter Parameters with Impedance Measurements

NASA Technical Reports Server (NTRS)

Saab, T.; Bandler, S. R.; Chervenak, J.; Figueroa-Feliciano, E.; Finkbeiner, F.; Iyomoto, N.; Kelley, R.; Kilbourne, C. A.; Lindeman, M. A.; Porter, F. S.;

Determination of the optimal mesh parameters for Iguassu centrifuge flow and separation calculations

NASA Astrophysics Data System (ADS)

Romanihin, S. M.; Tronin, I. V.

2016-09-01

We present the method and the results of the determination for optimal computational mesh parameters for axisymmetric modeling of flow and separation in the Iguasu gas centrifuge. The aim of this work was to determine the mesh parameters which provide relatively low computational cost whithout loss of accuracy. We use direct search optimization algorithm to calculate optimal mesh parameters. Obtained parameters were tested by the calculation of the optimal working regime of the Iguasu GC. Separative power calculated using the optimal mesh parameters differs less than 0.5% from the result obtained on the detailed mesh. Presented method can be used to determine optimal mesh parameters of the Iguasu GC with different rotor speeds.

A Four-parameter Budyko Equation for Mean Annual Water Balance

NASA Astrophysics Data System (ADS)

Tang, Y.; Wang, D.

2016-12-01

In this study, a four-parameter Budyko equation for long-term water balance at watershed scale is derived based on the proportionality relationships of the two-stage partitioning of precipitation. The four-parameter Budyko equation provides a practical solution to balance model simplicity and representation of dominated hydrologic processes. Under the four-parameter Budyko framework, the key hydrologic processes related to the lower bound of Budyko curve are determined, that is, the lower bound is corresponding to the situation when surface runoff and initial evaporation not competing with base flow generation are zero. The derived model is applied to 166 MOPEX watersheds in United States, and the dominant controlling factors on each parameter are determined. Then, four statistical models are proposed to predict the four model parameters based on the dominant controlling factors, e.g., saturated hydraulic conductivity, fraction of sand, time period between two storms, watershed slope, and Normalized Difference Vegetation Index. This study shows a potential application of the four-parameter Budyko equation to constrain land-surface parameterizations in ungauged watersheds or general circulation models.

Perceptual Calibration for Immersive Display Environments

PubMed Central

Ponto, Kevin; Gleicher, Michael; Radwin, Robert G.; Shin, Hyun Joon

2013-01-01

The perception of objects, depth, and distance has been repeatedly shown to be divergent between virtual and physical environments. We hypothesize that many of these discrepancies stem from incorrect geometric viewing parameters, specifically that physical measurements of eye position are insufficiently precise to provide proper viewing parameters. In this paper, we introduce a perceptual calibration procedure derived from geometric models. While most research has used geometric models to predict perceptual errors, we instead use these models inversely to determine perceptually correct viewing parameters. We study the advantages of these new psychophysically determined viewing parameters compared to the commonly used measured viewing parameters in an experiment with 20 subjects. The perceptually calibrated viewing parameters for the subjects generally produced new virtual eye positions that were wider and deeper than standard practices would estimate. Our study shows that perceptually calibrated viewing parameters can significantly improve depth acuity, distance estimation, and the perception of shape. PMID:23428454

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.

Updated determination of stress parameters for nine well-recorded earthquakes in eastern North America

USGS Publications Warehouse

Boore, David M.

2012-01-01

Stress parameters (Δσ) are determined for nine relatively well-recorded earthquakes in eastern North America for ten attenuation models. This is an update of a previous study by Boore et al. (2010). New to this paper are observations from the 2010 Val des Bois earthquake, additional observations for the 1988 Saguenay and 2005 Riviere du Loup earthquakes, and consideration of six attenuation models in addition to the four used in the previous study. As in that study, it is clear that Δσ depends strongly on the rate of geometrical spreading (as well as other model parameters). The observations necessary to determine conclusively which attenuation model best fits the data are still lacking. At this time, a simple 1/R model seems to give as good an overall fit to the data as more complex models.

Determining the Full Halo Coronal Mass Ejection Characteristics

NASA Astrophysics Data System (ADS)

Fainshtein, V. G.

2010-11-01

Observing halo coronal mass ejections (HCMEs) in the coronagraph field of view allows one to only determine the apparent parameters in the plane of the sky. Recently, several methods have been proposed allowing one to find some true geometrical and kinematical parameters of HCMEs. In most cases, a simple cone model was used to describe the CME shape. Observations show that various modifications of the cone model ("ice cream models") are most appropriate for describing the shapes of individual CMEs. This paper uses the method of determining full HCME parameters proposed by the author earlier, for determining the parameters of 45 full HCMEs, with various modifications of their shapes. I show that the determined CME characteristics depend significantly on the chosen CME shape. I conclude that the absence of criteria for a preliminary evaluation of the CME shape is a major source of error in determining the true parameters of a full HCME with any of the known methods. I show that, regardless of the chosen CME form, the trajectory of practically all the HCMEs in question deviate from the radial direction towards the Sun-Earth axis at the initial stage of their movement, and their angular size, on average, significantly exceeds that of all the observable CMEs.

The Effect of Nondeterministic Parameters on Shock-Associated Noise Prediction Modeling

NASA Technical Reports Server (NTRS)

Dahl, Milo D.; Khavaran, Abbas

2010-01-01

Engineering applications for aircraft noise prediction contain models for physical phenomenon that enable solutions to be computed quickly. These models contain parameters that have an uncertainty not accounted for in the solution. To include uncertainty in the solution, nondeterministic computational methods are applied. Using prediction models for supersonic jet broadband shock-associated noise, fixed model parameters are replaced by probability distributions to illustrate one of these methods. The results show the impact of using nondeterministic parameters both on estimating the model output uncertainty and on the model spectral level prediction. In addition, a global sensitivity analysis is used to determine the influence of the model parameters on the output, and to identify the parameters with the least influence on model output.

Analysis of regional rainfall-runoff parameters for the Lake Michigan Diversion hydrological modeling

USGS Publications Warehouse

Soong, David T.; Over, Thomas M.

2015-01-01

Recalibration of the HSPF parameters to the updated inputs and land covers was completed on two representative watershed models selected from the nine by using a manual method (HSPEXP) and an automatic method (PEST). The objective of the recalibration was to develop a regional parameter set that improves the accuracy in runoff volume prediction for the nine study watersheds. Knowledge about flow and watershed characteristics plays a vital role for validating the calibration in both manual and automatic methods. The best performing parameter set was determined by the automatic calibration method on a two-watershed model. Applying this newly determined parameter set to the nine watersheds for runoff volume simulation resulted in “very good” ratings in five watersheds, an improvement as compared to “very good” ratings achieved for three watersheds by the North Branch parameter set.

First evidence of non-locality in real band-gap metamaterials: determining parameters in the relaxed micromorphic model

PubMed Central

Barbagallo, Gabriele; d’Agostino, Marco Valerio; Placidi, Luca; Neff, Patrizio

2016-01-01

In this paper, we propose the first estimate of some elastic parameters of the relaxed micromorphic model on the basis of real experiments of transmission of longitudinal plane waves across an interface separating a classical Cauchy material (steel plate) and a phononic crystal (steel plate with fluid-filled holes). A procedure is set up in order to identify the parameters of the relaxed micromorphic model by superimposing the experimentally based profile of the reflection coefficient (plotted as function of the wave-frequency) with the analogous profile obtained via numerical simulations. We determine five out of six constitutive parameters which are featured by the relaxed micromorphic model in the isotropic case, plus the determination of the micro-inertia parameter. The sixth elastic parameter, namely the Cosserat couple modulus μc, still remains undetermined, since experiments on transverse incident waves are not yet available. A fundamental result of this paper is the estimate of the non-locality intrinsically associated with the underlying microstructure of the metamaterial. We show that the characteristic length Lc measuring the non-locality of the phononic crystal is of the order of 13 of the diameter of its fluid-filled holes. PMID:27436984

First evidence of non-locality in real band-gap metamaterials: determining parameters in the relaxed micromorphic model.

PubMed

Madeo, Angela; Barbagallo, Gabriele; d'Agostino, Marco Valerio; Placidi, Luca; Neff, Patrizio

2016-06-01

In this paper, we propose the first estimate of some elastic parameters of the relaxed micromorphic model on the basis of real experiments of transmission of longitudinal plane waves across an interface separating a classical Cauchy material (steel plate) and a phononic crystal (steel plate with fluid-filled holes). A procedure is set up in order to identify the parameters of the relaxed micromorphic model by superimposing the experimentally based profile of the reflection coefficient (plotted as function of the wave-frequency) with the analogous profile obtained via numerical simulations. We determine five out of six constitutive parameters which are featured by the relaxed micromorphic model in the isotropic case, plus the determination of the micro-inertia parameter. The sixth elastic parameter, namely the Cosserat couple modulus μ c , still remains undetermined, since experiments on transverse incident waves are not yet available. A fundamental result of this paper is the estimate of the non-locality intrinsically associated with the underlying microstructure of the metamaterial. We show that the characteristic length L c measuring the non-locality of the phononic crystal is of the order of [Formula: see text] of the diameter of its fluid-filled holes.

An Application of a Multidimensional Extension of the Two-Parameter Logistic Latent Trait Model.

ERIC Educational Resources Information Center

McKinley, Robert L.; Reckase, Mark D.

A latent trait model is described that is appropriate for use with tests that measure more than one dimension, and its application to both real and simulated test data is demonstrated. Procedures for estimating the parameters of the model are presented. The research objectives are to determine whether the two-parameter logistic model more…

Calibration of a biome-biogeochemical cycles model for modeling the net primary production of teak forests through inverse modeling of remotely sensed data

NASA Astrophysics Data System (ADS)

Imvitthaya, Chomchid; Honda, Kiyoshi; Lertlum, Surat; Tangtham, Nipon

2011-01-01

In this paper, we present the results of a net primary production (NPP) modeling of teak (Tectona grandis Lin F.), an important species in tropical deciduous forests. The biome-biogeochemical cycles or Biome-BGC model was calibrated to estimate net NPP through the inverse modeling approach. A genetic algorithm (GA) was linked with Biome-BGC to determine the optimal ecophysiological model parameters. The Biome-BGC was calibrated by adjusting the ecophysiological model parameters to fit the simulated LAI to the satellite LAI (SPOT-Vegetation), and the best fitness confirmed the high accuracy of generated ecophysioligical parameter from GA. The modeled NPP, using optimized parameters from GA as input data, was evaluated using daily NPP derived by the MODIS satellite and the annual field data in northern Thailand. The results showed that NPP obtained using the optimized ecophysiological parameters were more accurate than those obtained using default literature parameterization. This improvement occurred mainly because the model's optimized parameters reduced the bias by reducing systematic underestimation in the model. These Biome-BGC results can be effectively applied in teak forests in tropical areas. The study proposes a more effective method of using GA to determine ecophysiological parameters at the site level and represents a first step toward the analysis of the carbon budget of teak plantations at the regional scale.

Determination of HCME 3-D parameters using a full ice-cream cone model

NASA Astrophysics Data System (ADS)

Na, Hyeonock; Moon, Yong-Jae; Lee, Harim

2016-05-01

It is very essential to determine three dimensional parameters (e.g., radial speed, angular width, source location) of Coronal Mass Ejections (CMEs) for space weather forecast. Several cone models (e.g., an elliptical cone model, an ice-cream cone model, an asymmetric cone model) have been examined to estimate these parameters. In this study, we investigate which cone type is close to a halo CME morphology using 26 CMEs: halo CMEs by one spacecraft (SOHO or STEREO-A or B) and as limb CMEs by the other ones. From cone shape parameters of these CMEs such as their front curvature, we find that near full ice-cream cone type CMEs are much closer to observations than shallow ice-cream cone type CMEs. Thus we develop a new cone model in which a full ice-cream cone consists of many flat cones with different heights and angular widths. This model is carried out by the following steps: (1) construct a cone for given height and angular width, (2) project the cone onto the sky plane, (3) select points comprising the outer boundary, and (4) minimize the difference between the estimated projection speeds with the observed ones. By applying this model to 12 SOHO/LASCO halo CMEs, we find that 3-D parameters from our method are similar to those from other stereoscopic methods (a geometrical triangulation method and a Graduated Cylindrical Shell model) based on multi-spacecraft data. We are developing a general ice-cream cone model whose front shape is a free parameter determined by observations.

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.

The application of virtual prototyping methods to determine the dynamic parameters of mobile robot

NASA Astrophysics Data System (ADS)

Kurc, Krzysztof; Szybicki, Dariusz; Burghardt, Andrzej; Muszyńska, Magdalena

2016-04-01

The paper presents methods used to determine the parameters necessary to build a mathematical model of an underwater robot with a crawler drive. The parameters present in the dynamics equation will be determined by means of advanced mechatronic design tools, including: CAD/CAE software andMES modules. The virtual prototyping process is described as well as the various possible uses (design adaptability) depending on the optional accessories added to the vehicle. A mathematical model is presented to show the kinematics and dynamics of the underwater crawler robot, essential for the design stage.

An analytical-numerical approach for parameter determination of a five-parameter single-diode model of photovoltaic cells and modules

NASA Astrophysics Data System (ADS)

Hejri, Mohammad; Mokhtari, Hossein; Azizian, Mohammad Reza; Söder, Lennart

2016-04-01

Parameter extraction of the five-parameter single-diode model of solar cells and modules from experimental data is a challenging problem. These parameters are evaluated from a set of nonlinear equations that cannot be solved analytically. On the other hand, a numerical solution of such equations needs a suitable initial guess to converge to a solution. This paper presents a new set of approximate analytical solutions for the parameters of a five-parameter single-diode model of photovoltaic (PV) cells and modules. The proposed solutions provide a good initial point which guarantees numerical analysis convergence. The proposed technique needs only a few data from the PV current-voltage characteristics, i.e. open circuit voltage Voc, short circuit current Isc and maximum power point current and voltage Im; Vm making it a fast and low cost parameter determination technique. The accuracy of the presented theoretical I-V curves is verified by experimental data.

Generalized Processing Tree Models: Jointly Modeling Discrete and Continuous Variables.

PubMed

Heck, Daniel W; Erdfelder, Edgar; Kieslich, Pascal J

2018-05-24

Multinomial processing tree models assume that discrete cognitive states determine observed response frequencies. Generalized processing tree (GPT) models extend this conceptual framework to continuous variables such as response times, process-tracing measures, or neurophysiological variables. GPT models assume finite-mixture distributions, with weights determined by a processing tree structure, and continuous components modeled by parameterized distributions such as Gaussians with separate or shared parameters across states. We discuss identifiability, parameter estimation, model testing, a modeling syntax, and the improved precision of GPT estimates. Finally, a GPT version of the feature comparison model of semantic categorization is applied to computer-mouse trajectories.

Toward On-line Parameter Estimation of Concentric Tube Robots Using a Mechanics-based Kinematic Model

PubMed Central

Jang, Cheongjae; Ha, Junhyoung; Dupont, Pierre E.; Park, Frank Chongwoo

2017-01-01

Although existing mechanics-based models of concentric tube robots have been experimentally demonstrated to approximate the actual kinematics, determining accurate estimates of model parameters remains difficult due to the complex relationship between the parameters and available measurements. Further, because the mechanics-based models neglect some phenomena like friction, nonlinear elasticity, and cross section deformation, it is also not clear if model error is due to model simplification or to parameter estimation errors. The parameters of the superelastic materials used in these robots can be slowly time-varying, necessitating periodic re-estimation. This paper proposes a method for estimating the mechanics-based model parameters using an extended Kalman filter as a step toward on-line parameter estimation. Our methodology is validated through both simulation and experiments. PMID:28717554

Sorption testing and generalized composite surface complexation models for determining uranium sorption parameters at a proposed in-situ recovery site

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

Johnson, Raymond H.; Truax, Ryan A.; Lankford, David A.

Solid-phase iron concentrations and generalized composite surface complexation models were used to evaluate procedures in determining uranium sorption on oxidized aquifer material at a proposed U in situ recovery (ISR) site. At the proposed Dewey Burdock ISR site in South Dakota, USA, oxidized aquifer material occurs downgradient of the U ore zones. Solid-phase Fe concentrations did not explain our batch sorption test results,though total extracted Fe appeared to be positively correlated with overall measured U sorption. Batch sorption test results were used to develop generalized composite surface complexation models that incorporated the full genericsorption potential of each sample, without detailedmore » mineralogiccharacterization. The resultant models provide U sorption parameters (site densities and equilibrium constants) for reactive transport modeling. The generalized composite surface complexation sorption models were calibrated to batch sorption data from three oxidized core samples using inverse modeling, and gave larger sorption parameters than just U sorption on the measured solidphase Fe. These larger sorption parameters can significantly influence reactive transport modeling, potentially increasing U attenuation. Because of the limited number of calibration points, inverse modeling required the reduction of estimated parameters by fixing two parameters. The best-fit models used fixed values for equilibrium constants, with the sorption site densities being estimated by the inversion process. While these inverse routines did provide best-fit sorption parameters, local minima and correlated parameters might require further evaluation. Despite our limited number of proxy samples, the procedures presented provide a valuable methodology to consider for sites where metal sorption parameters are required. Furthermore, these sorption parameters can be used in reactive transport modeling to assess downgradient metal attenuation, especially when no other calibration data are available, such as at proposed U ISR sites.« less

Sorption testing and generalized composite surface complexation models for determining uranium sorption parameters at a proposed in-situ recovery site

DOE PAGES

Johnson, Raymond H.; Truax, Ryan A.; Lankford, David A.; ...

2016-02-03

Solid-phase iron concentrations and generalized composite surface complexation models were used to evaluate procedures in determining uranium sorption on oxidized aquifer material at a proposed U in situ recovery (ISR) site. At the proposed Dewey Burdock ISR site in South Dakota, USA, oxidized aquifer material occurs downgradient of the U ore zones. Solid-phase Fe concentrations did not explain our batch sorption test results,though total extracted Fe appeared to be positively correlated with overall measured U sorption. Batch sorption test results were used to develop generalized composite surface complexation models that incorporated the full genericsorption potential of each sample, without detailedmore » mineralogiccharacterization. The resultant models provide U sorption parameters (site densities and equilibrium constants) for reactive transport modeling. The generalized composite surface complexation sorption models were calibrated to batch sorption data from three oxidized core samples using inverse modeling, and gave larger sorption parameters than just U sorption on the measured solidphase Fe. These larger sorption parameters can significantly influence reactive transport modeling, potentially increasing U attenuation. Because of the limited number of calibration points, inverse modeling required the reduction of estimated parameters by fixing two parameters. The best-fit models used fixed values for equilibrium constants, with the sorption site densities being estimated by the inversion process. While these inverse routines did provide best-fit sorption parameters, local minima and correlated parameters might require further evaluation. Despite our limited number of proxy samples, the procedures presented provide a valuable methodology to consider for sites where metal sorption parameters are required. Furthermore, these sorption parameters can be used in reactive transport modeling to assess downgradient metal attenuation, especially when no other calibration data are available, such as at proposed U ISR sites.« less

Estimating winter wheat phenological parameters: Implications for crop modeling

USDA-ARS?s Scientific Manuscript database

Crop parameters, such as the timing of developmental events, are critical for accurate simulation results in crop simulation models, yet uncertainty often exists in determining the parameters. Factors contributing to the uncertainty include: a) sources of variation within a plant (i.e., within diffe...

Derivation of WECC Distributed PV System Model Parameters from Quasi-Static Time-Series Distribution System Simulations

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

Mather, Barry A; Boemer, Jens C.; Vittal, Eknath

The response of low voltage networks with high penetration of PV systems to transmission network faults will, in the future, determine the overall power system performance during certain hours of the year. The WECC distributed PV system model (PVD1) is designed to represent small-scale distribution-connected systems. Although default values are provided by WECC for the model parameters, tuning of those parameters seems to become important in order to accurately estimate the partial loss of distributed PV systems for bulk system studies. The objective of this paper is to describe a new methodology to determine the WECC distributed PV system (PVD1)more » model parameters and to derive parameter sets obtained for six distribution circuits of a Californian investor-owned utility with large amounts of distributed PV systems. The results indicate that the parameters for the partial loss of distributed PV systems may differ significantly from the default values provided by WECC.« less

Determining dynamical parameters of the Milky Way Galaxy based on high-accuracy radio astrometry

NASA Astrophysics Data System (ADS)

Honma, Mareki; Nagayama, Takumi; Sakai, Nobuyuki

2015-08-01

In this paper we evaluate how the dynamical structure of the Galaxy can be constrained by high-accuracy VLBI (Very Long Baseline Interferometry) astrometry such as VERA (VLBI Exploration of Radio Astrometry). We generate simulated samples of maser sources which follow the gas motion caused by a spiral or bar potential, with their distribution similar to those currently observed with VERA and VLBA (Very Long Baseline Array). We apply the Markov chain Monte Carlo analyses to the simulated sample sources to determine the dynamical parameter of the models. We show that one can successfully determine the initial model parameters if astrometric results are obtained for a few hundred sources with currently achieved astrometric accuracy. If astrometric data are available from 500 sources, the expected accuracy of R0 and Θ0 is ˜ 1% or higher, and parameters related to the spiral structure can be constrained by an error of 10% or with higher accuracy. We also show that the parameter determination accuracy is basically independent of the locations of resonances such as corotation and/or inner/outer Lindblad resonances. We also discuss the possibility of model selection based on the Bayesian information criterion (BIC), and demonstrate that BIC can be used to discriminate different dynamical models of the Galaxy.

On the biosorption, by brown seaweed, Lobophora variegata, of Ni(II) from aqueous solutions: equilibrium and thermodynamic studies.

PubMed

Basha, Shaik; Jaiswar, Santlal; Jha, Bhavanath

2010-09-01

The biosorption equilibrium isotherms of Ni(II) onto marine brown algae Lobophora variegata, which was chemically-modified by CaCl(2) were studied and modeled. To predict the biosorption isotherms and to determine the characteristic parameters for process design, twenty-three one-, two-, three-, four- and five-parameter isotherm models were applied to experimental data. The interaction among biosorbed molecules is attractive and biosorption is carried out on energetically different sites and is an endothermic process. The five-parameter Fritz-Schluender model gives the most accurate fit with high regression coefficient, R (2) (0.9911-0.9975) and F-ratio (118.03-179.96), and low standard error, SE (0.0902-0.0.1556) and the residual or sum of square error, SSE (0.0012-0.1789) values to all experimental data in comparison to other models. The biosorption isotherm models fitted the experimental data in the order: Fritz-Schluender (five-parameter) > Freundlich (two-parameter) > Langmuir (two-parameter) > Khan (three-parameter) > Fritz-Schluender (four-parameter). The thermodynamic parameters such as DeltaG (0), DeltaH (0) and DeltaS (0) have been determined, which indicates the sorption of Ni(II) onto L. variegata was spontaneous and endothermic in nature.

Four-parameter model for polarization-resolved rough-surface BRDF.

PubMed

Renhorn, Ingmar G E; Hallberg, Tomas; Bergström, David; Boreman, Glenn D

2011-01-17

A modeling procedure is demonstrated, which allows representation of polarization-resolved BRDF data using only four parameters: the real and imaginary parts of an effective refractive index with an added parameter taking grazing incidence absorption into account and an angular-scattering parameter determined from the BRDF measurement of a chosen angle of incidence, preferably close to normal incidence. These parameters allow accurate predictions of s- and p-polarized BRDF for a painted rough surface, over three decades of variation in BRDF magnitude. To characterize any particular surface of interest, the measurements required to determine these four parameters are the directional hemispherical reflectance (DHR) for s- and p-polarized input radiation and the BRDF at a selected angle of incidence. The DHR data describes the angular and polarization dependence, as well as providing the overall normalization constraint. The resulting model conserves energy and fulfills the reciprocity criteria.

Study of Parameters And Methods of LL-Ⅳ Distributed Hydrological Model in DMIP2

NASA Astrophysics Data System (ADS)

Li, L.; Wu, J.; Wang, X.; Yang, C.; Zhao, Y.; Zhou, H.

2008-05-01

: The Physics-based distributed hydrological model is considered as an important developing period from the traditional experience-hydrology to the physical hydrology. The Hydrology Laboratory of the NOAA National Weather Service proposes the first and second phase of the Distributed Model Intercomparison Project (DMIP)，that it is a great epoch-making work. LL distributed hydrological model has been developed to the fourth generation since it was established in 1997 on the Fengman-I district reservoir area (11000 km2).The LL-I distributed hydrological model was born with the applications of flood control system in the Fengman-I in China. LL-II was developed under the DMIP-I support, it is combined with GIS, RS, GPS, radar rainfall measurement.LL-III was established along with Applications of LL Distributed Model on Water Resources which was supported by the 973-projects of The Ministry of Science and Technology of the People's Republic of China. LL-Ⅳ was developed to face China's water problem. Combined with Blue River and the Baron Fork River basin of DMIP-II, the convection-diffusion equation of non-saturated and saturated seepage was derived from the soil water dynamics and continuous equation. In view of the technical characteristics of the model, the advantage of using convection-diffusion equation to compute confluence overall is longer period of predictable, saving memory space, fast budgeting, clear physical concepts, etc. The determination of parameters of hydrological model is the key, including experience coefficients and parameters of physical parameters. There are methods of experience, inversion, and the optimization to determine the model parameters, and each has advantages and disadvantages. This paper briefly introduces the LL-Ⅳ distribution hydrological model equations, and particularly introduces methods of parameters determination and simulation results on Blue River and Baron Fork River basin for DMIP-II. The soil moisture diffusion coefficient and coefficient of hydraulic conductivity are involved all through the LL-Ⅳ distribution of runoff and slope convergence model, used mainly empirical formula to determine. It's used optimization methods to calculate the two parameters of evaporation capacity (coefficient of bare land and vegetation land), two parameters of interception and wave velocity of Overland Flow, interflow and groundwater. The approach of determining wave velocity of River Network confluence and diffusion coefficient is: 1. Estimate roughness based mainly on digital information such as land use, soil texture, etc. 2.Establish the empirical formula. Another method is called convection-diffusion numerical inversion.

Improving RNA nearest neighbor parameters for helices by going beyond the two-state model.

PubMed

Spasic, Aleksandar; Berger, Kyle D; Chen, Jonathan L; Seetin, Matthew G; Turner, Douglas H; Mathews, David H

2018-06-01

RNA folding free energy change nearest neighbor parameters are widely used to predict folding stabilities of secondary structures. They were determined by linear regression to datasets of optical melting experiments on small model systems. Traditionally, the optical melting experiments are analyzed assuming a two-state model, i.e. a structure is either complete or denatured. Experimental evidence, however, shows that structures exist in an ensemble of conformations. Partition functions calculated with existing nearest neighbor parameters predict that secondary structures can be partially denatured, which also directly conflicts with the two-state model. Here, a new approach for determining RNA nearest neighbor parameters is presented. Available optical melting data for 34 Watson-Crick helices were fit directly to a partition function model that allows an ensemble of conformations. Fitting parameters were the enthalpy and entropy changes for helix initiation, terminal AU pairs, stacks of Watson-Crick pairs and disordered internal loops. The resulting set of nearest neighbor parameters shows a 38.5% improvement in the sum of residuals in fitting the experimental melting curves compared to the current literature set.

The Supernovae Analysis Application (SNAP)

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

Bayless, Amanda J.; Fryer, Christopher Lee; Wollaeger, Ryan Thomas

The SuperNovae Analysis aPplication (SNAP) is a new tool for the analysis of SN observations and validation of SN models. SNAP consists of a publicly available relational database with observational light curve, theoretical light curve, and correlation table sets with statistical comparison software, and a web interface available to the community. The theoretical models are intended to span a gridded range of parameter space. The goal is to have users upload new SN models or new SN observations and run the comparison software to determine correlations via the website. There are problems looming on the horizon that SNAP is beginningmore » to solve. For example, large surveys will discover thousands of SNe annually. Frequently, the parameter space of a new SN event is unbounded. SNAP will be a resource to constrain parameters and determine if an event needs follow-up without spending resources to create new light curve models from scratch. Second, there is no rapidly available, systematic way to determine degeneracies between parameters, or even what physics is needed to model a realistic SN. The correlations made within the SNAP system are beginning to solve these problems.« less

The Supernovae Analysis Application (SNAP)

DOE PAGES

Bayless, Amanda J.; Fryer, Christopher Lee; Wollaeger, Ryan Thomas; ...

2017-09-06

The SuperNovae Analysis aPplication (SNAP) is a new tool for the analysis of SN observations and validation of SN models. SNAP consists of a publicly available relational database with observational light curve, theoretical light curve, and correlation table sets with statistical comparison software, and a web interface available to the community. The theoretical models are intended to span a gridded range of parameter space. The goal is to have users upload new SN models or new SN observations and run the comparison software to determine correlations via the website. There are problems looming on the horizon that SNAP is beginningmore » to solve. For example, large surveys will discover thousands of SNe annually. Frequently, the parameter space of a new SN event is unbounded. SNAP will be a resource to constrain parameters and determine if an event needs follow-up without spending resources to create new light curve models from scratch. Second, there is no rapidly available, systematic way to determine degeneracies between parameters, or even what physics is needed to model a realistic SN. The correlations made within the SNAP system are beginning to solve these problems.« less

The Supernovae Analysis Application (SNAP)

NASA Astrophysics Data System (ADS)

Bayless, Amanda J.; Fryer, Chris L.; Wollaeger, Ryan; Wiggins, Brandon; Even, Wesley; de la Rosa, Janie; Roming, Peter W. A.; Frey, Lucy; Young, Patrick A.; Thorpe, Rob; Powell, Luke; Landers, Rachel; Persson, Heather D.; Hay, Rebecca

2017-09-01

The SuperNovae Analysis aPplication (SNAP) is a new tool for the analysis of SN observations and validation of SN models. SNAP consists of a publicly available relational database with observational light curve, theoretical light curve, and correlation table sets with statistical comparison software, and a web interface available to the community. The theoretical models are intended to span a gridded range of parameter space. The goal is to have users upload new SN models or new SN observations and run the comparison software to determine correlations via the website. There are problems looming on the horizon that SNAP is beginning to solve. For example, large surveys will discover thousands of SNe annually. Frequently, the parameter space of a new SN event is unbounded. SNAP will be a resource to constrain parameters and determine if an event needs follow-up without spending resources to create new light curve models from scratch. Second, there is no rapidly available, systematic way to determine degeneracies between parameters, or even what physics is needed to model a realistic SN. The correlations made within the SNAP system are beginning to solve these problems.

Uncertainty Quantification in Simulations of Epidemics Using Polynomial Chaos

PubMed Central

Santonja, F.; Chen-Charpentier, B.

2012-01-01

Mathematical models based on ordinary differential equations are a useful tool to study the processes involved in epidemiology. Many models consider that the parameters are deterministic variables. But in practice, the transmission parameters present large variability and it is not possible to determine them exactly, and it is necessary to introduce randomness. In this paper, we present an application of the polynomial chaos approach to epidemiological mathematical models based on ordinary differential equations with random coefficients. Taking into account the variability of the transmission parameters of the model, this approach allows us to obtain an auxiliary system of differential equations, which is then integrated numerically to obtain the first-and the second-order moments of the output stochastic processes. A sensitivity analysis based on the polynomial chaos approach is also performed to determine which parameters have the greatest influence on the results. As an example, we will apply the approach to an obesity epidemic model. PMID:22927889

Prediction of biomechanical parameters of the proximal femur using statistical appearance models and support vector regression.

PubMed

Fritscher, Karl; Schuler, Benedikt; Link, Thomas; Eckstein, Felix; Suhm, Norbert; Hänni, Markus; Hengg, Clemens; Schubert, Rainer

2008-01-01

Fractures of the proximal femur are one of the principal causes of mortality among elderly persons. Traditional methods for the determination of femoral fracture risk use methods for measuring bone mineral density. However, BMD alone is not sufficient to predict bone failure load for an individual patient and additional parameters have to be determined for this purpose. In this work an approach that uses statistical models of appearance to identify relevant regions and parameters for the prediction of biomechanical properties of the proximal femur will be presented. By using Support Vector Regression the proposed model based approach is capable of predicting two different biomechanical parameters accurately and fully automatically in two different testing scenarios.

Implicit assimilation for marine ecological models

NASA Astrophysics Data System (ADS)

Weir, B.; Miller, R.; Spitz, Y. H.

2012-12-01

We use a new data assimilation method to estimate the parameters of a marine ecological model. At a given point in the ocean, the estimated values of the parameters determine the behaviors of the modeled planktonic groups, and thus indicate which species are dominant. To begin, we assimilate in situ observations, e.g., the Bermuda Atlantic Time-series Study, the Hawaii Ocean Time-series, and Ocean Weather Station Papa. From there, we estimate the parameters at surrounding points in space based on satellite observations of ocean color. Given the variation of the estimated parameters, we divide the ocean into regions meant to represent distinct ecosystems. An important feature of the data assimilation approach is that it refines the confidence limits of the optimal Gaussian approximation to the distribution of the parameters. This enables us to determine the ecological divisions with greater accuracy.

Mathematical modeling of a thermovoltaic cell

NASA Technical Reports Server (NTRS)

White, Ralph E.; Kawanami, Makoto

1992-01-01

A new type of battery named 'Vaporvolt' cell is in the early stage of its development. A mathematical model of a CuO/Cu 'Vaporvolt' cell is presented that can be used to predict the potential and the transport behavior of the cell during discharge. A sensitivity analysis of the various transport and electrokinetic parameters indicates which parameters have the most influence on the predicted energy and power density of the 'Vaporvolt' cell. This information can be used to decide which parameters should be optimized or determined more accurately through further modeling or experimental studies. The optimal thicknesses of electrodes and separator, the concentration of the electrolyte, and the current density are determined by maximizing the power density. These parameter sensitivities and optimal design parameter values will help in the development of a better CuO/Cu 'Vaporvolt' cell.

DRAINMOD-GIS: a lumped parameter watershed scale drainage and water quality model

Treesearch

G.P. Fernandez; G.M. Chescheir; R.W. Skaggs; D.M. Amatya

2006-01-01

A watershed scale lumped parameter hydrology and water quality model that includes an uncertainty analysis component was developed and tested on a lower coastal plain watershed in North Carolina. Uncertainty analysis was used to determine the impacts of uncertainty in field and network parameters of the model on the predicted outflows and nitrate-nitrogen loads at the...

Validation and upgrading of physically based mathematical models

NASA Technical Reports Server (NTRS)

Duval, Ronald

1992-01-01

The validation of the results of physically-based mathematical models against experimental results was discussed. Systematic techniques are used for: (1) isolating subsets of the simulator mathematical model and comparing the response of each subset to its experimental response for the same input conditions; (2) evaluating the response error to determine whether it is the result of incorrect parameter values, incorrect structure of the model subset, or unmodeled external effects of cross coupling; and (3) modifying and upgrading the model and its parameter values to determine the most physically appropriate combination of changes.

Applications of Monte Carlo method to nonlinear regression of rheological data

NASA Astrophysics Data System (ADS)

Kim, Sangmo; Lee, Junghaeng; Kim, Sihyun; Cho, Kwang Soo

2018-02-01

In rheological study, it is often to determine the parameters of rheological models from experimental data. Since both rheological data and values of the parameters vary in logarithmic scale and the number of the parameters is quite large, conventional method of nonlinear regression such as Levenberg-Marquardt (LM) method is usually ineffective. The gradient-based method such as LM is apt to be caught in local minima which give unphysical values of the parameters whenever the initial guess of the parameters is far from the global optimum. Although this problem could be solved by simulated annealing (SA), the Monte Carlo (MC) method needs adjustable parameter which could be determined in ad hoc manner. We suggest a simplified version of SA, a kind of MC methods which results in effective values of the parameters of most complicated rheological models such as the Carreau-Yasuda model of steady shear viscosity, discrete relaxation spectrum and zero-shear viscosity as a function of concentration and molecular weight.

Prediction of compressibility parameters of the soils using artificial neural network.

PubMed

Kurnaz, T Fikret; Dagdeviren, Ugur; Yildiz, Murat; Ozkan, Ozhan

2016-01-01

The compression index and recompression index are one of the important compressibility parameters to determine the settlement calculation for fine-grained soil layers. These parameters can be determined by carrying out laboratory oedometer test on undisturbed samples; however, the test is quite time-consuming and expensive. Therefore, many empirical formulas based on regression analysis have been presented to estimate the compressibility parameters using soil index properties. In this paper, an artificial neural network (ANN) model is suggested for prediction of compressibility parameters from basic soil properties. For this purpose, the input parameters are selected as the natural water content, initial void ratio, liquid limit and plasticity index. In this model, two output parameters, including compression index and recompression index, are predicted in a combined network structure. As the result of the study, proposed ANN model is successful for the prediction of the compression index, however the predicted recompression index values are not satisfying compared to the compression index.

Determining the accuracy of maximum likelihood parameter estimates with colored residuals

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.; Klein, Vladislav

1994-01-01

An important part of building high fidelity mathematical models based on measured data is calculating the accuracy associated with statistical estimates of the model parameters. Indeed, without some idea of the accuracy of parameter estimates, the estimates themselves have limited value. In this work, an expression based on theoretical analysis was developed to properly compute parameter accuracy measures for maximum likelihood estimates with colored residuals. This result is important because experience from the analysis of measured data reveals that the residuals from maximum likelihood estimation are almost always colored. The calculations involved can be appended to conventional maximum likelihood estimation algorithms. Simulated data runs were used to show that the parameter accuracy measures computed with this technique accurately reflect the quality of the parameter estimates from maximum likelihood estimation without the need for analysis of the output residuals in the frequency domain or heuristically determined multiplication factors. The result is general, although the application studied here is maximum likelihood estimation of aerodynamic model parameters from flight test data.

A generalized methodology to characterize composite materials for pyrolysis models

NASA Astrophysics Data System (ADS)

McKinnon, Mark B.

The predictive capabilities of computational fire models have improved in recent years such that models have become an integral part of many research efforts. Models improve the understanding of the fire risk of materials and may decrease the number of expensive experiments required to assess the fire hazard of a specific material or designed space. A critical component of a predictive fire model is the pyrolysis sub-model that provides a mathematical representation of the rate of gaseous fuel production from condensed phase fuels given a heat flux incident to the material surface. The modern, comprehensive pyrolysis sub-models that are common today require the definition of many model parameters to accurately represent the physical description of materials that are ubiquitous in the built environment. Coupled with the increase in the number of parameters required to accurately represent the pyrolysis of materials is the increasing prevalence in the built environment of engineered composite materials that have never been measured or modeled. The motivation behind this project is to develop a systematic, generalized methodology to determine the requisite parameters to generate pyrolysis models with predictive capabilities for layered composite materials that are common in industrial and commercial applications. This methodology has been applied to four common composites in this work that exhibit a range of material structures and component materials. The methodology utilizes a multi-scale experimental approach in which each test is designed to isolate and determine a specific subset of the parameters required to define a material in the model. Data collected in simultaneous thermogravimetry and differential scanning calorimetry experiments were analyzed to determine the reaction kinetics, thermodynamic properties, and energetics of decomposition for each component of the composite. Data collected in microscale combustion calorimetry experiments were analyzed to determine the heats of complete combustion of the volatiles produced in each reaction. Inverse analyses were conducted on sample temperature data collected in bench-scale tests to determine the thermal transport parameters of each component through degradation. Simulations of quasi-one-dimensional bench-scale gasification tests generated from the resultant models using the ThermaKin modeling environment were compared to experimental data to independently validate the models.

Development of an in-vitro circulatory system with known resistance and capacitance

NASA Technical Reports Server (NTRS)

Offerdahl, C. D.; Schaub, J. D.; Koenig, S. C.; Swope, R. D.; Ewert, D. L.; Convertino, V. A. (Principal Investigator)

1996-01-01

An in-vitro (hydrodynamic) model of the circulatory system was developed. The model consisted of a pump, compliant tubing, and valves for resistance. The model is used to simulate aortic pressure and flow. These parameters were measured using a Konigsburg Pressure transducer and a Triton ART2 flow probe. In addition, venous pressure and flow were measured on the downstream side of the resistance. The system has a known compliance and resistance. Steady and pulsatile flow tests were conducted to determine the resistance of the model. A static compliance test was used to determine the compliance of the system. The aortic pressure and flow obtained from the hydrodynamic model will be used to test the accuracy of parameter estimation models such as the 2-element and 4-element Windkessel models and the 3-element Westkessel model. Verifying analytical models used in determining total peripheral resistance (TPR) and systemic arterial compliance (SAC) is important because it provides insight into hemodynamic parameters that indicate baroreceptor responsiveness to situations such as changes in gravitational acceleration.

On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models part 2. Parameter and state estimation

NASA Astrophysics Data System (ADS)

Fleischer, Christian; Waag, Wladislaw; Heyn, Hans-Martin; Sauer, Dirk Uwe

2014-09-01

Lithium-ion battery systems employed in high power demanding systems such as electric vehicles require a sophisticated monitoring system to ensure safe and reliable operation. Three major states of the battery are of special interest and need to be constantly monitored. These include: battery state of charge (SoC), battery state of health (capacity fade determination, SoH), and state of function (power fade determination, SoF). The second paper concludes the series by presenting a multi-stage online parameter identification technique based on a weighted recursive least quadratic squares parameter estimator to determine the parameters of the proposed battery model from the first paper during operation. A novel mutation based algorithm is developed to determine the nonlinear current dependency of the charge-transfer resistance. The influence of diffusion is determined by an on-line identification technique and verified on several batteries at different operation conditions. This method guarantees a short response time and, together with its fully recursive structure, assures a long-term stable monitoring of the battery parameters. The relative dynamic voltage prediction error of the algorithm is reduced to 2%. The changes of parameters are used to determine the states of the battery. The algorithm is real-time capable and can be implemented on embedded systems.

Dependence of subject-specific parameters for a fast helical CT respiratory motion model on breathing rate: an animal study

NASA Astrophysics Data System (ADS)

O'Connell, Dylan; Thomas, David H.; Lamb, James M.; Lewis, John H.; Dou, Tai; Sieren, Jered P.; Saylor, Melissa; Hofmann, Christian; Hoffman, Eric A.; Lee, Percy P.; Low, Daniel A.

2018-02-01

To determine if the parameters relating lung tissue displacement to a breathing surrogate signal in a previously published respiratory motion model vary with the rate of breathing during image acquisition. An anesthetized pig was imaged using multiple fast helical scans to sample the breathing cycle with simultaneous surrogate monitoring. Three datasets were collected while the animal was mechanically ventilated with different respiratory rates: 12 bpm (breaths per minute), 17 bpm, and 24 bpm. Three sets of motion model parameters describing the correspondences between surrogate signals and tissue displacements were determined. The model error was calculated individually for each dataset, as well asfor pairs of parameters and surrogate signals from different experiments. The values of one model parameter, a vector field denoted α which related tissue displacement to surrogate amplitude, determined for each experiment were compared. The mean model error of the three datasets was 1.00  ±  0.36 mm with a 95th percentile value of 1.69 mm. The mean error computed from all combinations of parameters and surrogate signals from different datasets was 1.14  ±  0.42 mm with a 95th percentile of 1.95 mm. The mean difference in α over all pairs of experiments was 4.7%  ±  5.4%, and the 95th percentile was 16.8%. The mean angle between pairs of α was 5.0  ±  4.0 degrees, with a 95th percentile of 13.2 mm. The motion model parameters were largely unaffected by changes in the breathing rate during image acquisition. The mean error associated with mismatched sets of parameters and surrogate signals was 0.14 mm greater than the error achieved when using parameters and surrogate signals acquired with the same breathing rate, while maximum respiratory motion was 23.23 mm on average.

Description of the Hexadecapole Deformation Parameter in the sdg Interacting Boson Model

NASA Astrophysics Data System (ADS)

Liu, Yu-xin; Sun, Di; Wang, Jia-jun; Han, Qi-zhi

1998-04-01

The hexadecapole deformation parameter β4 of the rare-earth and actinide nuclei is investigated in the framework of the sdg interacing boson model. An explicit relation between the geometric hexadecapole deformation parameter β4 and the intrinsic deformation parameters epsilon4, epsilon2 are obtained. The deformation parameters β4 of the rare-earths and actinides are determined without any free parameter. The calculated results agree with experimental data well. It also shows that the SU(5) limit of the sdg interacting boson model can describe the β4 systematics as well as the SU(3) limit.

Estimation of nonlinear pilot model parameters including time delay.

NASA Technical Reports Server (NTRS)

Schiess, J. R.; Roland, V. R.; Wells, W. R.

1972-01-01

Investigation of the feasibility of using a Kalman filter estimator for the identification of unknown parameters in nonlinear dynamic systems with a time delay. The problem considered is the application of estimation theory to determine the parameters of a family of pilot models containing delayed states. In particular, the pilot-plant dynamics are described by differential-difference equations of the retarded type. The pilot delay, included as one of the unknown parameters to be determined, is kept in pure form as opposed to the Pade approximations generally used for these systems. Problem areas associated with processing real pilot response data are included in the discussion.

Design of Experiments for the Thermal Characterization of Metallic Foam

NASA Technical Reports Server (NTRS)

Crittenden, Paul E.; Cole, Kevin D.

2003-01-01

Metallic foams are being investigated for possible use in the thermal protection systems of reusable launch vehicles. As a result, the performance of these materials needs to be characterized over a wide range of temperatures and pressures. In this paper a radiation/conduction model is presented for heat transfer in metallic foams. Candidates for the optimal transient experiment to determine the intrinsic properties of the model are found by two methods. First, an optimality criterion is used to find an experiment to find all of the parameters using one heating event. Second, a pair of heating events is used to determine the parameters in which one heating event is optimal for finding the parameters related to conduction, while the other heating event is optimal for finding the parameters associated with radiation. Simulated data containing random noise was analyzed to determine the parameters using both methods. In all cases the parameter estimates could be improved by analyzing a larger data record than suggested by the optimality criterion.

Techniques for evaluating optimum data center operation

DOEpatents

Hamann, Hendrik F.; Rodriguez, Sergio Adolfo Bermudez; Wehle, Hans-Dieter

2017-06-14

Techniques for modeling a data center are provided. In one aspect, a method for determining data center efficiency is provided. The method includes the following steps. Target parameters for the data center are obtained. Technology pre-requisite parameters for the data center are obtained. An optimum data center efficiency is determined given the target parameters for the data center and the technology pre-requisite parameters for the data center.

Process Parameter Optimization for Wobbling Laser Spot Welding of Ti6Al4V Alloy

NASA Astrophysics Data System (ADS)

Vakili-Farahani, F.; Lungershausen, J.; Wasmer, K.

Laser beam welding (LBW) coupled with "wobble effect" (fast oscillation of the laser beam) is very promising for high precision micro-joining industry. For this process, similarly to the conventional LBW, the laser welding process parameters play a very significant role in determining the quality of a weld joint. Consequently, four process parameters (laser power, wobble frequency, number of rotations within a single laser pulse and focused position) and 5 responses (penetration, width, heat affected zone (HAZ), area of the fusion zone, area of HAZ and hardness) were investigated for spot welding of Ti6Al4V alloy (grade 5) using a design of experiments (DoE) approach. This paper presents experimental results showing the effects of variating the considered most important process parameters on the spot weld quality of Ti6Al4V alloy. Semi-empirical mathematical models were developed to correlate laser welding parameters to each of the measured weld responses. Adequacies of the models were then examined by various methods such as ANOVA. These models not only allows a better understanding of the wobble laser welding process and predict the process performance but also determines optimal process parameters. Therefore, optimal combination of process parameters was determined considering certain quality criteria set.

Modelling of industrial robot in LabView Robotics

NASA Astrophysics Data System (ADS)

Banas, W.; Cwikła, G.; Foit, K.; Gwiazda, A.; Monica, Z.; Sekala, A.

2017-08-01

Currently can find many models of industrial systems including robots. These models differ from each other not only by the accuracy representation parameters, but the representation range. For example, CAD models describe the geometry of the robot and some even designate a mass parameters as mass, center of gravity, moment of inertia, etc. These models are used in the design of robotic lines and sockets. Also systems for off-line programming use these models and many of them can be exported to CAD. It is important to note that models for off-line programming describe not only the geometry but contain the information necessary to create a program for the robot. Exports from CAD to off-line programming system requires additional information. These models are used for static determination of reachability points, and testing collision. It’s enough to generate a program for the robot, and even check the interaction of elements of the production line, or robotic cell. Mathematical models allow robots to study the properties of kinematic and dynamic of robot movement. In these models the geometry is not so important, so are used only selected parameters such as the length of the robot arm, the center of gravity, moment of inertia. These parameters are introduced into the equations of motion of the robot and motion parameters are determined.

Displacement back analysis for a high slope of the Dagangshan Hydroelectric Power Station based on BP neural network and particle swarm optimization.

PubMed

Liang, Zhengzhao; Gong, Bin; Tang, Chunan; Zhang, Yongbin; Ma, Tianhui

2014-01-01

The right bank high slope of the Dagangshan Hydroelectric Power Station is located in complicated geological conditions with deep fractures and unloading cracks. How to obtain the mechanical parameters and then evaluate the safety of the slope are the key problems. This paper presented a displacement back analysis for the slope using an artificial neural network model (ANN) and particle swarm optimization model (PSO). A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model. The backpropagation ANN model was used to establish a mapping function between the mechanical parameters and the monitoring displacements. The PSO model was applied to initialize the weights and thresholds of the backpropagation (BP) network model and determine suitable values of the mechanical parameters. Then the elastic moduli of the rock masses were obtained according to the monitoring displacement data at different excavation stages, and the BP neural network model was proved to be valid by comparing the measured displacements, the displacements predicted by the BP neural network model, and the numerical simulation using the back-analyzed parameters. The proposed model is useful for rock mechanical parameters determination and instability investigation of rock slopes.

Displacement Back Analysis for a High Slope of the Dagangshan Hydroelectric Power Station Based on BP Neural Network and Particle Swarm Optimization

PubMed Central

Liang, Zhengzhao; Gong, Bin; Tang, Chunan; Zhang, Yongbin; Ma, Tianhui

2014-01-01

The right bank high slope of the Dagangshan Hydroelectric Power Station is located in complicated geological conditions with deep fractures and unloading cracks. How to obtain the mechanical parameters and then evaluate the safety of the slope are the key problems. This paper presented a displacement back analysis for the slope using an artificial neural network model (ANN) and particle swarm optimization model (PSO). A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model. The backpropagation ANN model was used to establish a mapping function between the mechanical parameters and the monitoring displacements. The PSO model was applied to initialize the weights and thresholds of the backpropagation (BP) network model and determine suitable values of the mechanical parameters. Then the elastic moduli of the rock masses were obtained according to the monitoring displacement data at different excavation stages, and the BP neural network model was proved to be valid by comparing the measured displacements, the displacements predicted by the BP neural network model, and the numerical simulation using the back-analyzed parameters. The proposed model is useful for rock mechanical parameters determination and instability investigation of rock slopes. PMID:25140345

On the identification of cohesive parameters for printed metal-polymer interfaces

NASA Astrophysics Data System (ADS)

Heinrich, Felix; Langner, Hauke H.; Lammering, Rolf

2017-05-01

The mechanical behavior of printed electronics on fiber reinforced composites is investigated. A methodology based on cohesive zone models is employed, considering interfacial strengths, stiffnesses and critical strain energy release rates. A double cantilever beam test and an end notched flexure test are carried out to experimentally determine critical strain energy release rates under fracture modes I and II. Numerical simulations are performed in Abaqus 6.13 to model both tests. Applying the simulations, an inverse parameter identification is run to determine the full set of cohesive parameters.

Family of columns isospectral to gravity-loaded columns with tip force: A discrete approach

NASA Astrophysics Data System (ADS)

Ramachandran, Nirmal; Ganguli, Ranjan

2018-06-01

A discrete model is introduced to analyze transverse vibration of straight, clamped-free (CF) columns of variable cross-sectional geometry under the influence of gravity and a constant axial force at the tip. The discrete model is used to determine critical combinations of loading parameters - a gravity parameter and a tip force parameter - that cause onset of dynamic instability in the CF column. A methodology, based on matrix-factorization, is described to transform the discrete model into a family of models corresponding to weightless and unloaded clamped-free (WUCF) columns, each with a transverse vibration spectrum isospectral to the original model. Characteristics of models in this isospectral family are dependent on three transformation parameters. A procedure is discussed to convert the isospectral discrete model description into geometric description of realistic columns i.e. from the discrete model, we construct isospectral WUCF columns with rectangular cross-sections varying in width and depth. As part of numerical studies to demonstrate efficacy of techniques presented, frequency parameters of a uniform column and three types of tapered CF columns under different combinations of loading parameters are obtained from the discrete model. Critical combinations of these parameters for a typical tapered column are derived. These results match with published results. Example CF columns, under arbitrarily-chosen combinations of loading parameters are considered and for each combination, isospectral WUCF columns are constructed. Role of transformation parameters in determining characteristics of isospectral columns is discussed and optimum values are deduced. Natural frequencies of these WUCF columns computed using Finite Element Method (FEM) match well with those of the given gravity-loaded CF column with tip force, hence confirming isospectrality.

Three-dimensional FEM model of FBGs in PANDA fibers with experimentally determined model parameters

NASA Astrophysics Data System (ADS)

Lindner, Markus; Hopf, Barbara; Koch, Alexander W.; Roths, Johannes

2017-04-01

A 3D-FEM model has been developed to improve the understanding of multi-parameter sensing with Bragg gratings in attached or embedded polarization maintaining fibers. The material properties of the fiber, especially Young's modulus and Poisson's ratio of the fiber's stress applying parts, are crucial for accurate simulations, but are usually not provided by the manufacturers. A methodology is presented to determine the unknown parameters by using experimental characterizations of the fiber and iterative FEM simulations. The resulting 3D-Model is capable of describing the change in birefringence of the free fiber when exposed to longitudinal strain. In future studies the 3D-FEM model will be employed to study the interaction of PANDA fibers with the surrounding materials in which they are embedded.

Application of an automatic approach to calibrate the NEMURO nutrient-phytoplankton-zooplankton food web model in the Oyashio region

NASA Astrophysics Data System (ADS)

Ito, Shin-ichi; Yoshie, Naoki; Okunishi, Takeshi; Ono, Tsuneo; Okazaki, Yuji; Kuwata, Akira; Hashioka, Taketo; Rose, Kenneth A.; Megrey, Bernard A.; Kishi, Michio J.; Nakamachi, Miwa; Shimizu, Yugo; Kakehi, Shigeho; Saito, Hiroaki; Takahashi, Kazutaka; Tadokoro, Kazuaki; Kusaka, Akira; Kasai, Hiromi

2010-10-01

The Oyashio region in the western North Pacific supports high biological productivity and has been well monitored. We applied the NEMURO (North Pacific Ecosystem Model for Understanding Regional Oceanography) model to simulate the nutrients, phytoplankton, and zooplankton dynamics. Determination of parameters values is very important, yet ad hoc calibration methods are often used. We used the automatic calibration software PEST (model-independent Parameter ESTimation), which has been used previously with NEMURO but in a system without ontogenetic vertical migration of the large zooplankton functional group. Determining the performance of PEST with vertical migration, and obtaining a set of realistic parameter values for the Oyashio, will likely be useful in future applications of NEMURO. Five identical twin simulation experiments were performed with the one-box version of NEMURO. The experiments differed in whether monthly snapshot or averaged state variables were used, in whether state variables were model functional groups or were aggregated (total phytoplankton, small plus large zooplankton), and in whether vertical migration of large zooplankton was included or not. We then applied NEMURO to monthly climatological field data covering 1 year for the Oyashio, and compared model fits and parameter values between PEST-determined estimates and values used in previous applications to the Oyashio region that relied on ad hoc calibration. We substituted the PEST and ad hoc calibrated parameter values into a 3-D version of NEMURO for the western North Pacific, and compared the two sets of spatial maps of chlorophyll- a with satellite-derived data. The identical twin experiments demonstrated that PEST could recover the known model parameter values when vertical migration was included, and that over-fitting can occur as a result of slight differences in the values of the state variables. PEST recovered known parameter values when using monthly snapshots of aggregated state variables, but estimated a different set of parameters with monthly averaged values. Both sets of parameters resulted in good fits of the model to the simulated data. Disaggregating the variables provided to PEST into functional groups did not solve the over-fitting problem, and including vertical migration seemed to amplify the problem. When we used the climatological field data, simulated values with PEST-estimated parameters were closer to these field data than with the previously determined ad hoc set of parameter values. When these same PEST and ad hoc sets of parameter values were substituted into 3-D-NEMURO (without vertical migration), the PEST-estimated parameter values generated spatial maps that were similar to the satellite data for the Kuroshio Extension during January and March and for the subarctic ocean from May to November. With non-linear problems, such as vertical migration, PEST should be used with caution because parameter estimates can be sensitive to how the data are prepared and to the values used for the searching parameters of PEST. We recommend the usage of PEST, or other parameter optimization methods, to generate first-order parameter estimates for simulating specific systems and for insertion into 2-D and 3-D models. The parameter estimates that are generated are useful, and the inconsistencies between simulated values and the available field data provide valuable information on model behavior and the dynamics of the ecosystem.

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.

Influence of different dose calculation algorithms on the estimate of NTCP for lung complications.

PubMed

Hedin, Emma; Bäck, Anna

2013-09-06

Due to limitations and uncertainties in dose calculation algorithms, different algorithms can predict different dose distributions and dose-volume histograms for the same treatment. This can be a problem when estimating the normal tissue complication probability (NTCP) for patient-specific dose distributions. Published NTCP model parameters are often derived for a different dose calculation algorithm than the one used to calculate the actual dose distribution. The use of algorithm-specific NTCP model parameters can prevent errors caused by differences in dose calculation algorithms. The objective of this work was to determine how to change the NTCP model parameters for lung complications derived for a simple correction-based pencil beam dose calculation algorithm, in order to make them valid for three other common dose calculation algorithms. NTCP was calculated with the relative seriality (RS) and Lyman-Kutcher-Burman (LKB) models. The four dose calculation algorithms used were the pencil beam (PB) and collapsed cone (CC) algorithms employed by Oncentra, and the pencil beam convolution (PBC) and anisotropic analytical algorithm (AAA) employed by Eclipse. Original model parameters for lung complications were taken from four published studies on different grades of pneumonitis, and new algorithm-specific NTCP model parameters were determined. The difference between original and new model parameters was presented in relation to the reported model parameter uncertainties. Three different types of treatments were considered in the study: tangential and locoregional breast cancer treatment and lung cancer treatment. Changing the algorithm without the derivation of new model parameters caused changes in the NTCP value of up to 10 percentage points for the cases studied. Furthermore, the error introduced could be of the same magnitude as the confidence intervals of the calculated NTCP values. The new NTCP model parameters were tabulated as the algorithm was varied from PB to PBC, AAA, or CC. Moving from the PB to the PBC algorithm did not require new model parameters; however, moving from PB to AAA or CC did require a change in the NTCP model parameters, with CC requiring the largest change. It was shown that the new model parameters for a given algorithm are different for the different treatment types.

Hot-spot model for accretion disc variability as random process. II. Mathematics of the power-spectrum break frequency

NASA Astrophysics Data System (ADS)

Pecháček, T.; Goosmann, R. W.; Karas, V.; Czerny, B.; Dovčiak, M.

2013-08-01

Context. We study some general properties of accretion disc variability in the context of stationary random processes. In particular, we are interested in mathematical constraints that can be imposed on the functional form of the Fourier power-spectrum density (PSD) that exhibits a multiply broken shape and several local maxima. Aims: We develop a methodology for determining the regions of the model parameter space that can in principle reproduce a PSD shape with a given number and position of local peaks and breaks of the PSD slope. Given the vast space of possible parameters, it is an important requirement that the method is fast in estimating the PSD shape for a given parameter set of the model. Methods: We generated and discuss the theoretical PSD profiles of a shot-noise-type random process with exponentially decaying flares. Then we determined conditions under which one, two, or more breaks or local maxima occur in the PSD. We calculated positions of these features and determined the changing slope of the model PSD. Furthermore, we considered the influence of the modulation by the orbital motion for a variability pattern assumed to result from an orbiting-spot model. Results: We suggest that our general methodology can be useful for describing non-monotonic PSD profiles (such as the trend seen, on different scales, in exemplary cases of the high-mass X-ray binary Cygnus X-1 and the narrow-line Seyfert galaxy Ark 564). We adopt a model where these power spectra are reproduced as a superposition of several Lorentzians with varying amplitudes in the X-ray-band light curve. Our general approach can help in constraining the model parameters and in determining which parts of the parameter space are accessible under various circumstances.

A Short-Term and High-Resolution System Load Forecasting Approach Using Support Vector Regression with Hybrid Parameters Optimization

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

Jiang, Huaiguang

This work proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of themore » hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system.« less

StePar: an automatic code for stellar parameter determination

NASA Astrophysics Data System (ADS)

Tabernero, H. M.; González Hernández, J. I.; Montes, D.

2013-05-01

We introduce a new automatic code (StePar) for determinig stellar atmospheric parameters (T_{eff}, log{g}, ξ and [Fe/H]) in an automated way. StePar employs the 2002 version of the MOOG code (Sneden 1973) and a grid of Kurucz ATLAS9 plane-paralell model atmospheres (Kurucz 1993). The atmospheric parameters are obtained from the EWs of 263 Fe I and 36 Fe II lines (obtained from Sousa et al. 2008, A&A, 487, 373) iterating until the excitation and ionization equilibrium are fullfilled. StePar uses a Downhill Simplex method that minimizes a quadratic form composed by the excitation and ionization equilibrium conditions. Atmospheric parameters determined by StePar are independent of the stellar parameters initial-guess for the problem star, therefore we employ the canonical solar values as initial input. StePar can only deal with FGK stars from F6 to K4, also it can not work with fast rotators, veiled spectra, very metal poor stars or Signal to noise ratio below 30. Optionally StePar can operate with MARCS models (Gustafson et al. 2008, A&A, 486, 951) instead of Kurucz ATLAS9 models, additionally Turbospectrum (Alvarez & Plez 1998, A&A, 330, 1109) can replace the MOOG code and play its role during the parameter determination. StePar has been used to determine stellar parameters for some studies (Tabernero et al. 2012, A&A, 547, A13; Wisniewski et al. 2012, AJ, 143, 107). In addition StePar is being used to obtain parameters for FGK stars from the GAIA-ESO Survey.

Bayesian Inference in Satellite Gravity Inversion

NASA Technical Reports Server (NTRS)

Kis, K. I.; Taylor, Patrick T.; Wittmann, G.; Kim, Hyung Rae; Torony, B.; Mayer-Guerr, T.

2005-01-01

To solve a geophysical inverse problem means applying measurements to determine the parameters of the selected model. The inverse problem is formulated as the Bayesian inference. The Gaussian probability density functions are applied in the Bayes's equation. The CHAMP satellite gravity data are determined at the altitude of 400 kilometer altitude over the South part of the Pannonian basin. The model of interpretation is the right vertical cylinder. The parameters of the model are obtained from the minimum problem solved by the Simplex method.

Identification of Synchronous Machine Stability - Parameters: AN On-Line Time-Domain Approach.

NASA Astrophysics Data System (ADS)

Le, Loc Xuan

1987-09-01

A time-domain modeling approach is described which enables the stability-study parameters of the synchronous machine to be determined directly from input-output data measured at the terminals of the machine operating under normal conditions. The transient responses due to system perturbations are used to identify the parameters of the equivalent circuit models. The described models are verified by comparing their responses with the machine responses generated from the transient stability models of a small three-generator multi-bus power system and of a single -machine infinite-bus power network. The least-squares method is used for the solution of the model parameters. As a precaution against ill-conditioned problems, the singular value decomposition (SVD) is employed for its inherent numerical stability. In order to identify the equivalent-circuit parameters uniquely, the solution of a linear optimization problem with non-linear constraints is required. Here, the SVD appears to offer a simple solution to this otherwise difficult problem. Furthermore, the SVD yields solutions with small bias and, therefore, physically meaningful parameters even in the presence of noise in the data. The question concerning the need for a more advanced model of the synchronous machine which describes subtransient and even sub-subtransient behavior is dealt with sensibly by the concept of condition number. The concept provides a quantitative measure for determining whether such an advanced model is indeed necessary. Finally, the recursive SVD algorithm is described for real-time parameter identification and tracking of slowly time-variant parameters. The algorithm is applied to identify the dynamic equivalent power system model.

Parameter sensitivity analysis of a 1-D cold region lake model for land-surface schemes

NASA Astrophysics Data System (ADS)

Guerrero, José-Luis; Pernica, Patricia; Wheater, Howard; Mackay, Murray; Spence, Chris

2017-12-01

Lakes might be sentinels of climate change, but the uncertainty in their main feedback to the atmosphere - heat-exchange fluxes - is often not considered within climate models. Additionally, these fluxes are seldom measured, hindering critical evaluation of model output. Analysis of the Canadian Small Lake Model (CSLM), a one-dimensional integral lake model, was performed to assess its ability to reproduce diurnal and seasonal variations in heat fluxes and the sensitivity of simulated fluxes to changes in model parameters, i.e., turbulent transport parameters and the light extinction coefficient (Kd). A C++ open-source software package, Problem Solving environment for Uncertainty Analysis and Design Exploration (PSUADE), was used to perform sensitivity analysis (SA) and identify the parameters that dominate model behavior. The generalized likelihood uncertainty estimation (GLUE) was applied to quantify the fluxes' uncertainty, comparing daily-averaged eddy-covariance observations to the output of CSLM. Seven qualitative and two quantitative SA methods were tested, and the posterior likelihoods of the modeled parameters, obtained from the GLUE analysis, were used to determine the dominant parameters and the uncertainty in the modeled fluxes. Despite the ubiquity of the equifinality issue - different parameter-value combinations yielding equivalent results - the answer to the question was unequivocal: Kd, a measure of how much light penetrates the lake, dominates sensible and latent heat fluxes, and the uncertainty in their estimates is strongly related to the accuracy with which Kd is determined. This is important since accurate and continuous measurements of Kd could reduce modeling uncertainty.

Global Sensitivity Applied to Dynamic Combined Finite Discrete Element Methods for Fracture Simulation

NASA Astrophysics Data System (ADS)

Godinez, H. C.; Rougier, E.; Osthus, D.; Srinivasan, G.

2017-12-01

Fracture propagation play a key role for a number of application of interest to the scientific community. From dynamic fracture processes like spall and fragmentation in metals and detection of gas flow in static fractures in rock and the subsurface, the dynamics of fracture propagation is important to various engineering and scientific disciplines. In this work we implement a global sensitivity analysis test to the Hybrid Optimization Software Suite (HOSS), a multi-physics software tool based on the combined finite-discrete element method, that is used to describe material deformation and failure (i.e., fracture and fragmentation) under a number of user-prescribed boundary conditions. We explore the sensitivity of HOSS for various model parameters that influence how fracture are propagated through a material of interest. The parameters control the softening curve that the model relies to determine fractures within each element in the mesh, as well a other internal parameters which influence fracture behavior. The sensitivity method we apply is the Fourier Amplitude Sensitivity Test (FAST), which is a global sensitivity method to explore how each parameter influence the model fracture and to determine the key model parameters that have the most impact on the model. We present several sensitivity experiments for different combination of model parameters and compare against experimental data for verification.

Impact of the time scale of model sensitivity response on coupled model parameter estimation

NASA Astrophysics Data System (ADS)

Liu, Chang; Zhang, Shaoqing; Li, Shan; Liu, Zhengyu

2017-11-01

That a model has sensitivity responses to parameter uncertainties is a key concept in implementing model parameter estimation using filtering theory and methodology. Depending on the nature of associated physics and characteristic variability of the fluid in a coupled system, the response time scales of a model to parameters can be different, from hourly to decadal. Unlike state estimation, where the update frequency is usually linked with observational frequency, the update frequency for parameter estimation must be associated with the time scale of the model sensitivity response to the parameter being estimated. Here, with a simple coupled model, the impact of model sensitivity response time scales on coupled model parameter estimation is studied. The model includes characteristic synoptic to decadal scales by coupling a long-term varying deep ocean with a slow-varying upper ocean forced by a chaotic atmosphere. Results show that, using the update frequency determined by the model sensitivity response time scale, both the reliability and quality of parameter estimation can be improved significantly, and thus the estimated parameters make the model more consistent with the observation. These simple model results provide a guideline for when real observations are used to optimize the parameters in a coupled general circulation model for improving climate analysis and prediction initialization.

Determination of morphological parameters of biological cells by analysis of scattered-light distributions

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

Burger, D.E.

1979-11-01

The extraction of morphological parameters from biological cells by analysis of light-scatter patterns is described. A light-scattering measurement system has been designed and constructed that allows one to visually examine and photographically record biological cells or cell models and measure the light-scatter pattern of an individual cell or cell model. Using a laser or conventional illumination, the imaging system consists of a modified microscope with a 35 mm camera attached to record the cell image or light-scatter pattern. Models of biological cells were fabricated. The dynamic range and angular distributions of light scattered from these models was compared to calculatedmore » distributions. Spectrum analysis techniques applied on the light-scatter data give the sought after morphological cell parameters. These results compared favorably to shape parameters of the fabricated cell models confirming the mathematical model procedure. For nucleated biological material, correct nuclear and cell eccentricity as well as the nuclear and cytoplasmic diameters were determined. A method for comparing the flow equivalent of nuclear and cytoplasmic size to the actual dimensions is shown. This light-scattering experiment provides baseline information for automated cytology. In its present application, it involves correlating average size as measured in flow cytology to the actual dimensions determined from this technique. (ERB)« less

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

Model-based high-throughput design of ion exchange protein chromatography.

PubMed

Khalaf, Rushd; Heymann, Julia; LeSaout, Xavier; Monard, Florence; Costioli, Matteo; Morbidelli, Massimo

2016-08-12

This work describes the development of a model-based high-throughput design (MHD) tool for the operating space determination of a chromatographic cation-exchange protein purification process. Based on a previously developed thermodynamic mechanistic model, the MHD tool generates a large amount of system knowledge and thereby permits minimizing the required experimental workload. In particular, each new experiment is designed to generate information needed to help refine and improve the model. Unnecessary experiments that do not increase system knowledge are avoided. Instead of aspiring to a perfectly parameterized model, the goal of this design tool is to use early model parameter estimates to find interesting experimental spaces, and to refine the model parameter estimates with each new experiment until a satisfactory set of process parameters is found. The MHD tool is split into four sections: (1) prediction, high throughput experimentation using experiments in (2) diluted conditions and (3) robotic automated liquid handling workstations (robotic workstation), and (4) operating space determination and validation. (1) Protein and resin information, in conjunction with the thermodynamic model, is used to predict protein resin capacity. (2) The predicted model parameters are refined based on gradient experiments in diluted conditions. (3) Experiments on the robotic workstation are used to further refine the model parameters. (4) The refined model is used to determine operating parameter space that allows for satisfactory purification of the protein of interest on the HPLC scale. Each section of the MHD tool is used to define the adequate experimental procedures for the next section, thus avoiding any unnecessary experimental work. We used the MHD tool to design a polishing step for two proteins, a monoclonal antibody and a fusion protein, on two chromatographic resins, in order to demonstrate it has the ability to strongly accelerate the early phases of process development. Copyright © 2016 Elsevier B.V. All rights reserved.

Efficient micromagnetic modelling of spin-transfer torque and spin-orbit torque

NASA Astrophysics Data System (ADS)

Abert, Claas; Bruckner, Florian; Vogler, Christoph; Suess, Dieter

2018-05-01

While the spin-diffusion model is considered one of the most complete and accurate tools for the description of spin transport and spin torque, its solution in the context of dynamical micromagnetic simulations is numerically expensive. We propose a procedure to retrieve the free parameters of a simple macro-spin like spin-torque model through the spin-diffusion model. In case of spin-transfer torque the simplified model complies with the model of Slonczewski. A similar model can be established for the description of spin-orbit torque. In both cases the spin-diffusion model enables the retrieval of free model parameters from the geometry and the material parameters of the system. Since these parameters usually have to be determined phenomenologically through experiments, the proposed method combines the strength of the diffusion model to resolve material parameters and geometry with the high performance of simple torque models.

Statistical Parameter Study of the Time Interval Distribution for Nonparalyzable, Paralyzable, and Hybrid Dead Time Models

NASA Astrophysics Data System (ADS)

Syam, Nur Syamsi; Maeng, Seongjin; Kim, Myo Gwang; Lim, Soo Yeon; Lee, Sang Hoon

2018-05-01

A large dead time of a Geiger Mueller (GM) detector may cause a large count loss in radiation measurements and consequently may cause distortion of the Poisson statistic of radiation events into a new distribution. The new distribution will have different statistical parameters compared to the original distribution. Therefore, the variance, skewness, and excess kurtosis in association with the observed count rate of the time interval distribution for well-known nonparalyzable, paralyzable, and nonparalyzable-paralyzable hybrid dead time models of a Geiger Mueller detector were studied using Monte Carlo simulation (GMSIM). These parameters were then compared with the statistical parameters of a perfect detector to observe the change in the distribution. The results show that the behaviors of the statistical parameters for the three dead time models were different. The values of the skewness and the excess kurtosis of the nonparalyzable model are equal or very close to those of the perfect detector, which are ≅2 for skewness, and ≅6 for excess kurtosis, while the statistical parameters in the paralyzable and hybrid model obtain minimum values that occur around the maximum observed count rates. The different trends of the three models resulting from the GMSIM simulation can be used to distinguish the dead time behavior of a GM counter; i.e. whether the GM counter can be described best by using the nonparalyzable, paralyzable, or hybrid model. In a future study, these statistical parameters need to be analyzed further to determine the possibility of using them to determine a dead time for each model, particularly for paralyzable and hybrid models.

Global Sensitivity of Simulated Water Balance Indicators Under Future Climate Change in the Colorado Basin

DOE PAGES

Bennett, Katrina Eleanor; Urrego Blanco, Jorge Rolando; Jonko, Alexandra; ...

2017-11-20

The Colorado River basin is a fundamentally important river for society, ecology and energy in the United States. Streamflow estimates are often provided using modeling tools which rely on uncertain parameters; sensitivity analysis can help determine which parameters impact model results. Despite the fact that simulated flows respond to changing climate and vegetation in the basin, parameter sensitivity of the simulations under climate change has rarely been considered. In this study, we conduct a global sensitivity analysis to relate changes in runoff, evapotranspiration, snow water equivalent and soil moisture to model parameters in the Variable Infiltration Capacity (VIC) hydrologic model.more » Here, we combine global sensitivity analysis with a space-filling Latin Hypercube sampling of the model parameter space and statistical emulation of the VIC model to examine sensitivities to uncertainties in 46 model parameters following a variance-based approach.« less

Global Sensitivity of Simulated Water Balance Indicators Under Future Climate Change in the Colorado Basin

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

Bennett, Katrina Eleanor; Urrego Blanco, Jorge Rolando; Jonko, Alexandra

The Colorado River basin is a fundamentally important river for society, ecology and energy in the United States. Streamflow estimates are often provided using modeling tools which rely on uncertain parameters; sensitivity analysis can help determine which parameters impact model results. Despite the fact that simulated flows respond to changing climate and vegetation in the basin, parameter sensitivity of the simulations under climate change has rarely been considered. In this study, we conduct a global sensitivity analysis to relate changes in runoff, evapotranspiration, snow water equivalent and soil moisture to model parameters in the Variable Infiltration Capacity (VIC) hydrologic model.more » Here, we combine global sensitivity analysis with a space-filling Latin Hypercube sampling of the model parameter space and statistical emulation of the VIC model to examine sensitivities to uncertainties in 46 model parameters following a variance-based approach.« less

Method, system, and computer-readable medium for determining performance characteristics of an object undergoing one or more arbitrary aging conditions

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

Gering, Kevin L.

A method, system, and computer-readable medium are described for characterizing performance loss of an object undergoing an arbitrary aging condition. Baseline aging data may be collected from the object for at least one known baseline aging condition over time, determining baseline multiple sigmoid model parameters from the baseline data, and performance loss of the object may be determined over time through multiple sigmoid model parameters associated with the object undergoing the arbitrary aging condition using a differential deviation-from-baseline approach from the baseline multiple sigmoid model parameters. The system may include an object, monitoring hardware configured to sample performance characteristics ofmore » the object, and a processor coupled to the monitoring hardware. The processor is configured to determine performance loss for the arbitrary aging condition from a comparison of the performance characteristics of the object deviating from baseline performance characteristics associated with a baseline aging condition.« less

From design to manufacturing of asymmetric teeth gears using computer application

NASA Astrophysics Data System (ADS)

Suciu, F.; Dascalescu, A.; Ungureanu, M.

2017-05-01

The asymmetric cylindrical gears, with involutes teeth profiles having different base circle diameters, are nonstandard gears, used with the aim to obtain better function parameters for the active profile. We will expect that the manufacturing of these gears became possible only after the design and realization of some specific tools. The paper present how the computer aided design and applications developed in MATLAB, for obtain the geometrical parameters, in the same time for calculation some functional parameters like stress and displacements, transmission error, efficiency of the gears and the 2D models, generated with AUTOLISP applications, are used for computer aided manufacturing of asymmetric gears with standard tools. So the specific tools considered one of the disadvantages of these gears are not necessary and implicitly the expected supplementary costs are reduced. The calculus algorithm established for the asymmetric gear design application use the „direct design“ of the spur gears. This method offers the possibility of determining first the parameters of the gears, followed by the determination of the asymmetric gear rack’s parameters, based on those of the gears. Using original design method and computer applications have been determined the geometrical parameters, the 2D and 3D models of the asymmetric gears and on the base of these models have been manufacturing on CNC machine tool asymmetric gears.

System health monitoring using multiple-model adaptive estimation techniques

NASA Astrophysics Data System (ADS)

Sifford, Stanley Ryan

Monitoring system health for fault detection and diagnosis by tracking system parameters concurrently with state estimates is approached using a new multiple-model adaptive estimation (MMAE) method. This novel method is called GRid-based Adaptive Parameter Estimation (GRAPE). GRAPE expands existing MMAE methods by using new techniques to sample the parameter space. GRAPE expands on MMAE with the hypothesis that sample models can be applied and resampled without relying on a predefined set of models. GRAPE is initially implemented in a linear framework using Kalman filter models. A more generalized GRAPE formulation is presented using extended Kalman filter (EKF) models to represent nonlinear systems. GRAPE can handle both time invariant and time varying systems as it is designed to track parameter changes. Two techniques are presented to generate parameter samples for the parallel filter models. The first approach is called selected grid-based stratification (SGBS). SGBS divides the parameter space into equally spaced strata. The second approach uses Latin Hypercube Sampling (LHS) to determine the parameter locations and minimize the total number of required models. LHS is particularly useful when the parameter dimensions grow. Adding more parameters does not require the model count to increase for LHS. Each resample is independent of the prior sample set other than the location of the parameter estimate. SGBS and LHS can be used for both the initial sample and subsequent resamples. Furthermore, resamples are not required to use the same technique. Both techniques are demonstrated for both linear and nonlinear frameworks. The GRAPE framework further formalizes the parameter tracking process through a general approach for nonlinear systems. These additional methods allow GRAPE to either narrow the focus to converged values within a parameter range or expand the range in the appropriate direction to track the parameters outside the current parameter range boundary. Customizable rules define the specific resample behavior when the GRAPE parameter estimates converge. Convergence itself is determined from the derivatives of the parameter estimates using a simple moving average window to filter out noise. The system can be tuned to match the desired performance goals by making adjustments to parameters such as the sample size, convergence criteria, resample criteria, initial sampling method, resampling method, confidence in prior sample covariances, sample delay, and others.

Impact parameter determination in experimental analysis using a neural network

NASA Astrophysics Data System (ADS)

Haddad, F.; Hagel, K.; Li, J.; Mdeiwayeh, N.; Natowitz, J. B.; Wada, R.; Xiao, B.; David, C.; Freslier, M.; Aichelin, J.

1997-03-01

A neural network is used to determine the impact parameter in 40Ca+40Ca reactions. The effect of the detection efficiency as well as the model dependence of the training procedure has been studied carefully. An overall improvement of the impact parameter determination of 25% is obtained using this technique. The analysis of Amphora 40Ca+40Ca data at 35 MeV per nucleon using a neural network shows two well-separated classes of events among the selected ``complete'' events.

Improved parameter inference in catchment models: 1. Evaluating parameter uncertainty

NASA Astrophysics Data System (ADS)

Kuczera, George

1983-10-01

A Bayesian methodology is developed to evaluate parameter uncertainty in catchment models fitted to a hydrologic response such as runoff, the goal being to improve the chance of successful regionalization. The catchment model is posed as a nonlinear regression model with stochastic errors possibly being both autocorrelated and heteroscedastic. The end result of this methodology, which may use Box-Cox power transformations and ARMA error models, is the posterior distribution, which summarizes what is known about the catchment model parameters. This can be simplified to a multivariate normal provided a linearization in parameter space is acceptable; means of checking and improving this assumption are discussed. The posterior standard deviations give a direct measure of parameter uncertainty, and study of the posterior correlation matrix can indicate what kinds of data are required to improve the precision of poorly determined parameters. Finally, a case study involving a nine-parameter catchment model fitted to monthly runoff and soil moisture data is presented. It is shown that use of ordinary least squares when its underlying error assumptions are violated gives an erroneous description of parameter uncertainty.

Lumped parametric model of the human ear for sound transmission.

PubMed

Feng, Bin; Gan, Rong Z

2004-09-01

A lumped parametric model of the human auditoria peripherals consisting of six masses suspended with six springs and ten dashpots was proposed. This model will provide the quantitative basis for the construction of a physical model of the human middle ear. The lumped model parameters were first identified using published anatomical data, and then determined through a parameter optimization process. The transfer function of the middle ear obtained from human temporal bone experiments with laser Doppler interferometers was used for creating the target function during the optimization process. It was found that, among 14 spring and dashpot parameters, there were five parameters which had pronounced effects on the dynamic behaviors of the model. The detailed discussion on the sensitivity of those parameters was provided with appropriate applications for sound transmission in the ear. We expect that the methods for characterizing the lumped model of the human ear and the model parameters will be useful for theoretical modeling of the ear function and construction of the ear physical model.

Control and Diagnostic Model of Brushless Dc Motor

NASA Astrophysics Data System (ADS)

Abramov, Ivan V.; Nikitin, Yury R.; Abramov, Andrei I.; Sosnovich, Ella V.; Božek, Pavol

2014-09-01

A simulation model of brushless DC motor (BLDC) control and diagnostics is considered. The model has been developed using a freeware complex "Modeling in technical devices". Faults and diagnostic parameters of BLDC are analyzed. A logicallinguistic diagnostic model of BLDC has been developed on basis of fuzzy logic. The calculated rules determine dependence of technical condition on diagnostic parameters, their trends and utilized lifetime of BLDC. Experimental results of BLDC technical condition diagnostics are discussed. It is shown that in the course of BLDC degradation the motor condition change depends on diagnostic parameter values

The general linear inverse problem - Implication of surface waves and free oscillations for earth structure.

NASA Technical Reports Server (NTRS)

Wiggins, R. A.

1972-01-01

The discrete general linear inverse problem reduces to a set of m equations in n unknowns. There is generally no unique solution, but we can find k linear combinations of parameters for which restraints are determined. The parameter combinations are given by the eigenvectors of the coefficient matrix. The number k is determined by the ratio of the standard deviations of the observations to the allowable standard deviations in the resulting solution. Various linear combinations of the eigenvectors can be used to determine parameter resolution and information distribution among the observations. Thus we can determine where information comes from among the observations and exactly how it constraints the set of possible models. The application of such analyses to surface-wave and free-oscillation observations indicates that (1) phase, group, and amplitude observations for any particular mode provide basically the same type of information about the model; (2) observations of overtones can enhance the resolution considerably; and (3) the degree of resolution has generally been overestimated for many model determinations made from surface waves.

Using Active Learning for Speeding up Calibration in Simulation Models.

PubMed

Cevik, Mucahit; Ergun, Mehmet Ali; Stout, Natasha K; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2016-07-01

Most cancer simulation models include unobservable parameters that determine disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality, and their values are typically estimated via a lengthy calibration procedure, which involves evaluating a large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We developed an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs and therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using the previously developed University of Wisconsin breast cancer simulation model (UWBCS). In a recent study, calibration of the UWBCS required the evaluation of 378 000 input parameter combinations to build a race-specific model, and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378 000 combinations. Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. © The Author(s) 2015.

Using Active Learning for Speeding up Calibration in Simulation Models

PubMed Central

Cevik, Mucahit; Ali Ergun, Mehmet; Stout, Natasha K.; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2015-01-01

Background Most cancer simulation models include unobservable parameters that determine the disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality and their values are typically estimated via lengthy calibration procedure, which involves evaluating large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Methods Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We develop an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs, therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using previously developed University of Wisconsin Breast Cancer Simulation Model (UWBCS). Results In a recent study, calibration of the UWBCS required the evaluation of 378,000 input parameter combinations to build a race-specific model and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378,000 combinations. Conclusion Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. PMID:26471190

Improved Fuzzy K-Nearest Neighbor Using Modified Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Jamaluddin; Siringoringo, Rimbun

2017-12-01

Fuzzy k-Nearest Neighbor (FkNN) is one of the most powerful classification methods. The presence of fuzzy concepts in this method successfully improves its performance on almost all classification issues. The main drawbackof FKNN is that it is difficult to determine the parameters. These parameters are the number of neighbors (k) and fuzzy strength (m). Both parameters are very sensitive. This makes it difficult to determine the values of ‘m’ and ‘k’, thus making FKNN difficult to control because no theories or guides can deduce how proper ‘m’ and ‘k’ should be. This study uses Modified Particle Swarm Optimization (MPSO) to determine the best value of ‘k’ and ‘m’. MPSO is focused on the Constriction Factor Method. Constriction Factor Method is an improvement of PSO in order to avoid local circumstances optima. The model proposed in this study was tested on the German Credit Dataset. The test of the data/The data test has been standardized by UCI Machine Learning Repository which is widely applied to classification problems. The application of MPSO to the determination of FKNN parameters is expected to increase the value of classification performance. Based on the experiments that have been done indicating that the model offered in this research results in a better classification performance compared to the Fk-NN model only. The model offered in this study has an accuracy rate of 81%, while. With using Fk-NN model, it has the accuracy of 70%. At the end is done comparison of research model superiority with 2 other classification models;such as Naive Bayes and Decision Tree. This research model has a better performance level, where Naive Bayes has accuracy 75%, and the decision tree model has 70%

The Definition of Difficulty and Discrimination for Multidimensional Item Response Theory Models.

ERIC Educational Resources Information Center

Reckase, Mark D.; McKinley, Robert L.

A study was undertaken to develop guidelines for the interpretation of the parameters of three multidimensional item response theory models and to determine the relationship between the parameters and traditional concepts of item difficulty and discrimination. The three models considered were multidimensional extensions of the one-, two-, and…

BAYESIAN PARAMETER ESTIMATION IN A MIXED-ORDER MODEL OF BOD DECAY. (U915590)

EPA Science Inventory

We describe a generalized version of the BOD decay model in which the reaction is allowed to assume an order other than one. This is accomplished by making the exponent on BOD concentration a free parameter to be determined by the data. This "mixed-order" model may be ...

Determination of parameters of the Johnson-Cook model for the description of deformation and fracture of titanium alloys

NASA Astrophysics Data System (ADS)

Buzyurkin, A. E.; Gladky, I. L.; Kraus, E. I.

2015-03-01

Stress-strain curves of dynamic loading of VT6, OT4, and OT4-0 titanium-based alloys are constructed on the basis of experimental data, and the Johnson-Cook model parameters are determined. Results of LS-DYNA simulations of the processes of deformation and fracture of the fan casing after its high-velocity impact with a fan blade simulator are presented.

Exact solution of three-dimensional transport problems using one-dimensional models. [in semiconductor devices

NASA Technical Reports Server (NTRS)

Misiakos, K.; Lindholm, F. A.

1986-01-01

Several parameters of certain three-dimensional semiconductor devices including diodes, transistors, and solar cells can be determined without solving the actual boundary-value problem. The recombination current, transit time, and open-circuit voltage of planar diodes are emphasized here. The resulting analytical expressions enable determination of the surface recombination velocity of shallow planar diodes. The method involves introducing corresponding one-dimensional models having the same values of these parameters.

Advanced Method to Estimate Fuel Slosh Simulation Parameters

NASA Technical Reports Server (NTRS)

Schlee, Keith; Gangadharan, Sathya; Ristow, James; Sudermann, James; Walker, Charles; Hubert, Carl

2005-01-01

The nutation (wobble) of a spinning spacecraft in the presence of energy dissipation is a well-known problem in dynamics and is of particular concern for space missions. The nutation of a spacecraft spinning about its minor axis typically grows exponentially and the rate of growth is characterized by the Nutation Time Constant (NTC). For launch vehicles using spin-stabilized upper stages, fuel slosh in the spacecraft propellant tanks is usually the primary source of energy dissipation. For analytical prediction of the NTC this fuel slosh is commonly modeled using simple mechanical analogies such as pendulums or rigid rotors coupled to the spacecraft. Identifying model parameter values which adequately represent the sloshing dynamics is the most important step in obtaining an accurate NTC estimate. Analytic determination of the slosh model parameters has met with mixed success and is made even more difficult by the introduction of propellant management devices and elastomeric diaphragms. By subjecting full-sized fuel tanks with actual flight fuel loads to motion similar to that experienced in flight and measuring the forces experienced by the tanks these parameters can be determined experimentally. Currently, the identification of the model parameters is a laborious trial-and-error process in which the equations of motion for the mechanical analog are hand-derived, evaluated, and their results are compared with the experimental results. The proposed research is an effort to automate the process of identifying the parameters of the slosh model using a MATLAB/SimMechanics-based computer simulation of the experimental setup. Different parameter estimation and optimization approaches are evaluated and compared in order to arrive at a reliable and effective parameter identification process. To evaluate each parameter identification approach, a simple one-degree-of-freedom pendulum experiment is constructed and motion is induced using an electric motor. By applying the estimation approach to a simple, accurately modeled system, its effectiveness and accuracy can be evaluated. The same experimental setup can then be used with fluid-filled tanks to further evaluate the effectiveness of the process. Ultimately, the proven process can be applied to the full-sized spinning experimental setup to quickly and accurately determine the slosh model parameters for a particular spacecraft mission. Automating the parameter identification process will save time, allow more changes to be made to proposed designs, and lower the cost in the initial design stages.

Parameter estimation for terrain modeling from gradient data. [navigation system for Martian rover

NASA Technical Reports Server (NTRS)

Dangelo, K. R.

1974-01-01

A method is developed for modeling terrain surfaces for use on an unmanned Martian roving vehicle. The modeling procedure employs a two-step process which uses gradient as well as height data in order to improve the accuracy of the model's gradient. Least square approximation is used in order to stochastically determine the parameters which describe the modeled surface. A complete error analysis of the modeling procedure is included which determines the effect of instrumental measurement errors on the model's accuracy. Computer simulation is used as a means of testing the entire modeling process which includes the acquisition of data points, the two-step modeling process and the error analysis. Finally, to illustrate the procedure, a numerical example is included.

Determination of elastomeric foam parameters for simulations of complex loading.

PubMed

Petre, M T; Erdemir, A; Cavanagh, P R

2006-08-01

Finite element (FE) analysis has shown promise for the evaluation of elastomeric foam personal protection devices. Although appropriate representation of foam materials is necessary in order to obtain realistic simulation results, material definitions used in the literature vary widely and often fail to account for the multi-mode loading experienced by these devices. This study aims to provide a library of elastomeric foam material parameters that can be used in FE simulations of complex loading scenarios. Twelve foam materials used in footwear were tested in uni-axial compression, simple shear and volumetric compression. For each material, parameters for a common compressible hyperelastic material model used in FE analysis were determined using: (a) compression; (b) compression and shear data; and (c) data from all three tests. Material parameters and Drucker stability limits for the best fits are provided with their associated errors. The material model was able to reproduce deformation modes for which data was provided during parameter determination but was unable to predict behavior in other deformation modes. Simulation results were found to be highly dependent on the extent of the test data used to determine the parameters in the material definition. This finding calls into question the many published results of simulations of complex loading that use foam material parameters obtained from a single mode of testing. The library of foam parameters developed here presents associated errors in three deformation modes that should provide for a more informed selection of material parameters.

Modelling of slaughterhouse solid waste anaerobic digestion: determination of parameters and continuous reactor simulation.

PubMed

López, Iván; Borzacconi, Liliana

2010-10-01

A model based on the work of Angelidaki et al. (1993) was applied to simulate the anaerobic biodegradation of ruminal contents. In this study, two fractions of solids with different biodegradation rates were considered. A first-order kinetic was used for the easily biodegradable fraction and a kinetic expression that is function of the extracellular enzyme concentration was used for the slowly biodegradable fraction. Batch experiments were performed to obtain an accumulated methane curve that was then used to obtain the model parameters. For this determination, a methodology derived from the "multiple-shooting" method was successfully used. Monte Carlo simulations allowed a confidence range to be obtained for each parameter. Simulations of a continuous reactor were performed using the optimal set of model parameters. The final steady-states were determined as functions of the operational conditions (solids load and residence time). The simulations showed that methane flow peaked at a flow rate of 0.5-0.8 Nm(3)/d/m(reactor)(3) at a residence time of 10-20 days. Simulations allow the adequate selection of operating conditions of a continuous reactor. (c) 2010 Elsevier Ltd. All rights reserved.

Structural identifiability of cyclic graphical models of biological networks with latent variables.

PubMed

Wang, Yulin; Lu, Na; Miao, Hongyu

2016-06-13

Graphical models have long been used to describe biological networks for a variety of important tasks such as the determination of key biological parameters, and the structure of graphical model ultimately determines whether such unknown parameters can be unambiguously obtained from experimental observations (i.e., the identifiability problem). Limited by resources or technical capacities, complex biological networks are usually partially observed in experiment, which thus introduces latent variables into the corresponding graphical models. A number of previous studies have tackled the parameter identifiability problem for graphical models such as linear structural equation models (SEMs) with or without latent variables. However, the limited resolution and efficiency of existing approaches necessarily calls for further development of novel structural identifiability analysis algorithms. An efficient structural identifiability analysis algorithm is developed in this study for a broad range of network structures. The proposed method adopts the Wright's path coefficient method to generate identifiability equations in forms of symbolic polynomials, and then converts these symbolic equations to binary matrices (called identifiability matrix). Several matrix operations are introduced for identifiability matrix reduction with system equivalency maintained. Based on the reduced identifiability matrices, the structural identifiability of each parameter is determined. A number of benchmark models are used to verify the validity of the proposed approach. Finally, the network module for influenza A virus replication is employed as a real example to illustrate the application of the proposed approach in practice. The proposed approach can deal with cyclic networks with latent variables. The key advantage is that it intentionally avoids symbolic computation and is thus highly efficient. Also, this method is capable of determining the identifiability of each single parameter and is thus of higher resolution in comparison with many existing approaches. Overall, this study provides a basis for systematic examination and refinement of graphical models of biological networks from the identifiability point of view, and it has a significant potential to be extended to more complex network structures or high-dimensional systems.

Quality by design: scale-up of freeze-drying cycles in pharmaceutical industry.

PubMed

Pisano, Roberto; Fissore, Davide; Barresi, Antonello A; Rastelli, Massimo

2013-09-01

This paper shows the application of mathematical modeling to scale-up a cycle developed with lab-scale equipment on two different production units. The above method is based on a simplified model of the process parameterized with experimentally determined heat and mass transfer coefficients. In this study, the overall heat transfer coefficient between product and shelf was determined by using the gravimetric procedure, while the dried product resistance to vapor flow was determined through the pressure rise test technique. Once model parameters were determined, the freeze-drying cycle of a parenteral product was developed via dynamic design space for a lab-scale unit. Then, mathematical modeling was used to scale-up the above cycle in the production equipment. In this way, appropriate values were determined for processing conditions, which allow the replication, in the industrial unit, of the product dynamics observed in the small scale freeze-dryer. This study also showed how inter-vial variability, as well as model parameter uncertainty, can be taken into account during scale-up calculations.

Kernel learning at the first level of inference.

PubMed

Cawley, Gavin C; Talbot, Nicola L C

2014-05-01

Kernel learning methods, whether Bayesian or frequentist, typically involve multiple levels of inference, with the coefficients of the kernel expansion being determined at the first level and the kernel and regularisation parameters carefully tuned at the second level, a process known as model selection. Model selection for kernel machines is commonly performed via optimisation of a suitable model selection criterion, often based on cross-validation or theoretical performance bounds. However, if there are a large number of kernel parameters, as for instance in the case of automatic relevance determination (ARD), there is a substantial risk of over-fitting the model selection criterion, resulting in poor generalisation performance. In this paper we investigate the possibility of learning the kernel, for the Least-Squares Support Vector Machine (LS-SVM) classifier, at the first level of inference, i.e. parameter optimisation. The kernel parameters and the coefficients of the kernel expansion are jointly optimised at the first level of inference, minimising a training criterion with an additional regularisation term acting on the kernel parameters. The key advantage of this approach is that the values of only two regularisation parameters need be determined in model selection, substantially alleviating the problem of over-fitting the model selection criterion. The benefits of this approach are demonstrated using a suite of synthetic and real-world binary classification benchmark problems, where kernel learning at the first level of inference is shown to be statistically superior to the conventional approach, improves on our previous work (Cawley and Talbot, 2007) and is competitive with Multiple Kernel Learning approaches, but with reduced computational expense. Copyright © 2014 Elsevier Ltd. All rights reserved.

Physically based model for extracting dual permeability parameters using non-Newtonian fluids

NASA Astrophysics Data System (ADS)

Abou Najm, M. R.; Basset, C.; Stewart, R. D.; Hauswirth, S.

2017-12-01

Dual permeability models are effective for the assessment of flow and transport in structured soils with two dominant structures. The major challenge to those models remains in the ability to determine appropriate and unique parameters through affordable, simple, and non-destructive methods. This study investigates the use of water and a non-Newtonian fluid in saturated flow experiments to derive physically-based parameters required for improved flow predictions using dual permeability models. We assess the ability of these two fluids to accurately estimate the representative pore sizes in dual-domain soils, by determining the effective pore sizes of macropores and micropores. We developed two sub-models that solve for the effective macropore size assuming either cylindrical (e.g., biological pores) or planar (e.g., shrinkage cracks and fissures) pore geometries, with the micropores assumed to be represented by a single effective radius. Furthermore, the model solves for the percent contribution to flow (wi) corresponding to the representative macro and micro pores. A user-friendly solver was developed to numerically solve the system of equations, given that relevant non-Newtonian viscosity models lack forms conducive to analytical integration. The proposed dual-permeability model is a unique attempt to derive physically based parameters capable of measuring dual hydraulic conductivities, and therefore may be useful in reducing parameter uncertainty and improving hydrologic model predictions.

Error propagation of partial least squares for parameters optimization in NIR modeling.

PubMed

Du, Chenzhao; Dai, Shengyun; Qiao, Yanjiang; Wu, Zhisheng

2018-03-05

A novel methodology is proposed to determine the error propagation of partial least-square (PLS) for parameters optimization in near-infrared (NIR) modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset (corn) and a complicated dataset (Gardenia) were used to establish PLS models under different modeling parameters. And error propagation of modeling parameters for water quantity in corn and geniposide quantity in Gardenia were presented by both type І and type II error. For example, when variable importance in the projection (VIP), interval partial least square (iPLS) and backward interval partial least square (BiPLS) variable selection algorithms were used for geniposide in Gardenia, compared with synergy interval partial least squares (SiPLS), the error weight varied from 5% to 65%, 55% and 15%. The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters. Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models. Copyright © 2017. Published by Elsevier B.V.

Error propagation of partial least squares for parameters optimization in NIR modeling

NASA Astrophysics Data System (ADS)

Du, Chenzhao; Dai, Shengyun; Qiao, Yanjiang; Wu, Zhisheng

2018-03-01

A novel methodology is proposed to determine the error propagation of partial least-square (PLS) for parameters optimization in near-infrared (NIR) modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset (corn) and a complicated dataset (Gardenia) were used to establish PLS models under different modeling parameters. And error propagation of modeling parameters for water quantity in corn and geniposide quantity in Gardenia were presented by both type І and type II error. For example, when variable importance in the projection (VIP), interval partial least square (iPLS) and backward interval partial least square (BiPLS) variable selection algorithms were used for geniposide in Gardenia, compared with synergy interval partial least squares (SiPLS), the error weight varied from 5% to 65%, 55% and 15%. The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters. Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models.

Influence of different dose calculation algorithms on the estimate of NTCP for lung complications

PubMed Central

Bäck, Anna

2013-01-01

Due to limitations and uncertainties in dose calculation algorithms, different algorithms can predict different dose distributions and dose‐volume histograms for the same treatment. This can be a problem when estimating the normal tissue complication probability (NTCP) for patient‐specific dose distributions. Published NTCP model parameters are often derived for a different dose calculation algorithm than the one used to calculate the actual dose distribution. The use of algorithm‐specific NTCP model parameters can prevent errors caused by differences in dose calculation algorithms. The objective of this work was to determine how to change the NTCP model parameters for lung complications derived for a simple correction‐based pencil beam dose calculation algorithm, in order to make them valid for three other common dose calculation algorithms. NTCP was calculated with the relative seriality (RS) and Lyman‐Kutcher‐Burman (LKB) models. The four dose calculation algorithms used were the pencil beam (PB) and collapsed cone (CC) algorithms employed by Oncentra, and the pencil beam convolution (PBC) and anisotropic analytical algorithm (AAA) employed by Eclipse. Original model parameters for lung complications were taken from four published studies on different grades of pneumonitis, and new algorithm‐specific NTCP model parameters were determined. The difference between original and new model parameters was presented in relation to the reported model parameter uncertainties. Three different types of treatments were considered in the study: tangential and locoregional breast cancer treatment and lung cancer treatment. Changing the algorithm without the derivation of new model parameters caused changes in the NTCP value of up to 10 percentage points for the cases studied. Furthermore, the error introduced could be of the same magnitude as the confidence intervals of the calculated NTCP values. The new NTCP model parameters were tabulated as the algorithm was varied from PB to PBC, AAA, or CC. Moving from the PB to the PBC algorithm did not require new model parameters; however, moving from PB to AAA or CC did require a change in the NTCP model parameters, with CC requiring the largest change. It was shown that the new model parameters for a given algorithm are different for the different treatment types. PACS numbers: 87.53.‐j, 87.53.Kn, 87.55.‐x, 87.55.dh, 87.55.kd PMID:24036865

Application of maximum entropy to statistical inference for inversion of data from a single track segment.

PubMed

Stotts, Steven A; Koch, Robert A

2017-08-01

In this paper an approach is presented to estimate the constraint required to apply maximum entropy (ME) for statistical inference with underwater acoustic data from a single track segment. Previous algorithms for estimating the ME constraint require multiple source track segments to determine the constraint. The approach is relevant for addressing model mismatch effects, i.e., inaccuracies in parameter values determined from inversions because the propagation model does not account for all acoustic processes that contribute to the measured data. One effect of model mismatch is that the lowest cost inversion solution may be well outside a relatively well-known parameter value's uncertainty interval (prior), e.g., source speed from track reconstruction or towed source levels. The approach requires, for some particular parameter value, the ME constraint to produce an inferred uncertainty interval that encompasses the prior. Motivating this approach is the hypothesis that the proposed constraint determination procedure would produce a posterior probability density that accounts for the effect of model mismatch on inferred values of other inversion parameters for which the priors might be quite broad. Applications to both measured and simulated data are presented for model mismatch that produces minimum cost solutions either inside or outside some priors.

The multiscale coarse-graining method. II. Numerical implementation for coarse-grained molecular models

PubMed Central

Noid, W. G.; Liu, Pu; Wang, Yanting; Chu, Jhih-Wei; Ayton, Gary S.; Izvekov, Sergei; Andersen, Hans C.; Voth, Gregory A.

2008-01-01

The multiscale coarse-graining (MS-CG) method [S. Izvekov and G. A. Voth, J. Phys. Chem. B 109, 2469 (2005);J. Chem. Phys. 123, 134105 (2005)] employs a variational principle to determine an interaction potential for a CG model from simulations of an atomically detailed model of the same system. The companion paper proved that, if no restrictions regarding the form of the CG interaction potential are introduced and if the equilibrium distribution of the atomistic model has been adequately sampled, then the MS-CG variational principle determines the exact many-body potential of mean force (PMF) governing the equilibrium distribution of CG sites generated by the atomistic model. In practice, though, CG force fields are not completely flexible, but only include particular types of interactions between CG sites, e.g., nonbonded forces between pairs of sites. If the CG force field depends linearly on the force field parameters, then the vector valued functions that relate the CG forces to these parameters determine a set of basis vectors that span a vector subspace of CG force fields. The companion paper introduced a distance metric for the vector space of CG force fields and proved that the MS-CG variational principle determines the CG force force field that is within that vector subspace and that is closest to the force field determined by the many-body PMF. The present paper applies the MS-CG variational principle for parametrizing molecular CG force fields and derives a linear least squares problem for the parameter set determining the optimal approximation to this many-body PMF. Linear systems of equations for these CG force field parameters are derived and analyzed in terms of equilibrium structural correlation functions. Numerical calculations for a one-site CG model of methanol and a molecular CG model of the EMIM+∕NO3− ionic liquid are provided to illustrate the method. PMID:18601325

Comparative assessment of five water infiltration models into the soil

NASA Astrophysics Data System (ADS)

Shahsavaramir, M.

2009-04-01

The knowledge of the soil hydraulic conditions particularly soil permeability is an important issue hydrological and climatic study. Because of its high spatial and temporal variability, soil infiltration monitoring scheme was investigated in view of its application in infiltration modelling. Some of models for infiltration into the soil have been developed, in this paper; we design and describe capability of five infiltration model into the soil. We took a decision to select the best model suggested. In this research in the first time, we designed a program in Quick Basic software and wrote algorithm of five models that include Kostiakove, Modified Kostiakove, Philip, S.C.S and Horton. Afterwards we supplied amounts of factual infiltration, according of get at infiltration data, by double rings method in 12 series of Saveh plain which situated in Markazi province in Iran. After accessing to models coefficients, these equations were regenerated by Excel software and calculations related to models acuity rate in proportion to observations and also related graphs were done by this software. Amounts of infiltration parameters, such as cumulative infiltration and infiltration rate were obtained from designed models. Then we compared amounts of observation and determination parameters of infiltration. The results show that Kostiakove and Modified Kostiakove models could quantify amounts of cumulative infiltration and infiltration rate in triple period (short, middle and long time). In tree series of soils, Horton model could determine infiltration amounts better than others in time trinal treatments. The results show that Philip model in seven series had a relatively good fitness for determination of infiltration parameters. Also Philip model in five series of soils, after passing of time, had curve shape; in fact this shown that attraction coefficient (s) was less than zero. After all S.C.S model among of others had the least capability to determination of infiltration parameters.

Benzoic Acid and Chlorobenzoic Acids: Thermodynamic Study of the Pure Compounds and Binary Mixtures With Water.

PubMed

Reschke, Thomas; Zherikova, Kseniya V; Verevkin, Sergey P; Held, Christoph

2016-03-01

Benzoic acid is a model compound for drug substances in pharmaceutical research. Process design requires information about thermodynamic phase behavior of benzoic acid and its mixtures with water and organic solvents. This work addresses phase equilibria that determine stability and solubility. In this work, Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) was used to model the phase behavior of aqueous and organic solutions containing benzoic acid and chlorobenzoic acids. Absolute vapor pressures of benzoic acid and 2-, 3-, and 4-chlorobenzoic acid from literature and from our own measurements were used to determine pure-component PC-SAFT parameters. Two binary interaction parameters between water and/or benzoic acid were used to model vapor-liquid and liquid-liquid equilibria of water and/or benzoic acid between 280 and 413 K. The PC-SAFT parameters and 1 binary interaction parameter were used to model aqueous solubility of the chlorobenzoic acids. Additionally, solubility of benzoic acid in organic solvents was predicted without using binary parameters. All results showed that pure-component parameters for benzoic acid and for the chlorobenzoic acids allowed for satisfying modeling phase equilibria. The modeling approach established in this work is a further step to screen solubility and to predict the whole phase region of mixtures containing pharmaceuticals. Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Model-based POD study of manual ultrasound inspection and sensitivity analysis using metamodel

NASA Astrophysics Data System (ADS)

Ribay, Guillemette; Artusi, Xavier; Jenson, Frédéric; Reece, Christopher; Lhuillier, Pierre-Emile

2016-02-01

The reliability of NDE can be quantified by using the Probability of Detection (POD) approach. Former studies have shown the potential of the model-assisted POD (MAPOD) approach to replace expensive experimental determination of POD curves. In this paper, we make use of CIVA software to determine POD curves for a manual ultrasonic inspection of a heavy component, for which a whole experimental POD campaign was not available. The influential parameters were determined by expert analysis. The semi-analytical models used in CIVA for wave propagation and beam-defect interaction have been validated in the range of variation of the influential parameters by comparison with finite element modelling (Athena). The POD curves are computed for « hit/miss » and « â versus a » analysis. The verification of Berens hypothesis is evaluated by statistical tools. A sensitivity study is performed to measure the relative influence of parameters on the defect response amplitude variance, using the Sobol sensitivity index. A meta-model is also built to reduce computing cost and enhance the precision of estimated index.

Determination of Littlest Higgs Model Parameters at the ILC

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

Conley, John A.; Hewett, JoAnne; Le, My Phuong

2005-07-27

We examine the effects of the extended gauge sector of the Littlest Higgs model in high energy e{sup +}e{sup -} collisions. We find that the search reach in e{sup +}e{sup -} {yields} f{bar f} at a {radical}s = 500 GeV International Linear Collider covers essentially the entire parameter region where the Littlest Higgs model is relevant to the gauge hierarchy problem. In addition, we show that this channel provides an accurate determination of the fundamental model parameters, to the precision of a few percent, provided that the LHC measures the mass of the heavy neutral gauge .eld. Additionally, we showmore » that the couplings of the extra gauge bosons to the light Higgs can be observed from the process e{sup +}e{sup -} {yields} Zh for a significant region of the parameter space. This allows for confirmation of the structure of the cancellation of the Higgs mass quadratic divergence and would verify the little Higgs mechanism.« less

The reconstruction of tachyon inflationary potentials

NASA Astrophysics Data System (ADS)

Fei, Qin; Gong, Yungui; Lin, Jiong; Yi, Zhu

2017-08-01

We derive a lower bound on the field excursion for the tachyon inflation, which is determined by the amplitude of the scalar perturbation and the number of e-folds before the end of inflation. Using the relation between the observables like ns and r with the slow-roll parameters, we reconstruct three classes of tachyon potentials. The model parameters are determined from the observations before the potentials are reconstructed, and the observations prefer the concave potential. We also discuss the constraints from the reheating phase preceding the radiation domination for the three classes of models by assuming the equation of state parameter wre during reheating is a constant. Depending on the model parameters and the value of wre, the constraints on Nre and Tre are different. As ns increases, the allowed reheating epoch becomes longer for wre=-1/3, 0 and 1/6 while the allowed reheating epoch becomes shorter for wre=2/3.

A Modified Johnson-Cook Model for Sheet Metal Forming at Elevated Temperatures and Its Application for Cooled Stress-Strain Curve and Spring-Back Prediction

NASA Astrophysics Data System (ADS)

Duc-Toan, Nguyen; Tien-Long, Banh; Young-Suk, Kim; Dong-Won, Jung

2011-08-01

In this study, a modified Johnson-Cook (J-C) model and an innovated method to determine (J-C) material parameters are proposed to predict more correctly stress-strain curve for tensile tests in elevated temperatures. A MATLAB tool is used to determine material parameters by fitting a curve to follow Ludwick's hardening law at various elevated temperatures. Those hardening law parameters are then utilized to determine modified (J-C) model material parameters. The modified (J-C) model shows the better prediction compared to the conventional one. As the first verification, an FEM tensile test simulation based on the isotropic hardening model for boron sheet steel at elevated temperatures was carried out via a user-material subroutine, using an explicit finite element code, and compared with the measurements. The temperature decrease of all elements due to the air cooling process was then calculated when considering the modified (J-C) model and coded to VUMAT subroutine for tensile test simulation of cooling process. The modified (J-C) model showed the good agreement between the simulation results and the corresponding experiments. The second investigation was applied for V-bending spring-back prediction of magnesium alloy sheets at elevated temperatures. Here, the combination of proposed J-C model with modified hardening law considering the unusual plastic behaviour for magnesium alloy sheet was adopted for FEM simulation of V-bending spring-back prediction and shown the good comparability with corresponding experiments.

A new approach to the extraction of single exponential diode model parameters

NASA Astrophysics Data System (ADS)

Ortiz-Conde, Adelmo; García-Sánchez, Francisco J.

2018-06-01

A new integration method is presented for the extraction of the parameters of a single exponential diode model with series resistance from the measured forward I-V characteristics. The extraction is performed using auxiliary functions based on the integration of the data which allow to isolate the effects of each of the model parameters. A differentiation method is also presented for data with low level of experimental noise. Measured and simulated data are used to verify the applicability of both proposed method. Physical insight about the validity of the model is also obtained by using the proposed graphical determinations of the parameters.

Normal Values for Heart Electrophysiology Parameters of Healthy Swine Determined on Electrophysiology Study.

PubMed

Noszczyk-Nowak, Agnieszka; Cepiel, Alicja; Janiszewski, Adrian; Pasławski, Robert; Gajek, Jacek; Pasławska, Urszula; Nicpoń, Józef

2016-01-01

Swine are a well-recognized animal model for human cardiovascular diseases. Despite the widespread use of porcine model in experimental electrophysiology, still no reference values for intracardiac electrical activity and conduction parameters determined during an invasive electrophysiology study (EPS) have been developed in this species thus far. The aim of the study was to develop a set of normal values for intracardiac electrical activity and conduction parameters determined during an invasive EPS of swine. The study included 36 healthy domestic swine (24-40 kg body weight). EPS was performed under a general anesthesia with midazolam, propofol and isoflurane. The reference values for intracardiac electrical activity and conduction parameters were calculated as arithmetic means ± 2 standard deviations. The reference values were determined for AH, HV and PA intervals, interatrial conduction time at its own and imposed rhythm, sinus node recovery time (SNRT), corrected sinus node recovery time (CSNRT), anterograde and retrograde Wenckebach points, atrial, atrioventricular node and ventricular refractory periods. No significant correlations were found between body weight and heart rate of the examined pigs and their electrophysiological parameters. The hereby presented reference values can be helpful in comparing the results of various studies, as well as in more accurately estimating the values of electrophysiological parameters that can be expected in a given experiment.

Development of analysis technique to predict the material behavior of blowing agent

NASA Astrophysics Data System (ADS)

Hwang, Ji Hoon; Lee, Seonggi; Hwang, So Young; Kim, Naksoo

2014-11-01

In order to numerically simulate the foaming behavior of mastic sealer containing the blowing agent, a foaming and driving force model are needed which incorporate the foaming characteristics. Also, the elastic stress model is required to represent the material behavior of co-existing phase of liquid state and the cured polymer. It is important to determine the thermal properties such as thermal conductivity and specific heat because foaming behavior is heavily influenced by temperature change. In this study, three models are proposed to explain the foaming process and material behavior during and after the process. To obtain the material parameters in each model, following experiments and the numerical simulations are performed: thermal test, simple shear test and foaming test. The error functions are defined as differences between the experimental measurements and the numerical simulation results, and then the parameters are determined by minimizing the error functions. To ensure the validity of the obtained parameters, the confirmation simulation for each model is conducted by applying the determined parameters. The cross-verification is performed by measuring the foaming/shrinkage force. The results of cross-verification tended to follow the experimental results. Interestingly, it was possible to estimate the micro-deformation occurring in automobile roof surface by applying the proposed model to oven process analysis. The application of developed analysis technique will contribute to the design with minimized micro-deformation.

Differential Evolution algorithm applied to FSW model calibration

NASA Astrophysics Data System (ADS)

Idagawa, H. S.; Santos, T. F. A.; Ramirez, A. J.

2014-03-01

Friction Stir Welding (FSW) is a solid state welding process that can be modelled using a Computational Fluid Dynamics (CFD) approach. These models use adjustable parameters to control the heat transfer and the heat input to the weld. These parameters are used to calibrate the model and they are generally determined using the conventional trial and error approach. Since this method is not very efficient, we used the Differential Evolution (DE) algorithm to successfully determine these parameters. In order to improve the success rate and to reduce the computational cost of the method, this work studied different characteristics of the DE algorithm, such as the evolution strategy, the objective function, the mutation scaling factor and the crossover rate. The DE algorithm was tested using a friction stir weld performed on a UNS S32205 Duplex Stainless Steel.

Geophysical technique for mineral exploration and discrimination based on electromagnetic methods and associated systems

DOEpatents

Zhdanov,; Michael, S [Salt Lake City, UT

2008-01-29

Mineral exploration needs a reliable method to distinguish between uneconomic mineral deposits and economic mineralization. A method and system includes a geophysical technique for subsurface material characterization, mineral exploration and mineral discrimination. The technique introduced in this invention detects induced polarization effects in electromagnetic data and uses remote geophysical observations to determine the parameters of an effective conductivity relaxation model using a composite analytical multi-phase model of the rock formations. The conductivity relaxation model and analytical model can be used to determine parameters related by analytical expressions to the physical characteristics of the microstructure of the rocks and minerals. These parameters are ultimately used for the discrimination of different components in underground formations, and in this way provide an ability to distinguish between uneconomic mineral deposits and zones of economic mineralization using geophysical remote sensing technology.

Determination of remodeling parameters for a strain-adaptive finite element model of the distal ulna.

PubMed

Neuert, Mark A C; Dunning, Cynthia E

2013-09-01

Strain energy-based adaptive material models are used to predict bone resorption resulting from stress shielding induced by prosthetic joint implants. Generally, such models are governed by two key parameters: a homeostatic strain-energy state (K) and a threshold deviation from this state required to initiate bone reformation (s). A refinement procedure has been performed to estimate these parameters in the femur and glenoid; this study investigates the specific influences of these parameters on resulting density distributions in the distal ulna. A finite element model of a human ulna was created using micro-computed tomography (µCT) data, initialized to a homogeneous density distribution, and subjected to approximate in vivo loading. Values for K and s were tested, and the resulting steady-state density distribution compared with values derived from µCT images. The sensitivity of these parameters to initial conditions was examined by altering the initial homogeneous density value. The refined model parameters selected were then applied to six additional human ulnae to determine their performance across individuals. Model accuracy using the refined parameters was found to be comparable with that found in previous studies of the glenoid and femur, and gross bone structures, such as the cortical shell and medullary canal, were reproduced. The model was found to be insensitive to initial conditions; however, a fair degree of variation was observed between the six specimens. This work represents an important contribution to the study of changes in load transfer in the distal ulna following the implementation of commercial orthopedic implants.

Aspen succession in the Intermountain West: A deterministic model

Treesearch

Dale L. Bartos; Frederick R. Ward; George S. Innis

1983-01-01

A deterministic model of succession in aspen forests was developed using existing data and intuition. The degree of uncertainty, which was determined by allowing the parameter values to vary at random within limits, was larger than desired. This report presents results of an analysis of model sensitivity to changes in parameter values. These results have indicated...

Estimation and upscaling of dual-permeability model parameters for the transport of E.coli D21g in soils with preferential flow

USDA-ARS?s Scientific Manuscript database

Dual-permeability models are increasingly used to quantify the transport of solutes and microorganisms in soils with preferential flow. An ability to accurately determine the model parameters and their variation with preferential pathway characteristics is crucial for predicting the transport of mi...

Application of Time-series Model to Predict Groundwater Quality Parameters for Agriculture: (Plain Mehran Case Study)

NASA Astrophysics Data System (ADS)

Mehrdad Mirsanjari, Mir; Mohammadyari, Fatemeh

2018-03-01

Underground water is regarded as considerable water source which is mainly available in arid and semi arid with deficient surface water source. Forecasting of hydrological variables are suitable tools in water resources management. On the other hand, time series concepts is considered efficient means in forecasting process of water management. In this study the data including qualitative parameters (electrical conductivity and sodium adsorption ratio) of 17 underground water wells in Mehran Plain has been used to model the trend of parameters change over time. Using determined model, the qualitative parameters of groundwater is predicted for the next seven years. Data from 2003 to 2016 has been collected and were fitted by AR, MA, ARMA, ARIMA and SARIMA models. Afterward, the best model is determined using information criterion or Akaike (AIC) and correlation coefficient. After modeling parameters, the map of agricultural land use in 2016 and 2023 were generated and the changes between these years were studied. Based on the results, the average of predicted SAR (Sodium Adsorption Rate) in all wells in the year 2023 will increase compared to 2016. EC (Electrical Conductivity) average in the ninth and fifteenth holes and decreases in other wells will be increased. The results indicate that the quality of groundwater for Agriculture Plain Mehran will decline in seven years.

O-star parameters from line profiles of wind-blanketed model atmospheres

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

Voels, S.A.

1989-01-01

The basic stellar parameters (i.e. effective temperature, gravity, helium content, bolometric correction, etc...) of several O-stars are determined by matching high signal-to-noise observed line profiles of optical hydrogen and helium line transitions with theoretical line profiles from a core-halo model of the stellar atmosphere. The core-halo atmosphere includes the effect of radiation backscattered from a stellar wind by incorporating the stellar wind model of Abbott and Lucy as a reflective upper boundary condition in the Mihalas atmosphere model. Three of the four supergiants analyzed showed an enhanced surface abundance of helium. Using a large sample of equivalent width data frommore » Conti a simple argument is made that surface enhancement of helium may be a common property of the most luminous supergiants. The stellar atmosphere theory is sufficient to determine the stellar parameters only if careful attention is paid to the detection and exclusion of lines which are not accurately modeled by the physical processes included. It was found that some strong lines which form entirely below the sonic point are not well modeled due to effects of atmospheric extension. For spectral class 09.5, one of these lines is the classification line He I {lambda}4471{angstrom}. For supergiant, the gravity determined could be systematically low by up to 0.05 dex as the radiation pressure due to lines is neglected. Within the error ranges, the stellar parameters determined, including helium abundance, agree with those from the stellar evolution calculations of Maeder and Maynet.« less

Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model

NASA Astrophysics Data System (ADS)

Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami

2017-06-01

A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.

An automated method of tuning an attitude estimator

NASA Technical Reports Server (NTRS)

Mason, Paul A. C.; Mook, D. Joseph

1995-01-01

Attitude determination is a major element of the operation and maintenance of a spacecraft. There are several existing methods of determining the attitude of a spacecraft. One of the most commonly used methods utilizes the Kalman filter to estimate the attitude of the spacecraft. Given an accurate model of a system and adequate observations, a Kalman filter can produce accurate estimates of the attitude. If the system model, filter parameters, or observations are inaccurate, the attitude estimates may be degraded. Therefore, it is advantageous to develop a method of automatically tuning the Kalman filter to produce the accurate estimates. In this paper, a three-axis attitude determination Kalman filter, which uses only magnetometer measurements, is developed and tested using real data. The appropriate filter parameters are found via the Process Noise Covariance Estimator (PNCE). The PNCE provides an optimal criterion for determining the best filter parameters.

Fisher Scoring Method for Parameter Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model

NASA Astrophysics Data System (ADS)

Widyaningsih, Purnami; Retno Sari Saputro, Dewi; Nugrahani Putri, Aulia

2017-06-01

GWOLR model combines geographically weighted regression (GWR) and (ordinal logistic reression) OLR models. Its parameter estimation employs maximum likelihood estimation. Such parameter estimation, however, yields difficult-to-solve system of nonlinear equations, and therefore numerical approximation approach is required. The iterative approximation approach, in general, uses Newton-Raphson (NR) method. The NR method has a disadvantage—its Hessian matrix is always the second derivatives of each iteration so it does not always produce converging results. With regard to this matter, NR model is modified by substituting its Hessian matrix into Fisher information matrix, which is termed Fisher scoring (FS). The present research seeks to determine GWOLR model parameter estimation using Fisher scoring method and apply the estimation on data of the level of vulnerability to Dengue Hemorrhagic Fever (DHF) in Semarang. The research concludes that health facilities give the greatest contribution to the probability of the number of DHF sufferers in both villages. Based on the number of the sufferers, IR category of DHF in both villages can be determined.

A GUI-based Tool for Bridging the Gap between Models and Process-Oriented Studies

NASA Astrophysics Data System (ADS)

Kornfeld, A.; Van der Tol, C.; Berry, J. A.

2014-12-01

Models used for simulation of photosynthesis and transpiration by canopies of terrestrial plants typically have subroutines such as STOMATA.F90, PHOSIB.F90 or BIOCHEM.m that solve for photosynthesis and associated processes. Key parameters such as the Vmax for Rubisco and temperature response parameters are required by these subroutines. These are often taken from the literature or determined by separate analysis of gas exchange experiments. It is useful to note however that subroutines can be extracted and run as standalone models to simulate leaf responses collected in gas exchange experiments. Furthermore, there are excellent non-linear fitting tools that can be used to optimize the parameter values in these models to fit the observations. Ideally the Vmax fit in this way should be the same as that determined by a separate analysis, but it may not because of interactions with other kinetic constants and the temperature dependence of these in the full subroutine. We submit that it is more useful to fit the complete model to the calibration experiments rather as disaggregated constants. We designed a graphical user interface (GUI) based tool that uses gas exchange photosynthesis data to directly estimate model parameters in the SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes) model and, at the same time, allow researchers to change parameters interactively to visualize how variation in model parameters affect predicted outcomes such as photosynthetic rates, electron transport, and chlorophyll fluorescence. We have also ported some of this functionality to an Excel spreadsheet, which could be used as a teaching tool to help integrate process-oriented and model-oriented studies.

Sensitivity of land surface modeling to parameters: An uncertainty quantification method applied to the Community Land Model

NASA Astrophysics Data System (ADS)

Ricciuto, D. M.; Mei, R.; Mao, J.; Hoffman, F. M.; Kumar, J.

2015-12-01

Uncertainties in land parameters could have important impacts on simulated water and energy fluxes and land surface states, which will consequently affect atmospheric and biogeochemical processes. Therefore, quantification of such parameter uncertainties using a land surface model is the first step towards better understanding of predictive uncertainty in Earth system models. In this study, we applied a random-sampling, high-dimensional model representation (RS-HDMR) method to analyze the sensitivity of simulated photosynthesis, surface energy fluxes and surface hydrological components to selected land parameters in version 4.5 of the Community Land Model (CLM4.5). Because of the large computational expense of conducting ensembles of global gridded model simulations, we used the results of a previous cluster analysis to select one thousand representative land grid cells for simulation. Plant functional type (PFT)-specific uniform prior ranges for land parameters were determined using expert opinion and literature survey, and samples were generated with a quasi-Monte Carlo approach-Sobol sequence. Preliminary analysis of 1024 simulations suggested that four PFT-dependent parameters (including slope of the conductance-photosynthesis relationship, specific leaf area at canopy top, leaf C:N ratio and fraction of leaf N in RuBisco) are the dominant sensitive parameters for photosynthesis, surface energy and water fluxes across most PFTs, but with varying importance rankings. On the other hand, for surface ans sub-surface runoff, PFT-independent parameters, such as the depth-dependent decay factors for runoff, play more important roles than the previous four PFT-dependent parameters. Further analysis by conditioning the results on different seasons and years are being conducted to provide guidance on how climate variability and change might affect such sensitivity. This is the first step toward coupled simulations including biogeochemical processes, atmospheric processes or both to determine the full range of sensitivity of Earth system modeling to land-surface parameters. This can facilitate sampling strategies in measurement campaigns targeted at reduction of climate modeling uncertainties and can also provide guidance on land parameter calibration for simulation optimization.

Optimal Tuner Selection for Kalman-Filter-Based Aircraft Engine Performance Estimation

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Garg, Sanjay

2011-01-01

An emerging approach in the field of aircraft engine controls and system health management is the inclusion of real-time, onboard models for the inflight estimation of engine performance variations. This technology, typically based on Kalman-filter concepts, enables the estimation of unmeasured engine performance parameters that can be directly utilized by controls, prognostics, and health-management applications. A challenge that complicates this practice is the fact that an aircraft engine s performance is affected by its level of degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. Through Kalman-filter-based estimation techniques, the level of engine performance degradation can be estimated, given that there are at least as many sensors as health parameters to be estimated. However, in an aircraft engine, the number of sensors available is typically less than the number of health parameters, presenting an under-determined estimation problem. A common approach to address this shortcoming is to estimate a subset of the health parameters, referred to as model tuning parameters. The problem/objective is to optimally select the model tuning parameters to minimize Kalman-filterbased estimation error. A tuner selection technique has been developed that specifically addresses the under-determined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine that seeks to minimize the theoretical mean-squared estimation error of the Kalman filter. This approach can significantly reduce the error in onboard aircraft engine parameter estimation applications such as model-based diagnostic, controls, and life usage calculations. The advantage of the innovation is the significant reduction in estimation errors that it can provide relative to the conventional approach of selecting a subset of health parameters to serve as the model tuning parameter vector. Because this technique needs only to be performed during the system design process, it places no additional computation burden on the onboard Kalman filter implementation. The technique has been developed for aircraft engine onboard estimation applications, as this application typically presents an under-determined estimation problem. However, this generic technique could be applied to other industries using gas turbine engine technology.

Practical identifiability analysis of a minimal cardiovascular system model.

PubMed

Pironet, Antoine; Docherty, Paul D; Dauby, Pierre C; Chase, J Geoffrey; Desaive, Thomas

2017-01-17

Parameters of mathematical models of the cardiovascular system can be used to monitor cardiovascular state, such as total stressed blood volume status, vessel elastance and resistance. To do so, the model parameters have to be estimated from data collected at the patient's bedside. This work considers a seven-parameter model of the cardiovascular system and investigates whether these parameters can be uniquely determined using indices derived from measurements of arterial and venous pressures, and stroke volume. An error vector defined the residuals between the simulated and reference values of the seven clinically available haemodynamic indices. The sensitivity of this error vector to each model parameter was analysed, as well as the collinearity between parameters. To assess practical identifiability of the model parameters, profile-likelihood curves were constructed for each parameter. Four of the seven model parameters were found to be practically identifiable from the selected data. The remaining three parameters were practically non-identifiable. Among these non-identifiable parameters, one could be decreased as much as possible. The other two non-identifiable parameters were inversely correlated, which prevented their precise estimation. This work presented the practical identifiability analysis of a seven-parameter cardiovascular system model, from limited clinical data. The analysis showed that three of the seven parameters were practically non-identifiable, thus limiting the use of the model as a monitoring tool. Slight changes in the time-varying function modeling cardiac contraction and use of larger values for the reference range of venous pressure made the model fully practically identifiable. Copyright © 2017. Published by Elsevier B.V.

PERIODIC AUTOREGRESSIVE-MOVING AVERAGE (PARMA) MODELING WITH APPLICATIONS TO WATER RESOURCES.

USGS Publications Warehouse

Vecchia, A.V.

1985-01-01

Results involving correlation properties and parameter estimation for autogressive-moving average models with periodic parameters are presented. A multivariate representation of the PARMA model is used to derive parameter space restrictions and difference equations for the periodic autocorrelations. Close approximation to the likelihood function for Gaussian PARMA processes results in efficient maximum-likelihood estimation procedures. Terms in the Fourier expansion of the parameters are sequentially included, and a selection criterion is given for determining the optimal number of harmonics to be included. Application of the techniques is demonstrated through analysis of a monthly streamflow time series.

A fuzzy rumor spreading model based on transmission capacity

NASA Astrophysics Data System (ADS)

Zhang, Yi; Xu, Jiuping; Wu, Yue

This paper proposes a rumor spreading model that considers three main factors: the event importance, event ambiguity, and the publics critical sense, each of which are defined by decision makers using linguistic descriptions and then transformed into triangular fuzzy numbers. To calculate the resultant force of these three factors, the transmission capacity and a new parameter category with fuzzy variables are determined. A rumor spreading model is then proposed which has fuzzy parameters rather than the fixed parameters in traditional models. As the proposed model considers the comprehensive factors affecting rumors from three aspects rather than examining special factors from a particular aspect. The proposed rumor spreading model is tested using different parameters for several different conditions on BA networks and three special cases are simulated. The simulation results for all three cases suggested that events of low importance, those that are only clarifying facts, and those that are strongly critical do not result in rumors. Therefore, the model assessment results were proven to be in agreement with reality. Parameters for the model were then determined and applied to an analysis of the 7.23 Yong-Wen line major transportation accident (YWMTA). When the simulated data were compared with the real data from this accident, the results demonstrated that the interval for the rumor spreading key point in the model was accurate, and that the key point for the YWMTA rumor spread fell into the range estimated by the model.

One-dimensional velocity model of the Middle Kura Depresion from local earthquakes data of Azerbaijan

NASA Astrophysics Data System (ADS)

Yetirmishli, G. C.; Kazimova, S. E.; Kazimov, I. E.

2011-09-01

We present the method for determining the velocity model of the Earth's crust and the parameters of earthquakes in the Middle Kura Depression from the data of network telemetry in Azerbaijan. Application of this method allowed us to recalculate the main parameters of the hypocenters of the earthquake, to compute the corrections to the arrival times of P and S waves at the observation station, and to significantly improve the accuracy in determining the coordinates of the earthquakes. The model was constructed using the VELEST program, which calculates one-dimensional minimal velocity models from the travel times of seismic waves.

Development and testing of a mouse simulated space flight model

NASA Technical Reports Server (NTRS)

Sonnenfeld, Gerald

1987-01-01

The development and testing of a mouse model for simulating some aspects of weightlessness that occurs during space flight, and the carrying out of immunological experiments on animals undergoing space flight is examined. The mouse model developed was an antiorthostatic, hypokinetic, hypodynamic suspension model similar to one used with rats. The study was divided into two parts. The first involved determination of which immunological parameters should be observed on animals flown during space flight or studied in the suspension model. The second involved suspending mice and determining which of those immunological parameters were altered by the suspension. Rats that were actually flown in Space Shuttle SL-3 were used to test the hypotheses.

Sensitivity of numerical simulation models of debris flow to the rheological parameters and application in the engineering environment

NASA Astrophysics Data System (ADS)

Rosso, M.; Sesenna, R.; Magni, L.; Demurtas, L.; Uras, G.

2009-04-01

Debris flows represents serious hazards in mountainous regions. For engineers it is important to know the quantitative analysis of the flow in terms of volumes, velocities and front height, and it is significant to predict possible triggering and deposition areas. In order to predict flow and deposition behaviour, debris flows traditionally have been regarded as homogenous fluids and bulk flow behaviour that was considered to be controlled by the rheological properties of the matrix. Flow mixtures with a considerable fraction of fines particles typically show a viscoplastic flow behaviour but due to the high variability of the material composition, complex physical interactions on the particle scale and time dependent effects, no generally applicable models are at time capable to cover the full range of all possible flow types. A first category of models, mostly of academic origin, uses a rigorous methodological approach, directed to describe to the phenomenon characterizing all the main parameters that regulate the origin and the propagation of the debris flow, with detail attention to rheology. A second category, which are referred mainly to the commercial environment, has as first objective the versatility and the simplicity of use, introducing theoretical simplifications in the definition of the rheology and in the propagation of the debris flow. The physical variables connected to the rheology are often difficult to determine and involve complex procedures of calibration of the model or long and expensive campaigns of measure, whose application can turn out not suitable to the engineering environment. The rheological parameters of the debris are however to the base of the codes of calculation mainly used in commerce. The necessary data to the implementation of the model refer mainly to the dynamic viscosity, to the shear stress, to the volumetric mass and to the volumetric concentration, that are linked variables. Through the application of various bidimensional and monodimensional commercial models for the simulation of debris flow, in particular because of the reconstruction of famous and expected events in the river basin of the Comboè torrent (Aosta Valley, Italy), it has been possible to reach careful consideration about the calibration of the rheological parameters and the sensitivity of simulation models, specifically about the variability of them. The geomechanical and volumetric characteristics of the sediment at the bottom of the debris could produce uncertainties in model implementation, above all in not exclusively cinematic models, mostly influenced by the rheological parameters. The parameter that mainly influences the final result of the applied numerical models is the volumetric solid concentration that is variable in space and time during the debris flow propagation. In fact rheological parameters are described by a power equation of volumetric concentration. The potentiality and the suitability of a numerical code in the engineering environmental application have to be consider not referring only to the quality and amount of results, but also to the sensibility regarding the parameters variability that are bases of the inner ruotines of the program. Therefore, a suitable model will have to be sensitive to the variability of parameters that the customer can calculate with greater precision. On the other side, it will have to be sufficiently stable to the variation of those parameters that the customer cannot define univocally, but only by range of variation. One of the models utilized for the simulation of debris flow on the Comboè Torrent has been demonstrated as an heavy influenced example by small variation of rheological parameters. Consequently, in spite of the possibility to lead accurate procedures of back-analysis about a recent intense event, it has been found a difficulty in the calibration of the concentration for new expected events. That involved an extreme variability of the final results. In order to achieve more accuracy in the numerical simulation, the rheological parameters were estimated by an implicit way, proceeding to their determination through the application of simple numerical iteration. In fact they can link the obtained values of velocity and hydraulic levels. Literature formulations were used in order to determine rheological parameters. The parameters and ? wowere correlated to velocity and to empirical parameters that have small range of variability. This approach allows to produce a control of input parameters in the calculation models, comparing the obtained base result (velocity and water surface elevation). The implementation of numerical models for engineering profession must be carried out in order that aleatory variables, that are difficult to determine, do not involve an extreme variability of the final result. However, it's a good manner to proceed to the determination of interested variables by means of empirical formulations and through the comparison between different simplified models, including in the analysis pure cinematic models.

Air drying modelling of Mastocarpus stellatus seaweed a source of hybrid carrageenan

NASA Astrophysics Data System (ADS)

Arufe, Santiago; Torres, Maria D.; Chenlo, Francisco; Moreira, Ramon

2018-01-01

Water sorption isotherms from 5 up to 65 °C and air drying kinetics at 35, 45 and 55 °C of Mastocarpus stellatus seaweed were determined. Experimental sorption data were modelled using BET and Oswin models. A four-parameter model, based on Oswin model, was proposed to estimate equilibrium moisture content as function of water activity and temperature simultaneously. Drying experiments showed that water removal rate increased significantly with temperature from 35 to 45 °C, but at higher temperatures drying rate remained constant. Some chemical modifications of the hybrid carrageenans present in the seaweed can be responsible of this unexpected thermal trend. Experimental drying data were modelled using two-parameter Page model (n, k). Page parameter n was constant (1.31 ± 0.10) at tested temperatures, but k varied significantly with drying temperature (from 18.5 ± 0.2 10-3 min-n at 35 °C up to 28.4 ± 0.8 10-3 min-n at 45 and 55 °C). Drying experiments allowed the determination of the critical moisture content of seaweed (0.87 ± 0.06 kg water (kg d.b.)-1). A diffusional model considering slab geometry was employed to determine the effective diffusion coefficient of water during the falling rate period at different temperatures.

Determining parameters of Moon's orbital and rotational motion from LLR observations using GRAIL and IERS-recommended models

NASA Astrophysics Data System (ADS)

Pavlov, Dmitry A.; Williams, James G.; Suvorkin, Vladimir V.

2016-11-01

The aim of this work is to combine the model of orbital and rotational motion of the Moon developed for DE430 with up-to-date astronomical, geodynamical, and geo- and selenophysical models. The parameters of the orbit and physical libration are determined in this work from lunar laser ranging (LLR) observations made at different observatories in 1970-2013. Parameters of other models are taken from solutions that were obtained independently from LLR. A new implementation of the DE430 lunar model, including the liquid core equations, was done within the EPM ephemeris. The postfit residuals of LLR observations make evident that the terrestrial models and solutions recommended by the IERS Conventions are compatible with the lunar theory. That includes: EGM2008 gravitational potential with conventional corrections and variations from solid and ocean tides; displacement of stations due to solid and ocean loading tides; and precession-nutation model. Usage of these models in the solution for LLR observations has allowed us to reduce the number of parameters to be fit. The fixed model of tidal variations of the geopotential has resulted in a lesser value of Moon's extra eccentricity rate, as compared to the original DE430 model with two fit parameters. A mixed model of lunar gravitational potential was used, with some coefficients determined from LLR observations, and other taken from the GL660b solution obtained from the GRAIL spacecraft mission. Solutions obtain accurate positions for the ranging stations and the five retroreflectors. Station motion is derived for sites with long data spans. Dissipation is detected at the lunar fluid core-solid mantle boundary demonstrating that a fluid core is present. Tidal dissipation is strong at both Earth and Moon. Consequently, the lunar semimajor axis is expanding by 38.20 mm/yr, the tidal acceleration in mean longitude is -25.90 {{}^' ' }}/cy^2, and the eccentricity is increasing by 1.48× 10^{-11} each year.

Transfer Function Identification Using Orthogonal Fourier Transform Modeling Functions

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

2013-01-01

A method for transfer function identification, including both model structure determination and parameter estimation, was developed and demonstrated. The approach uses orthogonal modeling functions generated from frequency domain data obtained by Fourier transformation of time series data. The method was applied to simulation data to identify continuous-time transfer function models and unsteady aerodynamic models. Model fit error, estimated model parameters, and the associated uncertainties were used to show the effectiveness of the method for identifying accurate transfer function models from noisy data.

Reactive decontamination of absorbing thin film polymer coatings: model development and parameter determination

NASA Astrophysics Data System (ADS)

Varady, Mark; Mantooth, Brent; Pearl, Thomas; Willis, Matthew

2014-03-01

A continuum model of reactive decontamination in absorbing polymeric thin film substrates exposed to the chemical warfare agent O-ethyl S-[2-(diisopropylamino)ethyl] methylphosphonothioate (known as VX) was developed to assess the performance of various decontaminants. Experiments were performed in conjunction with an inverse analysis method to obtain the necessary model parameters. The experiments involved contaminating a substrate with a fixed VX exposure, applying a decontaminant, followed by a time-resolved, liquid phase extraction of the absorbing substrate to measure the residual contaminant by chromatography. Decontamination model parameters were uniquely determined using the Levenberg-Marquardt nonlinear least squares fitting technique to best fit the experimental time evolution of extracted mass. The model was implemented numerically in both a 2D axisymmetric finite element program and a 1D finite difference code, and it was found that the more computationally efficient 1D implementation was sufficiently accurate. The resulting decontamination model provides an accurate quantification of contaminant concentration profile in the material, which is necessary to assess exposure hazards.

A Comparison of Various Stress Rupture Life Models for Orbiter Composite Pressure Vessels and Confidence Intervals

NASA Technical Reports Server (NTRS)

Grimes-Ledesma, Lorie; Murthy, Pappu L. N.; Phoenix, S. Leigh; Glaser, Ronald

2007-01-01

In conjunction with a recent NASA Engineering and Safety Center (NESC) investigation of flight worthiness of Kevlar Overwrapped Composite Pressure Vessels (COPVs) on board the Orbiter, two stress rupture life prediction models were proposed independently by Phoenix and by Glaser. In this paper, the use of these models to determine the system reliability of 24 COPVs currently in service on board the Orbiter is discussed. The models are briefly described, compared to each other, and model parameters and parameter uncertainties are also reviewed to understand confidence in reliability estimation as well as the sensitivities of these parameters in influencing overall predicted reliability levels. Differences and similarities in the various models will be compared via stress rupture reliability curves (stress ratio vs. lifetime plots). Also outlined will be the differences in the underlying model premises, and predictive outcomes. Sources of error and sensitivities in the models will be examined and discussed based on sensitivity analysis and confidence interval determination. Confidence interval results and their implications will be discussed for the models by Phoenix and Glaser.

Improving Fermi Orbit Determination and Prediction in an Uncertain Atmospheric Drag Environment

NASA Technical Reports Server (NTRS)

Vavrina, Matthew A.; Newman, Clark P.; Slojkowski, Steven E.; Carpenter, J. Russell

2014-01-01

Orbit determination and prediction of the Fermi Gamma-ray Space Telescope trajectory is strongly impacted by the unpredictability and variability of atmospheric density and the spacecraft's ballistic coefficient. Operationally, Global Positioning System point solutions are processed with an extended Kalman filter for orbit determination, and predictions are generated for conjunction assessment with secondary objects. When these predictions are compared to Joint Space Operations Center radar-based solutions, the close approach distance between the two predictions can greatly differ ahead of the conjunction. This work explores strategies for improving prediction accuracy and helps to explain the prediction disparities. Namely, a tuning analysis is performed to determine atmospheric drag modeling and filter parameters that can improve orbit determination as well as prediction accuracy. A 45% improvement in three-day prediction accuracy is realized by tuning the ballistic coefficient and atmospheric density stochastic models, measurement frequency, and other modeling and filter parameters.

Population models for passerine birds: structure, parameterization, and analysis

USGS Publications Warehouse

Noon, B.R.; Sauer, J.R.; McCullough, D.R.; Barrett, R.H.

1992-01-01

Population models have great potential as management tools, as they use infonnation about the life history of a species to summarize estimates of fecundity and survival into a description of population change. Models provide a framework for projecting future populations, determining the effects of management decisions on future population dynamics, evaluating extinction probabilities, and addressing a variety of questions of ecological and evolutionary interest. Even when insufficient information exists to allow complete identification of the model, the modelling procedure is useful because it forces the investigator to consider the life history of the species when determining what parameters should be estimated from field studies and provides a context for evaluating the relative importance of demographic parameters. Models have been little used in the study of the population dynamics of passerine birds because of: (1) widespread misunderstandings of the model structures and parameterizations, (2) a lack of knowledge of life histories of many species, (3) difficulties in obtaining statistically reliable estimates of demographic parameters for most passerine species, and (4) confusion about functional relationships among demographic parameters. As a result, studies of passerine demography are often designed inappropriately and fail to provide essential data. We review appropriate models for passerine bird populations and illustrate their possible uses in evaluating the effects of management or other environmental influences on population dynamics. We identify environmental influences on population dynamics. We identify parameters that must be estimated from field data, briefly review existing statistical methods for obtaining valid estimates, and evaluate the present status of knowledge of these parameters.

Determination of hyporheic travel time distributions and other parameters from concurrent conservative and reactive tracer tests by local-in-global optimization

NASA Astrophysics Data System (ADS)

Knapp, Julia L. A.; Cirpka, Olaf A.

2017-06-01

The complexity of hyporheic flow paths requires reach-scale models of solute transport in streams that are flexible in their representation of the hyporheic passage. We use a model that couples advective-dispersive in-stream transport to hyporheic exchange with a shape-free distribution of hyporheic travel times. The model also accounts for two-site sorption and transformation of reactive solutes. The coefficients of the model are determined by fitting concurrent stream-tracer tests of conservative (fluorescein) and reactive (resazurin/resorufin) compounds. The flexibility of the shape-free models give rise to multiple local minima of the objective function in parameter estimation, thus requiring global-search algorithms, which is hindered by the large number of parameter values to be estimated. We present a local-in-global optimization approach, in which we use a Markov-Chain Monte Carlo method as global-search method to estimate a set of in-stream and hyporheic parameters. Nested therein, we infer the shape-free distribution of hyporheic travel times by a local Gauss-Newton method. The overall approach is independent of the initial guess and provides the joint posterior distribution of all parameters. We apply the described local-in-global optimization method to recorded tracer breakthrough curves of three consecutive stream sections, and infer section-wise hydraulic parameter distributions to analyze how hyporheic exchange processes differ between the stream sections.

Effect of gastric emptying and entero-hepatic circulation on bioequivalence assessment of ranitidine.

PubMed

Chrenova, J; Durisova, M; Mircioiu, C; Dedik, L

2010-01-01

The aim of study was to compare the bioavailability of ranitidine obtained from either Ranitidine (300 mg tablet; LPH® S.C. LaborMed Pharma S.A. Romania: the test formulation) and Zantac® (300 mg tablet; GlaxoSmithKline, Austria: the reference formulation). Twelve, Romanian, healthy volunteers were enrolled in the study. An open-label, two-period, crossover, randomized design was used. Plasma levels of ranitidine were determined using the validated, high-pressure liquid chromatography (HPLC) method. The physiologically motivated time-delayed model was used for the data evaluation and a paired Student's t-test and Schuirmann's two one-sided tests were carried out to compare parameters. Nonmodeling parameters (AUC(t), AUC, C(max), T(max)) were tested by the paired Student's t-test and the 90 confidence intervals of the geometric mean ratios were determined by Schuirmann's tests. Paired Student's t-test showed no significant differences between nonmodeling and modeling parameters. The results of the Schuirmann's tests however indicated significant statistical differences with reference to AUC(t), AUC, C(max), T(max) and other modeling parameters, especially MT(c) and τ(c). Schuirmann's tests revealed significant bioequivalence between ranitidine formulations using the modeling parameters MRT and n. The presented model can be useful as an additional tool to assess drug bioequivalence, by screening for disruptive parameters. Copyright 2010 Prous Science, S.A.U. or its licensors. All rights reserved.

Obtaining of Analytical Relations for Hydraulic Parameters of Channels With Two Phase Flow Using Open CFD Toolbox

NASA Astrophysics Data System (ADS)

Varseev, E.

2017-11-01

The present work is dedicated to verification of numerical model in standard solver of open-source CFD code OpenFOAM for two-phase flow simulation and to determination of so-called “baseline” model parameters. Investigation of heterogeneous coolant flow parameters, which leads to abnormal friction increase of channel in two-phase adiabatic “water-gas” flows with low void fractions, presented.

Determining wave direction using curvature parameters.

PubMed

de Queiroz, Eduardo Vitarelli; de Carvalho, João Luiz Baptista

2016-01-01

The curvature of the sea wave was tested as a parameter for estimating wave direction in the search for better results in estimates of wave direction in shallow waters, where waves of different sizes, frequencies and directions intersect and it is difficult to characterize. We used numerical simulations of the sea surface to determine wave direction calculated from the curvature of the waves. Using 1000 numerical simulations, the statistical variability of the wave direction was determined. The results showed good performance by the curvature parameter for estimating wave direction. Accuracy in the estimates was improved by including wave slope parameters in addition to curvature. The results indicate that the curvature is a promising technique to estimate wave directions.•In this study, the accuracy and precision of curvature parameters to measure wave direction are analyzed using a model simulation that generates 1000 wave records with directional resolution.•The model allows the simultaneous simulation of time-series wave properties such as sea surface elevation, slope and curvature and they were used to analyze the variability of estimated directions.•The simultaneous acquisition of slope and curvature parameters can contribute to estimates wave direction, thus increasing accuracy and precision of results.

Improving fault image by determination of optimum seismic survey parameters using ray-based modeling

NASA Astrophysics Data System (ADS)

Saffarzadeh, Sadegh; Javaherian, Abdolrahim; Hasani, Hossein; Talebi, Mohammad Ali

2018-06-01

In complex structures such as faults, salt domes and reefs, specifying the survey parameters is more challenging and critical owing to the complicated wave field behavior involved in such structures. In the petroleum industry, detecting faults has become crucial for reservoir potential where faults can act as traps for hydrocarbon. In this regard, seismic survey modeling is employed to construct a model close to the real structure, and obtain very realistic synthetic seismic data. Seismic modeling software, the velocity model and parameters pre-determined by conventional methods enable a seismic survey designer to run a shot-by-shot virtual survey operation. A reliable velocity model of structures can be constructed by integrating the 2D seismic data, geological reports and the well information. The effects of various survey designs can be investigated by the analysis of illumination maps and flower plots. Also, seismic processing of the synthetic data output can describe the target image using different survey parameters. Therefore, seismic modeling is one of the most economical ways to establish and test the optimum acquisition parameters to obtain the best image when dealing with complex geological structures. The primary objective of this study is to design a proper 3D seismic survey orientation to achieve fault zone structures through ray-tracing seismic modeling. The results prove that a seismic survey designer can enhance the image of fault planes in a seismic section by utilizing the proposed modeling and processing approach.

The added value of remote sensing products in constraining hydrological models

NASA Astrophysics Data System (ADS)

Nijzink, Remko C.; Almeida, Susana; Pechlivanidis, Ilias; Capell, René; Gustafsson, David; Arheimer, Berit; Freer, Jim; Han, Dawei; Wagener, Thorsten; Sleziak, Patrik; Parajka, Juraj; Savenije, Hubert; Hrachowitz, Markus

2017-04-01

The calibration of a hydrological model still depends on the availability of streamflow data, even though more additional sources of information (i.e. remote sensed data products) have become more widely available. In this research, the model parameters of four different conceptual hydrological models (HYPE, HYMOD, TUW, FLEX) were constrained with remotely sensed products. The models were applied over 27 catchments across Europe to cover a wide range of climates, vegetation and landscapes. The fluxes and states of the models were correlated with the relevant products (e.g. MOD10A snow with modelled snow states), after which new a-posteriori parameter distributions were determined based on a weighting procedure using conditional probabilities. Briefly, each parameter was weighted with the coefficient of determination of the relevant regression between modelled states/fluxes and products. In this way, final feasible parameter sets were derived without the use of discharge time series. Initial results show that improvements in model performance, with regard to streamflow simulations, are obtained when the models are constrained with a set of remotely sensed products simultaneously. In addition, we present a more extensive analysis to assess a model's ability to reproduce a set of hydrological signatures, such as rising limb density or peak distribution. Eventually, this research will enhance our understanding and recommendations in the use of remotely sensed products for constraining conceptual hydrological modelling and improving predictive capability, especially for data sparse regions.

Microfocal angiography of the pulmonary vasculature

NASA Astrophysics Data System (ADS)

Clough, Anne V.; Haworth, Steven T.; Roerig, David T.; Linehan, John H.; Dawson, Christopher A.

1998-07-01

X-ray microfocal angiography provides a means of assessing regional microvascular perfusion parameters using residue detection of vascular indicators. As an application of this methodology, we studied the effects of alveolar hypoxia, a pulmonary vasoconstrictor, on the pulmonary microcirculation to determine changes in regional blood mean transit time, volume and flow between control and hypoxic conditions. Video x-ray images of a dog lung were acquired as a bolus of radiopaque contrast medium passed through the lobar vasculature. X-ray time-absorbance curves were acquired from arterial and microvascular regions-of-interest during both control and hypoxic alveolar gas conditions. A mathematical model based on indicator-dilution theory applied to image residue curves was applied to the data to determine changes in microvascular perfusion parameters. Sensitivity of the model parameters to the model assumptions was analyzed. Generally, the model parameter describing regional microvascular volume, corresponding to area under the microvascular absorbance curve, was the most robust. The results of the model analysis applied to the experimental data suggest a significant decrease in microvascular volume with hypoxia. However, additional model assumptions concerning the flow kinematics within the capillary bed may be required for assessing changes in regional microvascular flow and mean transit time from image residue data.

Model misspecification detection by means of multiple generator errors, using the observed potential map.

PubMed

Zhang, Z; Jewett, D L

1994-01-01

Due to model misspecification, currently-used Dipole Source Localization (DSL) methods may contain Multiple-Generator Errors (MulGenErrs) when fitting simultaneously-active dipoles. The size of the MulGenErr is a function of both the model used, and the dipole parameters, including the dipoles' waveforms (time-varying magnitudes). For a given fitting model, by examining the variation of the MulGenErrs (or the fit parameters) under different waveforms for the same generating-dipoles, the accuracy of the fitting model for this set of dipoles can be determined. This method of testing model misspecification can be applied to evoked potential maps even when the parameters of the generating-dipoles are unknown. The dipole parameters fitted in a model should only be accepted if the model can be shown to be sufficiently accurate.

Solar collector parameter identification from unsteady data by a discrete-gradient optimization algorithm

NASA Technical Reports Server (NTRS)

Hotchkiss, G. B.; Burmeister, L. C.; Bishop, K. A.

1980-01-01

A discrete-gradient optimization algorithm is used to identify the parameters in a one-node and a two-node capacitance model of a flat-plate collector. Collector parameters are first obtained by a linear-least-squares fit to steady state data. These parameters, together with the collector heat capacitances, are then determined from unsteady data by use of the discrete-gradient optimization algorithm with less than 10 percent deviation from the steady state determination. All data were obtained in the indoor solar simulator at the NASA Lewis Research Center.

A lateral dynamics of a wheelchair: identification and analysis of tire parameters.

PubMed

Silva, L C A; Corrêa, F C; Eckert, J J; Santiciolli, F M; Dedini, F G

2017-02-01

In vehicle dynamics studies, the tire behaviour plays an important role in planar motion of the vehicle. Therefore, a correct representation of tire is a necessity. This paper describes a mathematical model for wheelchair tire based on the Magic Formula model. This model is widely used to represent forces and moments between the tire and the ground; however some experimental parameters must be determined. The purpose of this work is to identify the tire parameters for the wheelchair tire model, implementing them in a dynamic model of the wheelchair. For this, we developed an experimental test rig to measure the tires parameters for the lateral dynamics of a wheelchair. This dynamic model was made using a multi-body software and the wheelchair behaviour was analysed and discussed according to the tire parameters. The result of this work is one step further towards the understanding of wheelchair dynamics.

Matching experimental and three dimensional numerical models for structural vibration problems with uncertainties

NASA Astrophysics Data System (ADS)

Langer, P.; Sepahvand, K.; Guist, C.; Bär, J.; Peplow, A.; Marburg, S.

2018-03-01

The simulation model which examines the dynamic behavior of real structures needs to address the impact of uncertainty in both geometry and material parameters. This article investigates three-dimensional finite element models for structural dynamics problems with respect to both model and parameter uncertainties. The parameter uncertainties are determined via laboratory measurements on several beam-like samples. The parameters are then considered as random variables to the finite element model for exploring the uncertainty effects on the quality of the model outputs, i.e. natural frequencies. The accuracy of the output predictions from the model is compared with the experimental results. To this end, the non-contact experimental modal analysis is conducted to identify the natural frequency of the samples. The results show a good agreement compared with experimental data. Furthermore, it is demonstrated that geometrical uncertainties have more influence on the natural frequencies compared to material parameters and material uncertainties are about two times higher than geometrical uncertainties. This gives valuable insights for improving the finite element model due to various parameter ranges required in a modeling process involving uncertainty.

Inferring Spatial Variations of Microstructural Properties from Macroscopic Mechanical Response

PubMed Central

Liu, Tengxiao; Hall, Timothy J.; Barbone, Paul E.; Oberai, Assad A.

2016-01-01

Disease alters tissue microstructure, which in turn affects the macroscopic mechanical properties of tissue. In elasticity imaging, the macroscopic response is measured and is used to infer the spatial distribution of the elastic constitutive parameters. When an empirical constitutive model is used these parameters cannot be linked to the microstructure. However, when the constitutive model is derived from a microstructural representation of the material, it allows for the possibility of inferring the local averages of the spatial distribution of the microstructural parameters. This idea forms the basis of this study. In particular, we first derive a constitutive model by homogenizing the mechanical response of a network of elastic, tortuous fibers. Thereafter, we use this model in an inverse problem to determine the spatial distribution of the microstructural parameters. We solve the inverse problem as a constrained minimization problem, and develop efficient methods for solving it. We apply these methods to displacement fields obtained by deforming gelatin-agar co-gels, and determine the spatial distribution of agar concentration and fiber tortuosity, thereby demonstrating that it is possible to image local averages of microstructural parameters from macroscopic measurements of deformation. PMID:27655420

Reactive flow model development for PBXW-126 using modern nonlinear optimization methods

NASA Astrophysics Data System (ADS)

Murphy, M. J.; Simpson, R. L.; Urtiew, P. A.; Souers, P. C.; Garcia, F.; Garza, R. G.

1996-05-01

The initiation and detonation behavior of PBXW-126 has been characterized and is described. PBXW-126 is a composite explosive consisting of approximately equal amounts of RDX, AP, AL, and NTO with a polyurethane binder. The three term ignition and growth of reaction model parameters (ignition+two growth terms) have been found using nonlinear optimization methods to determine the "best" set of model parameters. The ignition term treats the initiation of up to 0.5% of the RDX. The first growth term in the model treats the RDX growth of reaction up to 20% reacted. The second growth term treats the subsequent growth of reaction of the remaining AP/AL/NTO. The unreacted equation of state (EOS) was determined from the wave profiles of embedded gauge tests while the JWL product EOS was determined from cylinder expansion test results. The nonlinear optimization code, NLQPEB/GLO, was used to determine the "best" set of coefficients for the three term Lee-Tarver ignition and growth of reaction model.

Evolution of Geometric Sensitivity Derivatives from Computer Aided Design Models

NASA Technical Reports Server (NTRS)

Jones, William T.; Lazzara, David; Haimes, Robert

2010-01-01

The generation of design parameter sensitivity derivatives is required for gradient-based optimization. Such sensitivity derivatives are elusive at best when working with geometry defined within the solid modeling context of Computer-Aided Design (CAD) systems. Solid modeling CAD systems are often proprietary and always complex, thereby necessitating ad hoc procedures to infer parameter sensitivity. A new perspective is presented that makes direct use of the hierarchical associativity of CAD features to trace their evolution and thereby track design parameter sensitivity. In contrast to ad hoc methods, this method provides a more concise procedure following the model design intent and determining the sensitivity of CAD geometry directly to its respective defining parameters.

Determining Experimental Parameters for Thermal-Mechanical Forming Simulation considering Martensite Formation in Austenitic Stainless Steel

NASA Astrophysics Data System (ADS)

Schmid, Philipp; Liewald, Mathias

2011-08-01

The forming behavior of metastable austenitic stainless steel is mainly dominated by the temperature-dependent TRIP effect (transformation induced plasticity). Of course, the high dependency of material properties on the temperature level during forming means the temperature must be considered during the FE analysis. The strain-induced formation of α'-martensite from austenite can be represented by using finite element programs utilizing suitable models such as the Haensel-model. This paper discusses the determination of parameters for a completely thermal-mechanical forming simulation in LS-DYNA based on the material model of Haensel. The measurement of the martensite evolution in non-isothermal tensile tests was performed with metastable austenitic stainless steel EN 1.4301 at different rolling directions between 0° and 90 °. This allows an estimation of the influence of the rolling direction to the martensite formation. Of specific importance is the accuracy of the martensite content measured by magnetic induction methods (Feritscope). The observation of different factors, such as stress dependence of the magnetisation, blank thickness and numerous calibration curves discloses a substantial important influence on the parameter determination for the material models. The parameters obtained for use of Haensel model and temperature-dependent friction coefficients are used to simulate forming process of a real component and to validate its implementation in the commercial code LS-DYNA.

Model Construction and Analysis of Respiration in Halobacterium salinarum.

PubMed

Talaue, Cherryl O; del Rosario, Ricardo C H; Pfeiffer, Friedhelm; Mendoza, Eduardo R; Oesterhelt, Dieter

2016-01-01

The archaeon Halobacterium salinarum can produce energy using three different processes, namely photosynthesis, oxidative phosphorylation and fermentation of arginine, and is thus a model organism in bioenergetics. Compared to its bacteriorhodopsin-driven photosynthesis, less attention has been devoted to modeling its respiratory pathway. We created a system of ordinary differential equations that models its oxidative phosphorylation. The model consists of the electron transport chain, the ATP synthase, the potassium uniport and the sodium-proton antiport. By fitting the model parameters to experimental data, we show that the model can explain data on proton motive force generation, ATP production, and the charge balancing of ions between the sodium-proton antiporter and the potassium uniport. We performed sensitivity analysis of the model parameters to determine how the model will respond to perturbations in parameter values. The model and the parameters we derived provide a resource that can be used for analytical studies of the bioenergetics of H. salinarum.

Deriving global parameter estimates for the Noah land surface model using FLUXNET and machine learning

NASA Astrophysics Data System (ADS)

Chaney, Nathaniel W.; Herman, Jonathan D.; Ek, Michael B.; Wood, Eric F.

2016-11-01

With their origins in numerical weather prediction and climate modeling, land surface models aim to accurately partition the surface energy balance. An overlooked challenge in these schemes is the role of model parameter uncertainty, particularly at unmonitored sites. This study provides global parameter estimates for the Noah land surface model using 85 eddy covariance sites in the global FLUXNET network. The at-site parameters are first calibrated using a Latin Hypercube-based ensemble of the most sensitive parameters, determined by the Sobol method, to be the minimum stomatal resistance (rs,min), the Zilitinkevich empirical constant (Czil), and the bare soil evaporation exponent (fxexp). Calibration leads to an increase in the mean Kling-Gupta Efficiency performance metric from 0.54 to 0.71. These calibrated parameter sets are then related to local environmental characteristics using the Extra-Trees machine learning algorithm. The fitted Extra-Trees model is used to map the optimal parameter sets over the globe at a 5 km spatial resolution. The leave-one-out cross validation of the mapped parameters using the Noah land surface model suggests that there is the potential to skillfully relate calibrated model parameter sets to local environmental characteristics. The results demonstrate the potential to use FLUXNET to tune the parameterizations of surface fluxes in land surface models and to provide improved parameter estimates over the globe.

Probability distribution functions for unit hydrographs with optimization using genetic algorithm

NASA Astrophysics Data System (ADS)

Ghorbani, Mohammad Ali; Singh, Vijay P.; Sivakumar, Bellie; H. Kashani, Mahsa; Atre, Atul Arvind; Asadi, Hakimeh

2017-05-01

A unit hydrograph (UH) of a watershed may be viewed as the unit pulse response function of a linear system. In recent years, the use of probability distribution functions (pdfs) for determining a UH has received much attention. In this study, a nonlinear optimization model is developed to transmute a UH into a pdf. The potential of six popular pdfs, namely two-parameter gamma, two-parameter Gumbel, two-parameter log-normal, two-parameter normal, three-parameter Pearson distribution, and two-parameter Weibull is tested on data from the Lighvan catchment in Iran. The probability distribution parameters are determined using the nonlinear least squares optimization method in two ways: (1) optimization by programming in Mathematica; and (2) optimization by applying genetic algorithm. The results are compared with those obtained by the traditional linear least squares method. The results show comparable capability and performance of two nonlinear methods. The gamma and Pearson distributions are the most successful models in preserving the rising and recession limbs of the unit hydographs. The log-normal distribution has a high ability in predicting both the peak flow and time to peak of the unit hydrograph. The nonlinear optimization method does not outperform the linear least squares method in determining the UH (especially for excess rainfall of one pulse), but is comparable.

A short-term and high-resolution distribution system load forecasting approach using support vector regression with hybrid parameters optimization

DOE PAGES

Jiang, Huaiguang; Zhang, Yingchen; Muljadi, Eduard; ...

2016-01-01

This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of themore » hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of the paper.« less

Chaos synchronization and Nelder-Mead search for parameter estimation in nonlinear pharmacological systems: Estimating tumor antigenicity in a model of immunotherapy.

PubMed

Pillai, Nikhil; Craig, Morgan; Dokoumetzidis, Aristeidis; Schwartz, Sorell L; Bies, Robert; Freedman, Immanuel

2018-06-19

In mathematical pharmacology, models are constructed to confer a robust method for optimizing treatment. The predictive capability of pharmacological models depends heavily on the ability to track the system and to accurately determine parameters with reference to the sensitivity in projected outcomes. To closely track chaotic systems, one may choose to apply chaos synchronization. An advantageous byproduct of this methodology is the ability to quantify model parameters. In this paper, we illustrate the use of chaos synchronization combined with Nelder-Mead search to estimate parameters of the well-known Kirschner-Panetta model of IL-2 immunotherapy from noisy data. Chaos synchronization with Nelder-Mead search is shown to provide more accurate and reliable estimates than Nelder-Mead search based on an extended least squares (ELS) objective function. Our results underline the strength of this approach to parameter estimation and provide a broader framework of parameter identification for nonlinear models in pharmacology. Copyright © 2018 Elsevier Ltd. All rights reserved.

PROBLEMS OF THE OPTICAL MODEL FOR DEUTERONS. II. EXPERIMENTS FOR DETERMINATION OF THE PARAMETERS OF THE OPTICAL POTENTIAL FOR DEUTERONS (in Polish)

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

Grotowski, K.

1963-01-01

An experiment for determination of the parameters of the optical potential for deuterons is presented. Total reaction cross sections for the interaction of deuterons with nuclei were determined by evaluating the cross sections for the emission of charged particles and neutrons. The angular distributions for the elastic scattering of deuterons were also measured. (auth)

Sperm function and assisted reproduction technology

PubMed Central

MAAß, GESA; BÖDEKER, ROLF‐HASSO; SCHEIBELHUT, CHRISTINE; STALF, THOMAS; MEHNERT, CLAAS; SCHUPPE, HANS‐CHRISTIAN; JUNG, ANDREAS; SCHILL, WOLF‐BERNHARD

2005-01-01

The evaluation of different functional sperm parameters has become a tool in andrological diagnosis. These assays determine the sperm's capability to fertilize an oocyte. It also appears that sperm functions and semen parameters are interrelated and interdependent. Therefore, the question arose whether a given laboratory test or a battery of tests can predict the outcome in in vitro fertilization (IVF). One‐hundred and sixty‐one patients who underwent an IVF treatment were selected from a database of 4178 patients who had been examined for male infertility 3 months before or after IVF. Sperm concentration, motility, acrosin activity, acrosome reaction, sperm morphology, maternal age, number of transferred embryos, embryo score, fertilization rate and pregnancy rate were determined. In addition, logistic regression models to describe fertilization rate and pregnancy were developed. All the parameters in the models were dichotomized and intra‐ and interindividual variability of the parameters were assessed. Although the sperm parameters showed good correlations with IVF when correlated separately, the only essential parameter in the multivariate model was morphology. The enormous intra‐ and interindividual variability of the values was striking. In conclusion, our data indicate that the andrological status at the end of the respective treatment does not necessarily represent the status at the time of IVF. Despite a relatively low correlation coefficient in the logistic regression model, it appears that among the parameters tested, the most reliable parameter to predict fertilization is normal sperm morphology. (Reprod Med Biol 2005; 4: 7–30) PMID:29699207

An Independent Asteroseismic Analysis of the Fundamental Parameters and Internal Structure of the Solar-like Oscillator KIC 6225718

NASA Astrophysics Data System (ADS)

Wu, Tao; Li, Yan

2017-09-01

Asteroseismology is a useful tool that is usually used to probe stellar interiors and to determine stellar fundamental parameters, such as stellar mass, radius, and surface gravity. In order to probe stellar interiors, making comparisons between observations and models is usually used with the {χ }2-minimization method. The work of Wu & Li reported that the best parameter determined by the {χ }2-matching process is the acoustic radius for pure p-mode oscillations. In the present work, based on the theoretical calculations of Wu & Li, we will independently analyze the seismic observations of KIC 6225718 to determine its fundamental parameters and to investigate its interior properties. First, in order to test the method, we use it in the Sun to determine its fundamental parameters and to investigate interiors. Second, we independently determine the fundamental parameters of KIC 6225718 without any other non-seismic constraint. Therefore, those determined fundamental parameters are independent of those determined by other methods. They can be regarded as independent references in other analyses. Finally, we analyze the stellar internal structure and find that KIC 6225718 has a convective core with the size of 0.078-0.092 {R}⊙ . Its overshooting parameter {f}{ov} in the core is around 0.010. In addition, its center hydrogen {X}{{c}} is about 0.264-0.355.

Comparison of interplanetary CME arrival times and solar wind parameters based on the WSA-ENLIL model with three cone types and observations

NASA Astrophysics Data System (ADS)

Jang, Soojeong; Moon, Y.-J.; Lee, Jae-Ok; Na, Hyeonock

2014-09-01

We have made a comparison between coronal mass ejection (CME)-associated shock propagations based on the Wang-Sheeley-Arge (WSA)-ENLIL model using three cone types and in situ observations. For this we use 28 full-halo CMEs, whose cone parameters are determined and their corresponding interplanetary shocks were observed at the Earth, from 2001 to 2002. We consider three different cone types (an asymmetric cone model, an ice cream cone model, and an elliptical cone model) to determine 3-D CME cone parameters (radial velocity, angular width, and source location), which are the input values of the WSA-ENLIL model. The mean absolute error of the CME-associated shock travel times for the WSA-ENLIL model using the ice-cream cone model is 9.9 h, which is about 1 h smaller than those of the other models. We compare the peak values and profiles of solar wind parameters (speed and density) with in situ observations. We find that the root-mean-square errors of solar wind peak speed and density for the ice cream and asymmetric cone model are about 190 km/s and 24/cm3, respectively. We estimate the cross correlations between the models and observations within the time lag of ± 2 days from the shock travel time. The correlation coefficients between the solar wind speeds from the WSA-ENLIL model using three cone types and in situ observations are approximately 0.7, which is larger than those of solar wind density (cc ˜0.6). Our preliminary investigations show that the ice cream cone model seems to be better than the other cone models in terms of the input parameters of the WSA-ENLIL model.

Path loss variation of on-body UWB channel in the frequency bands of IEEE 802.15.6 standard.

PubMed

Goswami, Dayananda; Sarma, Kanak C; Mahanta, Anil

2016-06-01

The wireless body area network (WBAN) has gaining tremendous attention among researchers and academicians for its envisioned applications in healthcare service. Ultra wideband (UWB) radio technology is considered as excellent air interface for communication among body area network devices. Characterisation and modelling of channel parameters are utmost prerequisite for the development of reliable communication system. The path loss of on-body UWB channel for each frequency band defined in IEEE 802.15.6 standard is experimentally determined. The parameters of path loss model are statistically determined by analysing measurement data. Both the line-of-sight and non-line-of-sight channel conditions are considered in the measurement. Variations of parameter values with the size of human body are analysed along with the variation of parameter values with the surrounding environments. It is observed that the parameters of the path loss model vary with the frequency band as well as with the body size and surrounding environment. The derived parameter values are specific to the particular frequency bands of IEEE 802.15.6 standard, which will be useful for the development of efficient UWB WBAN system.

Determination of quantitative retention-activity relationships between pharmacokinetic parameters and biological effectiveness fingerprints of Salvia miltiorrhiza constituents using biopartitioning and microemulsion high-performance liquid chromatography.

PubMed

Gao, Haoshi; Huang, Hongzhang; Zheng, Aini; Yu, Nuojun; Li, Ning

2017-11-01

In this study, we analyzed danshen (Salvia miltiorrhiza) constituents using biopartitioning and microemulsion high-performance liquid chromatography (MELC). The quantitative retention-activity relationships (QRARs) of the constituents were established to model their pharmacokinetic (PK) parameters and chromatographic retention data, and generate their biological effectiveness fingerprints. A high-performance liquid chromatography (HPLC) method was established to determine the abundance of the extracted danshen constituents, such as sodium danshensu, rosmarinic acid, salvianolic acid B, protocatechuic aldehyde, cryptotanshinone, and tanshinone IIA. And another HPLC protocol was established to determine the abundance of those constituents in rat plasma samples. An experimental model was built in Sprague Dawley (SD) rats, and calculated the corresponding PK parameterst with 3P97 software package. Thirty-five model drugs were selected to test the PK parameter prediction capacities of the various MELC systems and to optimize the chromatographic protocols. QRARs and generated PK fingerprints were established. The test included water/oil-soluble danshen constituents and the prediction capacity of the regression model was validated. The results showed that the model had good predictability. Copyright © 2017. Published by Elsevier B.V.

Kinetic parameter estimation model for anaerobic co-digestion of waste activated sludge and microalgae.

PubMed

Lee, Eunyoung; Cumberbatch, Jewel; Wang, Meng; Zhang, Qiong

2017-03-01

Anaerobic co-digestion has a potential to improve biogas production, but limited kinetic information is available for co-digestion. This study introduced regression-based models to estimate the kinetic parameters for the co-digestion of microalgae and Waste Activated Sludge (WAS). The models were developed using the ratios of co-substrates and the kinetic parameters for the single substrate as indicators. The models were applied to the modified first-order kinetics and Monod model to determine the rate of hydrolysis and methanogenesis for the co-digestion. The results showed that the model using a hyperbola function was better for the estimation of the first-order kinetic coefficients, while the model using inverse tangent function closely estimated the Monod kinetic parameters. The models can be used for estimating kinetic parameters for not only microalgae-WAS co-digestion but also other substrates' co-digestion such as microalgae-swine manure and WAS-aquatic plants. Copyright © 2016 Elsevier Ltd. All rights reserved.

The circuit parameters measurement of the SABALAN-I plasma focus facility and comparison with Lee Model

NASA Astrophysics Data System (ADS)

Karimi, F. S.; Saviz, S.; Ghoranneviss, M.; Salem, M. K.; Aghamir, F. M.

The circuit parameters are investigated in a Mather-type plasma focus device. The experiments are performed in the SABALAN-I plasma focus facility (2 kJ, 20 kV, 10 μF). A 12-turn Rogowski coil is built and used to measure the time derivative of discharge current (dI/dt). The high pressure test has been performed in this work, as alternative technique to short circuit test to determine the machine circuit parameters and calibration factor of the Rogowski coil. The operating parameters are calculated by two methods and the results show that the relative error of determined parameters by method I, are very low in comparison to method II. Thus the method I produces more accurate results than method II. The high pressure test is operated with this assumption that no plasma motion and the circuit parameters may be estimated using R-L-C theory given that C0 is known. However, for a plasma focus, even at highest permissible pressure it is found that there is significant motion, so that estimated circuit parameters not accurate. So the Lee Model code is used in short circuit mode to generate the computed current trace for fitting to the current waveform was integrated from current derivative signal taken with Rogowski coil. Hence, the dynamics of plasma is accounted for into the estimation and the static bank parameters are determined accurately.

TAILSIM Users Guide

NASA Technical Reports Server (NTRS)

Hiltner, Dale W.

2000-01-01

The TAILSIM program uses a 4th order Runge-Kutta method to integrate the standard aircraft equations-of-motion (EOM). The EOM determine three translational and three rotational accelerations about the aircraft's body axis reference system. The forces and moments that drive the EOM are determined from aerodynamic coefficients, dynamic derivatives, and control inputs. Values for these terms are determined from linear interpolation of tables that are a function of parameters such as angle-of-attack and surface deflections. Buildup equations combine these terms and dimensionalize them to generate the driving total forces and moments. Features that make TAILSIM applicable to studies of tailplane stall include modeling of the reversible control System, modeling of the pilot performing a load factor and/or airspeed command task, and modeling of vertical gusts. The reversible control system dynamics can be described as two hinged masses connected by a spring. resulting in a fifth order system. The pilot model is a standard form of lead-lag with a time delay applied to an integrated pitch rate and/or airspeed error feedback. The time delay is implemented by a Pade approximation, while the commanded pitch rate is determined by a commanded load factor. Vertical gust inputs include a single 1-cosine gust and a continuous NASA Dryden gust model. These dynamic models. coupled with the use of a nonlinear database, allow the tailplane stall characteristics, elevator response, and resulting aircraft response, to be modeled. A useful output capability of the TAILSIM program is the ability to display multiple post-run plot pages to allow a quick assessment of the time history response. There are 16 plot pages currently available to the user. Each plot page displays 9 parameters. Each parameter can also be displayed individually. on a one plot-per-page format. For a more refined display of the results the program can also create files of tabulated data. which can then be used by other plotting programs. The TAILSIM program was written straightforwardly assuming the user would want to change the database tables, the buildup equations, the output parameters. and the pilot model parameters. A separate database file and input file are automatically read in by the program. The use of an include file to set up all common blocks facilitates easy changing of parameter names and array sizes.

Structural Identifiability of Dynamic Systems Biology Models

PubMed Central

Villaverde, Alejandro F.

2016-01-01

A powerful way of gaining insight into biological systems is by creating a nonlinear differential equation model, which usually contains many unknown parameters. Such a model is called structurally identifiable if it is possible to determine the values of its parameters from measurements of the model outputs. Structural identifiability is a prerequisite for parameter estimation, and should be assessed before exploiting a model. However, this analysis is seldom performed due to the high computational cost involved in the necessary symbolic calculations, which quickly becomes prohibitive as the problem size increases. In this paper we show how to analyse the structural identifiability of a very general class of nonlinear models by extending methods originally developed for studying observability. We present results about models whose identifiability had not been previously determined, report unidentifiabilities that had not been found before, and show how to modify those unidentifiable models to make them identifiable. This method helps prevent problems caused by lack of identifiability analysis, which can compromise the success of tasks such as experiment design, parameter estimation, and model-based optimization. The procedure is called STRIKE-GOLDD (STRuctural Identifiability taKen as Extended-Generalized Observability with Lie Derivatives and Decomposition), and it is implemented in a MATLAB toolbox which is available as open source software. The broad applicability of this approach facilitates the analysis of the increasingly complex models used in systems biology and other areas. PMID:27792726

Estimating parametric phenotypes that determine anthesis date in zea mays: Challenges in combining ecophysiological models with genetics

USDA-ARS?s Scientific Manuscript database

Ecophysiological crop models encode intra-species behaviors using parameters that are presumed to summarize genotypic properties of individual lines or cultivars. These genotype-specific parameters (GSP’s) can be interpreted as quantitative traits that can be mapped or otherwise analyzed, as are mor...

Optimizing the Determination of Roughness Parameters for Model Urban Canopies

NASA Astrophysics Data System (ADS)

Huq, Pablo; Rahman, Auvi

2018-05-01

We present an objective optimization procedure to determine the roughness parameters for very rough boundary-layer flow over model urban canopies. For neutral stratification the mean velocity profile above a model urban canopy is described by the logarithmic law together with the set of roughness parameters of displacement height d, roughness length z_0 , and friction velocity u_* . Traditionally, values of these roughness parameters are obtained by fitting the logarithmic law through (all) the data points comprising the velocity profile. The new procedure generates unique velocity profiles from subsets or combinations of the data points of the original velocity profile, after which all possible profiles are examined. Each of the generated profiles is fitted to the logarithmic law for a sequence of values of d, with the representative value of d obtained from the minima of the summed least-squares errors for all the generated profiles. The representative values of z_0 and u_* are identified by the peak in the bivariate histogram of z_0 and u_* . The methodology has been verified against laboratory datasets of flow above model urban canopies.

Accounting for uncertainty in model-based prevalence estimation: paratuberculosis control in dairy herds.

PubMed

Davidson, Ross S; McKendrick, Iain J; Wood, Joanna C; Marion, Glenn; Greig, Alistair; Stevenson, Karen; Sharp, Michael; Hutchings, Michael R

2012-09-10

A common approach to the application of epidemiological models is to determine a single (point estimate) parameterisation using the information available in the literature. However, in many cases there is considerable uncertainty about parameter values, reflecting both the incomplete nature of current knowledge and natural variation, for example between farms. Furthermore model outcomes may be highly sensitive to different parameter values. Paratuberculosis is an infection for which many of the key parameter values are poorly understood and highly variable, and for such infections there is a need to develop and apply statistical techniques which make maximal use of available data. A technique based on Latin hypercube sampling combined with a novel reweighting method was developed which enables parameter uncertainty and variability to be incorporated into a model-based framework for estimation of prevalence. The method was evaluated by applying it to a simulation of paratuberculosis in dairy herds which combines a continuous time stochastic algorithm with model features such as within herd variability in disease development and shedding, which have not been previously explored in paratuberculosis models. Generated sample parameter combinations were assigned a weight, determined by quantifying the model's resultant ability to reproduce prevalence data. Once these weights are generated the model can be used to evaluate other scenarios such as control options. To illustrate the utility of this approach these reweighted model outputs were used to compare standard test and cull control strategies both individually and in combination with simple husbandry practices that aim to reduce infection rates. The technique developed has been shown to be applicable to a complex model incorporating realistic control options. For models where parameters are not well known or subject to significant variability, the reweighting scheme allowed estimated distributions of parameter values to be combined with additional sources of information, such as that available from prevalence distributions, resulting in outputs which implicitly handle variation and uncertainty. This methodology allows for more robust predictions from modelling approaches by allowing for parameter uncertainty and combining different sources of information, and is thus expected to be useful in application to a large number of disease systems.

S stars in the Gaia era: stellar parameters and nucleosynthesis

NASA Astrophysics Data System (ADS)

van Eck, Sophie; Karinkuzhi, Drisya; Shetye, Shreeya; Jorissen, Alain; Goriely, Stéphane; Siess, Lionel; Merle, Thibault; Plez, Bertrand

2018-04-01

S stars are s-process and C-enriched (0.5

Automatic Parameterization Strategy for Cardiac Electrophysiology Simulations.

PubMed

Costa, Caroline Mendonca; Hoetzl, Elena; Rocha, Bernardo Martins; Prassl, Anton J; Plank, Gernot

2013-10-01

Driven by recent advances in medical imaging, image segmentation and numerical techniques, computer models of ventricular electrophysiology account for increasingly finer levels of anatomical and biophysical detail. However, considering the large number of model parameters involved parameterization poses a major challenge. A minimum requirement in combined experimental and modeling studies is to achieve good agreement in activation and repolarization sequences between model and experiment or patient data. In this study, we propose basic techniques which aid in determining bidomain parameters to match activation sequences. An iterative parameterization algorithm is implemented which determines appropriate bulk conductivities which yield prescribed velocities. In addition, a method is proposed for splitting the computed bulk conductivities into individual bidomain conductivities by prescribing anisotropy ratios.

Well behaved anisotropic compact star models in general relativity

NASA Astrophysics Data System (ADS)

Jasim, M. K.; Maurya, S. K.; Gupta, Y. K.; Dayanandan, B.

2016-11-01

Anisotropic compact star models have been constructed by assuming a particular form of a metric function e^{λ}. We solved the Einstein field equations for determining the metric function e^{ν}. For this purpose we have assumed a physically valid expression of radial pressure (pr). The obtained anisotropic compact star model is representing the realistic compact objects such as PSR 1937 +21. We have done an extensive study about physical parameters for anisotropic models and found that these parameters are well behaved throughout inside the star. Along with these we have also determined the equation of state for compact star which gives the radial pressure is purely the function of density i.e. pr=f(ρ).

Framework for Uncertainty Assessment - Hanford Site-Wide Groundwater Flow and Transport Modeling

NASA Astrophysics Data System (ADS)

Bergeron, M. P.; Cole, C. R.; Murray, C. J.; Thorne, P. D.; Wurstner, S. K.

2002-05-01

Pacific Northwest National Laboratory is in the process of development and implementation of an uncertainty estimation methodology for use in future site assessments that addresses parameter uncertainty as well as uncertainties related to the groundwater conceptual model. The long-term goals of the effort are development and implementation of an uncertainty estimation methodology for use in future assessments and analyses being made with the Hanford site-wide groundwater model. The basic approach in the framework developed for uncertainty assessment consists of: 1) Alternate conceptual model (ACM) identification to identify and document the major features and assumptions of each conceptual model. The process must also include a periodic review of the existing and proposed new conceptual models as data or understanding become available. 2) ACM development of each identified conceptual model through inverse modeling with historical site data. 3) ACM evaluation to identify which of conceptual models are plausible and should be included in any subsequent uncertainty assessments. 4) ACM uncertainty assessments will only be carried out for those ACMs determined to be plausible through comparison with historical observations and model structure identification measures. The parameter uncertainty assessment process generally involves: a) Model Complexity Optimization - to identify the important or relevant parameters for the uncertainty analysis; b) Characterization of Parameter Uncertainty - to develop the pdfs for the important uncertain parameters including identification of any correlations among parameters; c) Propagation of Uncertainty - to propagate parameter uncertainties (e.g., by first order second moment methods if applicable or by a Monte Carlo approach) through the model to determine the uncertainty in the model predictions of interest. 5)Estimation of combined ACM and scenario uncertainty by a double sum with each component of the inner sum (an individual CCDF) representing parameter uncertainty associated with a particular scenario and ACM and the outer sum enumerating the various plausible ACM and scenario combinations in order to represent the combined estimate of uncertainty (a family of CCDFs). A final important part of the framework includes identification, enumeration, and documentation of all the assumptions, which include those made during conceptual model development, required by the mathematical model, required by the numerical model, made during the spatial and temporal descretization process, needed to assign the statistical model and associated parameters that describe the uncertainty in the relevant input parameters, and finally those assumptions required by the propagation method. Pacific Northwest National Laboratory is operated for the U.S. Department of Energy under Contract DE-AC06-76RL01830.

The thermal structure of Titan's atmosphere

NASA Technical Reports Server (NTRS)

Mckay, Christopher P.; Pollack, James B.; Courtin, Regis

1989-01-01

The present radiative-convective model of the Titan atmosphere thermal structure obtains the solar and IR radiation in a series of spectral intervals with vertical resolution. Haze properties have been determined with a microphysics model encompassing a minimum of free parameters. It is determined that gas and haze opacity alone, using temperatures established by Voyager observations, yields a model that is within a few percent of the radiative convective balance throughout the Titan atmosphere. Model calculations of the surface temperature are generally colder than the observed value by 5-10 K; better agreement is obtained through adjustment of the model parameters. Sunlight absorption by stratospheric haze and pressure-induced gas opacity in the IR are the most important thermal structure-controlling factors.

Bayesian design criteria: computation, comparison, and application to a pharmacokinetic and a pharmacodynamic model.

PubMed

Merlé, Y; Mentré, F

1995-02-01

In this paper 3 criteria to design experiments for Bayesian estimation of the parameters of nonlinear models with respect to their parameters, when a prior distribution is available, are presented: the determinant of the Bayesian information matrix, the determinant of the pre-posterior covariance matrix, and the expected information provided by an experiment. A procedure to simplify the computation of these criteria is proposed in the case of continuous prior distributions and is compared with the criterion obtained from a linearization of the model about the mean of the prior distribution for the parameters. This procedure is applied to two models commonly encountered in the area of pharmacokinetics and pharmacodynamics: the one-compartment open model with bolus intravenous single-dose injection and the Emax model. They both involve two parameters. Additive as well as multiplicative gaussian measurement errors are considered with normal prior distributions. Various combinations of the variances of the prior distribution and of the measurement error are studied. Our attention is restricted to designs with limited numbers of measurements (1 or 2 measurements). This situation often occurs in practice when Bayesian estimation is performed. The optimal Bayesian designs that result vary with the variances of the parameter distribution and with the measurement error. The two-point optimal designs sometimes differ from the D-optimal designs for the mean of the prior distribution and may consist of replicating measurements. For the studied cases, the determinant of the Bayesian information matrix and its linearized form lead to the same optimal designs. In some cases, the pre-posterior covariance matrix can be far from its lower bound, namely, the inverse of the Bayesian information matrix, especially for the Emax model and a multiplicative measurement error. The expected information provided by the experiment and the determinant of the pre-posterior covariance matrix generally lead to the same designs except for the Emax model and the multiplicative measurement error. Results show that these criteria can be easily computed and that they could be incorporated in modules for designing experiments.

Synthesis and Characterization of Liquid Crystalline Epoxy Resins

DTIC Science & Technology

2014-01-01

Temperature dependence of the four parameters in the Burgers model. ......... 81 Figure 4.7 Dependence of creep compliance on creep time at different...Kinetic parameters for LCERs. ......................................................................... 65 Table 3.4 Kinetic parameters for non-LCERs...curing in a high strength magnetic field. The orientation was quantified by an orientation parameter determined with two-dimensional X-ray diffraction

Modeling and Analysis of Process Parameters for Evaluating Shrinkage Problems During Plastic Injection Molding of a DVD-ROM Cover

NASA Astrophysics Data System (ADS)

Öktem, H.

2012-01-01

Plastic injection molding plays a key role in the production of high-quality plastic parts. Shrinkage is one of the most significant problems of a plastic part in terms of quality in the plastic injection molding. This article focuses on the study of the modeling and analysis of the effects of process parameters on the shrinkage by evaluating the quality of the plastic part of a DVD-ROM cover made with Acrylonitrile Butadiene Styrene (ABS) polymer material. An effective regression model was developed to determine the mathematical relationship between the process parameters (mold temperature, melt temperature, injection pressure, injection time, and cooling time) and the volumetric shrinkage by utilizing the analysis data. Finite element (FE) analyses designed by Taguchi (L27) orthogonal arrays were run in the Moldflow simulation program. Analysis of variance (ANOVA) was then performed to check the adequacy of the regression model and to determine the effect of the process parameters on the shrinkage. Experiments were conducted to control the accuracy of the regression model with the FE analyses obtained from Moldflow. The results show that the regression model agrees very well with the FE analyses and the experiments. From this, it can be concluded that this study succeeded in modeling the shrinkage problem in our application.

Understanding the transmission dynamics of respiratory syncytial virus using multiple time series and nested models.

PubMed

White, L J; Mandl, J N; Gomes, M G M; Bodley-Tickell, A T; Cane, P A; Perez-Brena, P; Aguilar, J C; Siqueira, M M; Portes, S A; Straliotto, S M; Waris, M; Nokes, D J; Medley, G F

2007-09-01

The nature and role of re-infection and partial immunity are likely to be important determinants of the transmission dynamics of human respiratory syncytial virus (hRSV). We propose a single model structure that captures four possible host responses to infection and subsequent reinfection: partial susceptibility, altered infection duration, reduced infectiousness and temporary immunity (which might be partial). The magnitude of these responses is determined by four homotopy parameters, and by setting some of these parameters to extreme values we generate a set of eight nested, deterministic transmission models. In order to investigate hRSV transmission dynamics, we applied these models to incidence data from eight international locations. Seasonality is included as cyclic variation in transmission. Parameters associated with the natural history of the infection were assumed to be independent of geographic location, while others, such as those associated with seasonality, were assumed location specific. Models incorporating either of the two extreme assumptions for immunity (none or solid and lifelong) were unable to reproduce the observed dynamics. Model fits with either waning or partial immunity to disease or both were visually comparable. The best fitting structure was a lifelong partial immunity to both disease and infection. Observed patterns were reproduced by stochastic simulations using the parameter values estimated from the deterministic models.

Parameter and Process Significance in Mechanistic Modeling of Cellulose Hydrolysis

NASA Astrophysics Data System (ADS)

Rotter, B.; Barry, A.; Gerhard, J.; Small, J.; Tahar, B.

2005-12-01

The rate of cellulose hydrolysis, and of associated microbial processes, is important in determining the stability of landfills and their potential impact on the environment, as well as associated time scales. To permit further exploration in this field, a process-based model of cellulose hydrolysis was developed. The model, which is relevant to both landfill and anaerobic digesters, includes a novel approach to biomass transfer between a cellulose-bound biofilm and biomass in the surrounding liquid. Model results highlight the significance of the bacterial colonization of cellulose particles by attachment through contact in solution. Simulations revealed that enhanced colonization, and therefore cellulose degradation, was associated with reduced cellulose particle size, higher biomass populations in solution, and increased cellulose-binding ability of the biomass. A sensitivity analysis of the system parameters revealed different sensitivities to model parameters for a typical landfill scenario versus that for an anaerobic digester. The results indicate that relative surface area of cellulose and proximity of hydrolyzing bacteria are key factors determining the cellulose degradation rate.

Determination of Kinetic Parameters for the Thermal Decomposition of Parthenium hysterophorus

NASA Astrophysics Data System (ADS)

Dhaundiyal, Alok; Singh, Suraj B.; Hanon, Muammel M.; Rawat, Rekha

2018-02-01

A kinetic study of pyrolysis process of Parthenium hysterophorous is carried out by using thermogravimetric analysis (TGA) equipment. The present study investigates the thermal degradation and determination of the kinetic parameters such as activation E and the frequency factor A using model-free methods given by Flynn Wall and Ozawa (FWO), Kissinger-Akahira-Sonuse (KAS) and Kissinger, and model-fitting (Coats Redfern). The results derived from thermal decomposition process demarcate decomposition of Parthenium hysterophorous among the three main stages, such as dehydration, active and passive pyrolysis. It is shown through DTG thermograms that the increase in the heating rate caused temperature peaks at maximum weight loss rate to shift towards higher temperature regime. The results are compared with Coats Redfern (Integral method) and experimental results have shown that values of kinetic parameters obtained from model-free methods are in good agreement. Whereas the results obtained through Coats Redfern model at different heating rates are not promising, however, the diffusion models provided the good fitting with the experimental data.

Inverse models: A necessary next step in ground-water modeling

USGS Publications Warehouse

Poeter, E.P.; Hill, M.C.

1997-01-01

Inverse models using, for example, nonlinear least-squares regression, provide capabilities that help modelers take full advantage of the insight available from ground-water models. However, lack of information about the requirements and benefits of inverse models is an obstacle to their widespread use. This paper presents a simple ground-water flow problem to illustrate the requirements and benefits of the nonlinear least-squares repression method of inverse modeling and discusses how these attributes apply to field problems. The benefits of inverse modeling include: (1) expedited determination of best fit parameter values; (2) quantification of the (a) quality of calibration, (b) data shortcomings and needs, and (c) confidence limits on parameter estimates and predictions; and (3) identification of issues that are easily overlooked during nonautomated calibration.Inverse models using, for example, nonlinear least-squares regression, provide capabilities that help modelers take full advantage of the insight available from ground-water models. However, lack of information about the requirements and benefits of inverse models is an obstacle to their widespread use. This paper presents a simple ground-water flow problem to illustrate the requirements and benefits of the nonlinear least-squares regression method of inverse modeling and discusses how these attributes apply to field problems. The benefits of inverse modeling include: (1) expedited determination of best fit parameter values; (2) quantification of the (a) quality of calibration, (b) data shortcomings and needs, and (c) confidence limits on parameter estimates and predictions; and (3) identification of issues that are easily overlooked during nonautomated calibration.

The reconstruction of tachyon inflationary potentials

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

Fei, Qin; Gong, Yungui; Lin, Jiong

We derive a lower bound on the field excursion for the tachyon inflation, which is determined by the amplitude of the scalar perturbation and the number of e -folds before the end of inflation. Using the relation between the observables like n {sub s} and r with the slow-roll parameters, we reconstruct three classes of tachyon potentials. The model parameters are determined from the observations before the potentials are reconstructed, and the observations prefer the concave potential. We also discuss the constraints from the reheating phase preceding the radiation domination for the three classes of models by assuming the equationmore » of state parameter w {sub re} during reheating is a constant. Depending on the model parameters and the value of w {sub re} , the constraints on N {sub re} and T {sub re} are different. As n {sub s} increases, the allowed reheating epoch becomes longer for w {sub re} =−1/3, 0 and 1/6 while the allowed reheating epoch becomes shorter for w {sub re} =2/3.« less

Retrieving the optical parameters of biological tissues using diffuse reflectance spectroscopy and Fourier series expansions. I. theory and application.

PubMed

Muñoz Morales, Aarón A; Vázquez Y Montiel, Sergio

2012-10-01

The determination of optical parameters of biological tissues is essential for the application of optical techniques in the diagnosis and treatment of diseases. Diffuse Reflection Spectroscopy is a widely used technique to analyze the optical characteristics of biological tissues. In this paper we show that by using diffuse reflectance spectra and a new mathematical model we can retrieve the optical parameters by applying an adjustment of the data with nonlinear least squares. In our model we represent the spectra using a Fourier series expansion finding mathematical relations between the polynomial coefficients and the optical parameters. In this first paper we use spectra generated by the Monte Carlo Multilayered Technique to simulate the propagation of photons in turbid media. Using these spectra we determine the behavior of Fourier series coefficients when varying the optical parameters of the medium under study. With this procedure we find mathematical relations between Fourier series coefficients and optical parameters. Finally, the results show that our method can retrieve the optical parameters of biological tissues with accuracy that is adequate for medical applications.

New approaches in agent-based modeling of complex financial systems

NASA Astrophysics Data System (ADS)

Chen, Ting-Ting; Zheng, Bo; Li, Yan; Jiang, Xiong-Fei

2017-12-01

Agent-based modeling is a powerful simulation technique to understand the collective behavior and microscopic interaction in complex financial systems. Recently, the concept for determining the key parameters of agent-based models from empirical data instead of setting them artificially was suggested. We first review several agent-based models and the new approaches to determine the key model parameters from historical market data. Based on the agents' behaviors with heterogeneous personal preferences and interactions, these models are successful in explaining the microscopic origination of the temporal and spatial correlations of financial markets. We then present a novel paradigm combining big-data analysis with agent-based modeling. Specifically, from internet query and stock market data, we extract the information driving forces and develop an agent-based model to simulate the dynamic behaviors of complex financial systems.

Particle Dark Matter constraints: the effect of Galactic uncertainties

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

Benito, Maria; Bernal, Nicolás; Iocco, Fabio

2017-02-01

Collider, space, and Earth based experiments are now able to probe several extensions of the Standard Model of particle physics which provide viable dark matter candidates. Direct and indirect dark matter searches rely on inputs of astrophysical nature, such as the local dark matter density or the shape of the dark matter density profile in the target in object. The determination of these quantities is highly affected by astrophysical uncertainties. The latter, especially those for our own Galaxy, are ill-known, and often not fully accounted for when analyzing the phenomenology of particle physics models. In this paper we present amore » systematic, quantitative estimate of how astrophysical uncertainties on Galactic quantities (such as the local galactocentric distance, circular velocity, or the morphology of the stellar disk and bulge) propagate to the determination of the phenomenology of particle physics models, thus eventually affecting the determination of new physics parameters. We present results in the context of two specific extensions of the Standard Model (the Singlet Scalar and the Inert Doublet) that we adopt as case studies for their simplicity in illustrating the magnitude and impact of such uncertainties on the parameter space of the particle physics model itself. Our findings point toward very relevant effects of current Galactic uncertainties on the determination of particle physics parameters, and urge a systematic estimate of such uncertainties in more complex scenarios, in order to achieve constraints on the determination of new physics that realistically include all known uncertainties.« less

Definition and solution of a stochastic inverse problem for the Manning's n parameter field in hydrodynamic models.

PubMed

Butler, T; Graham, L; Estep, D; Dawson, C; Westerink, J J

2015-04-01

The uncertainty in spatially heterogeneous Manning's n fields is quantified using a novel formulation and numerical solution of stochastic inverse problems for physics-based models. The uncertainty is quantified in terms of a probability measure and the physics-based model considered here is the state-of-the-art ADCIRC model although the presented methodology applies to other hydrodynamic models. An accessible overview of the formulation and solution of the stochastic inverse problem in a mathematically rigorous framework based on measure theory is presented. Technical details that arise in practice by applying the framework to determine the Manning's n parameter field in a shallow water equation model used for coastal hydrodynamics are presented and an efficient computational algorithm and open source software package are developed. A new notion of "condition" for the stochastic inverse problem is defined and analyzed as it relates to the computation of probabilities. This notion of condition is investigated to determine effective output quantities of interest of maximum water elevations to use for the inverse problem for the Manning's n parameter and the effect on model predictions is analyzed.

Definition and solution of a stochastic inverse problem for the Manning's n parameter field in hydrodynamic models

NASA Astrophysics Data System (ADS)

Butler, T.; Graham, L.; Estep, D.; Dawson, C.; Westerink, J. J.

2015-04-01

The uncertainty in spatially heterogeneous Manning's n fields is quantified using a novel formulation and numerical solution of stochastic inverse problems for physics-based models. The uncertainty is quantified in terms of a probability measure and the physics-based model considered here is the state-of-the-art ADCIRC model although the presented methodology applies to other hydrodynamic models. An accessible overview of the formulation and solution of the stochastic inverse problem in a mathematically rigorous framework based on measure theory is presented. Technical details that arise in practice by applying the framework to determine the Manning's n parameter field in a shallow water equation model used for coastal hydrodynamics are presented and an efficient computational algorithm and open source software package are developed. A new notion of "condition" for the stochastic inverse problem is defined and analyzed as it relates to the computation of probabilities. This notion of condition is investigated to determine effective output quantities of interest of maximum water elevations to use for the inverse problem for the Manning's n parameter and the effect on model predictions is analyzed.

An improved method to determine neuromuscular properties using force laws - From single muscle to applications in human movements.

PubMed

Siebert, T; Sust, M; Thaller, S; Tilp, M; Wagner, H

2007-04-01

We evaluate an improved method for individually determining neuromuscular properties in vivo. The method is based on Hill's equation used as a force law combined with Newton's equation of motion. To ensure the range of validity of Hill's equation, we first perform detailed investigations on in vitro single muscles. The force-velocity relation determined with the model coincides well with results obtained by standard methods (r=.99) above 20% of the isometric force. In addition, the model-predicted force curves during work loop contractions very well agree with measurements (mean difference: 2-3%). Subsequently, we deduce theoretically under which conditions it is possible to combine several muscles of the human body to model muscles. This leads to a model equation for human leg extension movements containing parameters for the muscle properties and for the activation. To numerically determine these invariant neuromuscular properties we devise an experimental method based on concentric and isometric leg extensions. With this method we determine individual muscle parameters from experiments such that the simulated curves agree well with experiments (r=.99). A reliability test with 12 participants revealed correlations r=.72-.91 for the neuromuscular parameters (p<.01). Predictions of similar movements under different conditions show mean errors of about 5%. In addition, we present applications in sports practise and theory.

Measurement of angular parameters from the decay B0 → K*0μ+μ- in proton-proton collisions at √{ s } = 8TeV

NASA Astrophysics Data System (ADS)

Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Ambrogi, F.; Asilar, E.; Bergauer, T.; Brandstetter, J.; Brondolin, E.; Dragicevic, M.; Erö, J.; Flechl, M.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Grossmann, J.; Hrubec, J.; Jeitler, M.; König, A.; Krammer, N.; Krätschmer, I.; Liko, D.; Madlener, T.; Mikulec, I.; Pree, E.; Rad, N.; Rohringer, H.; Schieck, J.; Schöfbeck, R.; Spanring, M.; Spitzbart, D.; Waltenberger, W.; Wittmann, J.; Wulz, C.-E.; Zarucki, M.; Chekhovsky, V.; Mossolov, V.; Suarez Gonzalez, J.; De Wolf, E. A.; Di Croce, D.; Janssen, X.; Lauwers, J.; Van De Klundert, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Abu Zeid, S.; Blekman, F.; D'Hondt, J.; De Bruyn, I.; De Clercq, J.; Deroover, K.; Flouris, G.; Lontkovskyi, D.; Lowette, S.; Moortgat, S.; Moreels, L.; Python, Q.; Skovpen, K.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Parijs, I.; Beghin, D.; Brun, H.; Clerbaux, B.; De Lentdecker, G.; Delannoy, H.; Dorney, B.; Fasanella, G.; Favart, L.; Goldouzian, R.; Grebenyuk, A.; Karapostoli, G.; Lenzi, T.; Luetic, J.; Maerschalk, T.; Marinov, A.; Randle-conde, A.; Seva, T.; Starling, E.; Vander Velde, C.; Vanlaer, P.; Vannerom, D.; Yonamine, R.; Zenoni, F.; Zhang, F.; Cimmino, A.; Cornelis, T.; Dobur, D.; Fagot, A.; Gul, M.; Khvastunov, I.; Poyraz, D.; Roskas, C.; Salva, S.; Tytgat, M.; Verbeke, W.; Zaganidis, N.; Bakhshiansohi, H.; Bondu, O.; Brochet, S.; Bruno, G.; Caputo, C.; Caudron, A.; David, P.; De Visscher, S.; Delaere, C.; Delcourt, M.; Francois, B.; Giammanco, A.; Komm, M.; Krintiras, G.; Lemaitre, V.; Magitteri, A.; Mertens, A.; Musich, M.; Piotrzkowski, K.; Quertenmont, L.; Saggio, A.; Vidal Marono, M.; Wertz, S.; Zobec, J.; Beliy, N.; Aldá Júnior, W. L.; Alves, F. L.; Alves, G. A.; Brito, L.; Correa Martins Junior, M.; Hensel, C.; Moraes, A.; Pol, M. E.; Rebello Teles, P.; Belchior Batista Das Chagas, E.; Carvalho, W.; Chinellato, J.; Coelho, E.; Da Costa, E. M.; Da Silveira, G. G.; De Jesus Damiao, D.; Fonseca De Souza, S.; Huertas Guativa, L. M.; Malbouisson, H.; Melo De Almeida, M.; Mora Herrera, C.; Mundim, L.; Nogima, H.; Sanchez Rosas, L. J.; Santoro, A.; Sznajder, A.; Thiel, M.; Tonelli Manganote, E. J.; Torres Da Silva De Araujo, F.; Vilela Pereira, A.; Ahuja, S.; Bernardes, C. A.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Mercadante, P. G.; Novaes, S. F.; Padula, Sandra S.; Romero Abad, D.; Ruiz Vargas, J. C.; Aleksandrov, A.; Hadjiiska, R.; Iaydjiev, P.; Misheva, M.; Rodozov, M.; Shopova, M.; Sultanov, G.; Dimitrov, A.; Glushkov, I.; Litov, L.; Pavlov, B.; Petkov, P.; Fang, W.; Gao, X.; Yuan, L.; Ahmad, M.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Chen, M.; Chen, Y.; Jiang, C. H.; Leggat, D.; Liao, H.; Liu, Z.; Romeo, F.; Shaheen, S. M.; Spiezia, A.; Tao, J.; Wang, C.; Wang, Z.; Yazgan, E.; Zhang, H.; Zhang, S.; Zhao, J.; Ban, Y.; Chen, G.; Li, Q.; Linwei, L.; Liu, S.; Mao, Y.; Qian, S. J.; Wang, D.; Xu, Z.; Avila, C.; Cabrera, A.; Chaparro Sierra, L. F.; Florez, C.; González Hernández, C. F.; Ruiz Alvarez, J. D.; Segura Delgado, M. A.; Courbon, B.; Godinovic, N.; Lelas, D.; Puljak, I.; Ribeiro Cipriano, P. M.; Sculac, T.; Antunovic, Z.; Kovac, M.; Brigljevic, V.; Ferencek, D.; Kadija, K.; Mesic, B.; Starodumov, A.; Susa, T.; Ather, M. W.; Attikis, A.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Rykaczewski, H.; Finger, M.; Finger, M.; Carrera Jarrin, E.; Assran, Y.; Elgammal, S.; Mahrous, A.; Dewanjee, R. K.; Kadastik, M.; Perrini, L.; Raidal, M.; Tiko, A.; Veelken, C.; Eerola, P.; Kirschenmann, H.; Pekkanen, J.; Voutilainen, M.; Havukainen, J.; Heikkilä, J. K.; Järvinen, T.; Karimäki, V.; Kinnunen, R.; Lampén, T.; Lassila-Perini, K.; Laurila, S.; Lehti, S.; Lindén, T.; Luukka, P.; Siikonen, H.; Tuominen, E.; Tuominiemi, J.; Talvitie, J.; Tuuva, T.; Besancon, M.; Couderc, F.; Dejardin, M.; Denegri, D.; Faure, J. L.; Ferri, F.; Ganjour, S.; Ghosh, S.; Givernaud, A.; Gras, P.; Hamel de Monchenault, G.; Jarry, P.; Kucher, I.; Leloup, C.; Locci, E.; Machet, M.; Malcles, J.; Negro, G.; Rander, J.; Rosowsky, A.; Sahin, M. Ö.; Titov, M.; Abdulsalam, A.; Amendola, C.; Antropov, I.; Baffioni, S.; Beaudette, F.; Busson, P.; Cadamuro, L.; Charlot, C.; Granier de Cassagnac, R.; Jo, M.; Lisniak, S.; Lobanov, A.; Martin Blanco, J.; Nguyen, M.; Ochando, C.; Ortona, G.; Paganini, P.; Pigard, P.; Salerno, R.; Sauvan, J. B.; Sirois, Y.; Stahl Leiton, A. G.; Strebler, T.; Yilmaz, Y.; Zabi, A.; Zghiche, A.; Agram, J.-L.; Andrea, J.; Bloch, D.; Brom, J.-M.; Buttignol, M.; Chabert, E. C.; Chanon, N.; Collard, C.; Conte, E.; Coubez, X.; Fontaine, J.-C.; Gelé, D.; Goerlach, U.; Jansová, M.; Le Bihan, A.-C.; Tonon, N.; Van Hove, P.; Gadrat, S.; Beauceron, S.; Bernet, C.; Boudoul, G.; Chierici, R.; Contardo, D.; Depasse, P.; El Mamouni, H.; Fay, J.; Finco, L.; Gascon, S.; Gouzevitch, M.; Grenier, G.; Ille, B.; Lagarde, F.; Laktineh, I. B.; Lethuillier, M.; Mirabito, L.; Pequegnot, A. L.; Perries, S.; Popov, A.; Sordini, V.; Vander Donckt, M.; Viret, S.; Toriashvili, T.; Lomidze, D.; Autermann, C.; Feld, L.; Kiesel, M. K.; Klein, K.; Lipinski, M.; Preuten, M.; Schomakers, C.; Schulz, J.; Zhukov, V.; Albert, A.; Dietz-Laursonn, E.; Duchardt, D.; Endres, M.; Erdmann, M.; Erdweg, S.; Esch, T.; Fischer, R.; Güth, A.; Hamer, M.; Hebbeker, T.; Heidemann, C.; Hoepfner, K.; Knutzen, S.; Merschmeyer, M.; Meyer, A.; Millet, P.; Mukherjee, S.; Pook, T.; Radziej, M.; Reithler, H.; Rieger, M.; Scheuch, F.; Teyssier, D.; Thüer, S.; Flügge, G.; Kargoll, B.; Kress, T.; Künsken, A.; Müller, T.; Nehrkorn, A.; Nowack, A.; Pistone, C.; Pooth, O.; Stahl, A.; Aldaya Martin, M.; Arndt, T.; Asawatangtrakuldee, C.; Beernaert, K.; Behnke, O.; Behrens, U.; Bermúdez Martínez, A.; Bin Anuar, A. A.; Borras, K.; Botta, V.; Campbell, A.; Connor, P.; Contreras-Campana, C.; Costanza, F.; Diez Pardos, C.; Eckerlin, G.; Eckstein, D.; Eichhorn, T.; Eren, E.; Gallo, E.; Garay Garcia, J.; Geiser, A.; Gizhko, A.; Grados Luyando, J. 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I.; Bencze, G.; Hajdu, C.; Horvath, D.; Hunyadi, Á.; Sikler, F.; Veszpremi, V.; Beni, N.; Czellar, S.; Karancsi, J.; Makovec, A.; Molnar, J.; Szillasi, Z.; Bartók, M.; Raics, P.; Trocsanyi, Z. L.; Ujvari, B.; Choudhury, S.; Komaragiri, J. R.; Bahinipati, S.; Bhowmik, S.; Mal, P.; Mandal, K.; Nayak, A.; Sahoo, D. K.; Sahoo, N.; Swain, S. K.; Bansal, S.; Beri, S. B.; Bhatnagar, V.; Chawla, R.; Dhingra, N.; Kalsi, A. K.; Kaur, A.; Kaur, M.; Kaur, S.; Kumar, R.; Kumari, P.; Mehta, A.; Singh, J. B.; Walia, G.; Kumar, Ashok; Shah, Aashaq; Bhardwaj, A.; Chauhan, S.; Choudhary, B. C.; Garg, R. B.; Keshri, S.; Kumar, A.; Malhotra, S.; Naimuddin, M.; Ranjan, K.; Sharma, R.; Bhardwaj, R.; Bhattacharya, R.; Bhattacharya, S.; Bhawandeep, U.; Dey, S.; Dutt, S.; Dutta, S.; Ghosh, S.; Majumdar, N.; Modak, A.; Mondal, K.; Mukhopadhyay, S.; Nandan, S.; Purohit, A.; Roy, A.; Roy Chowdhury, S.; Sarkar, S.; Sharan, M.; Thakur, S.; Behera, P. K.; Chudasama, R.; Dutta, D.; Jha, V.; Kumar, V.; Mohanty, A. 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M.; Khakzad, M.; Mohammadi Najafabadi, M.; Naseri, M.; Paktinat Mehdiabadi, S.; Rezaei Hosseinabadi, F.; Safarzadeh, B.; Zeinali, M.; Felcini, M.; Grunewald, M.; Abbrescia, M.; Calabria, C.; Colaleo, A.; Creanza, D.; Cristella, L.; De Filippis, N.; De Palma, M.; Errico, F.; Fiore, L.; Iaselli, G.; Lezki, S.; Maggi, G.; Maggi, M.; Miniello, G.; My, S.; Nuzzo, S.; Pompili, A.; Pugliese, G.; Radogna, R.; Ranieri, A.; Selvaggi, G.; Sharma, A.; Silvestris, L.; Venditti, R.; Verwilligen, P.; Abbiendi, G.; Battilana, C.; Bonacorsi, D.; Borgonovi, L.; Braibant-Giacomelli, S.; Campanini, R.; Capiluppi, P.; Castro, A.; Cavallo, F. R.; Chhibra, S. S.; Codispoti, G.; Cuffiani, M.; Dallavalle, G. M.; Fabbri, F.; Fanfani, A.; Fasanella, D.; Giacomelli, P.; Grandi, C.; Guiducci, L.; Marcellini, S.; Masetti, G.; Montanari, A.; Navarria, F. L.; Perrotta, A.; Rossi, A. M.; Rovelli, T.; Siroli, G. 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A.; Lista, L.; Meola, S.; Paolucci, P.; Sciacca, C.; Thyssen, F.; Azzi, P.; Bacchetta, N.; Benato, L.; Boletti, A.; Carlin, R.; Carvalho Antunes De Oliveira, A.; Checchia, P.; Dall'Osso, M.; De Castro Manzano, P.; Dorigo, T.; Gasparini, U.; Gozzelino, A.; Lacaprara, S.; Lujan, P.; Margoni, M.; Meneguzzo, A. T.; Montecassiano, F.; Passaseo, M.; Pozzobon, N.; Ronchese, P.; Rossin, R.; Simonetto, F.; Torassa, E.; Zanetti, M.; Zotto, P.; Zumerle, G.; Braghieri, A.; Magnani, A.; Montagna, P.; Ratti, S. P.; Re, V.; Ressegotti, M.; Riccardi, C.; Salvini, P.; Vai, I.; Vitulo, P.; Alunni Solestizi, L.; Biasini, M.; Bilei, G. M.; Cecchi, C.; Ciangottini, D.; Fanò, L.; Lariccia, P.; Leonardi, R.; Manoni, E.; Mantovani, G.; Mariani, V.; Menichelli, M.; Rossi, A.; Santocchia, A.; Spiga, D.; Androsov, K.; Azzurri, P.; Bagliesi, G.; Boccali, T.; Borrello, L.; Castaldi, R.; Ciocci, M. A.; Dell'Orso, R.; Fedi, G.; Giannini, L.; Giassi, A.; Grippo, M. T.; Ligabue, F.; Lomtadze, T.; Manca, E.; Mandorli, G.; Martini, L.; Messineo, A.; Palla, F.; Rizzi, A.; Savoy-Navarro, A.; Spagnolo, P.; Tenchini, R.; Tonelli, G.; Venturi, A.; Verdini, P. G.; Barone, L.; Cavallari, F.; Cipriani, M.; Daci, N.; Del Re, D.; Di Marco, E.; Diemoz, M.; Gelli, S.; Longo, E.; Margaroli, F.; Marzocchi, B.; Meridiani, P.; Organtini, G.; Paramatti, R.; Preiato, F.; Rahatlou, S.; Rovelli, C.; Santanastasio, F.; Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Bartosik, N.; Bellan, R.; Biino, C.; Cartiglia, N.; Cenna, F.; Costa, M.; Covarelli, R.; Degano, A.; Demaria, N.; Kiani, B.; Mariotti, C.; Maselli, S.; Migliore, E.; Monaco, V.; Monteil, E.; Monteno, M.; Obertino, M. M.; Pacher, L.; Pastrone, N.; Pelliccioni, M.; Pinna Angioni, G. L.; Ravera, F.; Romero, A.; Ruspa, M.; Sacchi, R.; Shchelina, K.; Sola, V.; Solano, A.; Staiano, A.; Traczyk, P.; Belforte, S.; Casarsa, M.; Cossutti, F.; Della Ricca, G.; Zanetti, A.; Kim, D. H.; Kim, G. N.; Kim, M. S.; Lee, J.; Lee, S.; Lee, S. W.; Moon, C. S.; Oh, Y. D.; Sekmen, S.; Son, D. C.; Yang, Y. C.; Lee, A.; Kim, H.; Moon, D. H.; Oh, G.; Brochero Cifuentes, J. A.; Goh, J.; Kim, T. J.; Cho, S.; Choi, S.; Go, Y.; Gyun, D.; Ha, S.; Hong, B.; Jo, Y.; Kim, Y.; Lee, K.; Lee, K. S.; Lee, S.; Lim, J.; Park, S. K.; Roh, Y.; Almond, J.; Kim, J.; Kim, J. S.; Lee, H.; Lee, K.; Nam, K.; Oh, S. B.; Radburn-Smith, B. C.; Seo, S. h.; Yang, U. K.; Yoo, H. D.; Yu, G. B.; Choi, M.; Kim, H.; Kim, J. H.; Lee, J. S. H.; Park, I. C.; Choi, Y.; Hwang, C.; Lee, J.; Yu, I.; Dudenas, V.; Juodagalvis, A.; Vaitkus, J.; Ahmed, I.; Ibrahim, Z. A.; Md Ali, M. A. B.; Mohamad Idris, F.; Wan Abdullah, W. A. T.; Yusli, M. N.; Zolkapli, Z.; Reyes-Almanza, R.; Ramirez-Sanchez, G.; Duran-Osuna, M. C.; Castilla-Valdez, H.; De La Cruz-Burelo, E.; Heredia-De La Cruz, I.; Rabadan-Trejo, R. I.; Lopez-Fernandez, R.; Mejia Guisao, J.; Sanchez-Hernandez, A.; Carrillo Moreno, S.; Oropeza Barrera, C.; Vazquez Valencia, F.; Pedraza, I.; Salazar Ibarguen, H. A.; Uribe Estrada, C.; Morelos Pineda, A.; Krofcheck, D.; Butler, P. H.; Ahmad, A.; Ahmad, M.; Hassan, Q.; Hoorani, H. R.; Saddique, A.; Shah, M. A.; Shoaib, M.; Waqas, M.; Bialkowska, H.; Bluj, M.; Boimska, B.; Frueboes, T.; Górski, M.; Kazana, M.; Nawrocki, K.; Szleper, M.; Zalewski, P.; Bunkowski, K.; Byszuk, A.; Doroba, K.; Kalinowski, A.; Konecki, M.; Krolikowski, J.; Misiura, M.; Olszewski, M.; Pyskir, A.; Walczak, M.; Bargassa, P.; Beirão Da Cruz E Silva, C.; Di Francesco, A.; Faccioli, P.; Galinhas, B.; Gallinaro, M.; Hollar, J.; Leonardo, N.; Lloret Iglesias, L.; Nemallapudi, M. 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P.; Flix, J.; Fouz, M. C.; Gonzalez Lopez, O.; Goy Lopez, S.; Hernandez, J. M.; Josa, M. I.; Moran, D.; Pérez-Calero Yzquierdo, A.; Puerta Pelayo, J.; Quintario Olmeda, A.; Redondo, I.; Romero, L.; Soares, M. S.; Álvarez Fernández, A.; Albajar, C.; de Trocóniz, J. F.; Missiroli, M.; Cuevas, J.; Erice, C.; Fernandez Menendez, J.; Gonzalez Caballero, I.; González Fernández, J. R.; Palencia Cortezon, E.; Sanchez Cruz, S.; Vischia, P.; Vizan Garcia, J. M.; Cabrillo, I. J.; Calderon, A.; Chazin Quero, B.; Curras, E.; Duarte Campderros, J.; Fernandez, M.; Garcia-Ferrero, J.; Gomez, G.; Lopez Virto, A.; Marco, J.; Martinez Rivero, C.; Martinez Ruiz del Arbol, P.; Matorras, F.; Piedra Gomez, J.; Rodrigo, T.; Ruiz-Jimeno, A.; Scodellaro, L.; Trevisani, N.; Vila, I.; Vilar Cortabitarte, R.; Abbaneo, D.; Akgun, B.; Auffray, E.; Baillon, P.; Ball, A. H.; Barney, D.; Bendavid, J.; Bianco, M.; Bloch, P.; Bocci, A.; Botta, C.; Camporesi, T.; Castello, R.; Cepeda, M.; Cerminara, G.; Chapon, E.; Chen, Y.; d'Enterria, D.; Dabrowski, A.; Daponte, V.; David, A.; De Gruttola, M.; De Roeck, A.; Deelen, N.; Dobson, M.; du Pree, T.; Dünser, M.; Dupont, N.; Elliott-Peisert, A.; Everaerts, P.; Fallavollita, F.; Franzoni, G.; Fulcher, J.; Funk, W.; Gigi, D.; Gilbert, A.; Gill, K.; Glege, F.; Gulhan, D.; Harris, P.; Hegeman, J.; Innocente, V.; Jafari, A.; Janot, P.; Karacheban, O.; Kieseler, J.; Knünz, V.; Kornmayer, A.; Kortelainen, M. J.; Krammer, M.; Lange, C.; Lecoq, P.; Lourenço, C.; Lucchini, M. T.; Malgeri, L.; Mannelli, M.; Martelli, A.; Meijers, F.; Merlin, J. A.; Mersi, S.; Meschi, E.; Milenovic, P.; Moortgat, F.; Mulders, M.; Neugebauer, H.; Ngadiuba, J.; Orfanelli, S.; Orsini, L.; Pape, L.; Perez, E.; Peruzzi, M.; Petrilli, A.; Petrucciani, G.; Pfeiffer, A.; Pierini, M.; Rabady, D.; Racz, A.; Reis, T.; Rolandi, G.; Rovere, M.; Sakulin, H.; Schäfer, C.; Schwick, C.; Seidel, M.; Selvaggi, M.; Sharma, A.; Silva, P.; Sphicas, P.; Stakia, A.; Steggemann, J.; Stoye, M.; Tosi, M.; Treille, D.; Triossi, A.; Tsirou, A.; Veckalns, V.; Verweij, M.; Zeuner, W. D.; Bertl, W.; Caminada, L.; Deiters, K.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; Kotlinski, D.; Langenegger, U.; Rohe, T.; Wiederkehr, S. A.; Backhaus, M.; Bäni, L.; Berger, P.; Bianchini, L.; Casal, B.; Dissertori, G.; Dittmar, M.; Donegà, M.; Dorfer, C.; Grab, C.; Heidegger, C.; Hits, D.; Hoss, J.; Kasieczka, G.; Klijnsma, T.; Lustermann, W.; Mangano, B.; Marionneau, M.; Meinhard, M. T.; Meister, D.; Micheli, F.; Musella, P.; Nessi-Tedaldi, F.; Pandolfi, F.; Pata, J.; Pauss, F.; Perrin, G.; Perrozzi, L.; Quittnat, M.; Reichmann, M.; Sanz Becerra, D. A.; Schönenberger, M.; Shchutska, L.; Tavolaro, V. R.; Theofilatos, K.; Vesterbacka Olsson, M. L.; Wallny, R.; Zhu, D. H.; Aarrestad, T. K.; Amsler, C.; Canelli, M. F.; De Cosa, A.; Del Burgo, R.; Donato, S.; Galloni, C.; Hreus, T.; Kilminster, B.; Pinna, D.; Rauco, G.; Robmann, P.; Salerno, D.; Schweiger, K.; Seitz, C.; Takahashi, Y.; Zucchetta, A.; Candelise, V.; Doan, T. H.; Jain, Sh.; Khurana, R.; Kuo, C. M.; Lin, W.; Pozdnyakov, A.; Yu, S. S.; Kumar, Arun; Chang, P.; Chao, Y.; Chen, K. F.; Chen, P. H.; Fiori, F.; Hou, W.-S.; Hsiung, Y.; Liu, Y. F.; Lu, R.-S.; Paganis, E.; Psallidas, A.; Steen, A.; Tsai, J. f.; Asavapibhop, B.; Kovitanggoon, K.; Singh, G.; Srimanobhas, N.; Bakirci, M. N.; Bat, A.; Boran, F.; Damarseckin, S.; Demiroglu, Z. S.; Dozen, C.; Eskut, E.; Girgis, S.; Gokbulut, G.; Guler, Y.; Hos, I.; Kangal, E. E.; Kara, O.; Kiminsu, U.; Oglakci, M.; Onengut, G.; Ozdemir, K.; Ozturk, S.; Tali, B.; Tok, U. G.; Topakli, H.; Turkcapar, S.; Zorbakir, I. S.; Zorbilmez, C.; Bilin, B.; Karapinar, G.; Ocalan, K.; Yalvac, M.; Zeyrek, M.; Gülmez, E.; Kaya, M.; Kaya, O.; Tekten, S.; Yetkin, E. A.; Agaras, M. N.; Atay, S.; Cakir, A.; Cankocak, K.; Grynyov, B.; Levchuk, L.; Ball, F.; Beck, L.; Brooke, J. J.; Burns, D.; Clement, E.; Cussans, D.; Davignon, O.; Flacher, H.; Goldstein, J.; Heath, G. P.; Heath, H. F.; Kreczko, L.; Newbold, D. M.; Paramesvaran, S.; Sakuma, T.; Seif El Nasr-storey, S.; Smith, D.; Smith, V. J.; Bell, K. W.; Belyaev, A.; Brew, C.; Brown, R. M.; Calligaris, L.; Cieri, D.; Cockerill, D. J. A.; Coughlan, J. A.; Harder, K.; Harper, S.; Olaiya, E.; Petyt, D.; Shepherd-Themistocleous, C. H.; Thea, A.; Tomalin, I. R.; Williams, T.; Auzinger, G.; Bainbridge, R.; Borg, J.; Breeze, S.; Buchmuller, O.; Bundock, A.; Casasso, S.; Citron, M.; Colling, D.; Corpe, L.; Dauncey, P.; Davies, G.; De Wit, A.; Della Negra, M.; Di Maria, R.; Elwood, A.; Haddad, Y.; Hall, G.; Iles, G.; James, T.; Lane, R.; Laner, C.; Lyons, L.; Magnan, A.-M.; Malik, S.; Mastrolorenzo, L.; Matsushita, T.; Nash, J.; Nikitenko, A.; Palladino, V.; Pesaresi, M.; Raymond, D. M.; Richards, A.; Rose, A.; Scott, E.; Seez, C.; Shtipliyski, A.; Summers, S.; Tapper, A.; Uchida, K.; Vazquez Acosta, M.; Virdee, T.; Wardle, N.; Winterbottom, D.; Wright, J.; Zenz, S. C.; Cole, J. E.; Hobson, P. R.; Khan, A.; Kyberd, P.; Reid, I. D.; Symonds, P.; Teodorescu, L.; Turner, M.; Zahid, S.; Borzou, A.; Call, K.; Dittmann, J.; Hatakeyama, K.; Liu, H.; Pastika, N.; Smith, C.; Bartek, R.; Dominguez, A.; Buccilli, A.; Cooper, S. I.; Henderson, C.; Rumerio, P.; West, C.; Arcaro, D.; Avetisyan, A.; Bose, T.; Gastler, D.; Rankin, D.; Richardson, C.; Rohlf, J.; Sulak, L.; Zou, D.; Benelli, G.; Cutts, D.; Garabedian, A.; Hadley, M.; Hakala, J.; Heintz, U.; Hogan, J. M.; Kwok, K. H. M.; Laird, E.; Landsberg, G.; Lee, J.; Mao, Z.; Narain, M.; Pazzini, J.; Piperov, S.; Sagir, S.; Syarif, R.; Yu, D.; Band, R.; Brainerd, C.; Burns, D.; Calderon De La Barca Sanchez, M.; Chertok, M.; Conway, J.; Conway, R.; Cox, P. T.; Erbacher, R.; Flores, C.; Funk, G.; Gardner, M.; Ko, W.; Lander, R.; Mclean, C.; Mulhearn, M.; Pellett, D.; Pilot, J.; Shalhout, S.; Shi, M.; Smith, J.; Stolp, D.; Tos, K.; Tripathi, M.; Wang, Z.; Bachtis, M.; Bravo, C.; Cousins, R.; Dasgupta, A.; Florent, A.; Hauser, J.; Ignatenko, M.; Mccoll, N.; Regnard, S.; Saltzberg, D.; Schnaible, C.; Valuev, V.; Bouvier, E.; Burt, K.; Clare, R.; Ellison, J.; Gary, J. W.; Ghiasi Shirazi, S. M. A.; Hanson, G.; Heilman, J.; Kennedy, E.; Lacroix, F.; Long, O. R.; Olmedo Negrete, M.; Paneva, M. I.; Si, W.; Wang, L.; Wei, H.; Wimpenny, S.; Yates, B. R.; Branson, J. G.; Cittolin, S.; Derdzinski, M.; Gerosa, R.; Gilbert, D.; Hashemi, B.; Holzner, A.; Klein, D.; Kole, G.; Krutelyov, V.; Letts, J.; Macneill, I.; Masciovecchio, M.; Olivito, D.; Padhi, S.; Pieri, M.; Sani, M.; Sharma, V.; Simon, S.; Tadel, M.; Vartak, A.; Wasserbaech, S.; Wood, J.; Würthwein, F.; Yagil, A.; Zevi Della Porta, G.; Amin, N.; Bhandari, R.; Bradmiller-Feld, J.; Campagnari, C.; Dishaw, A.; Dutta, V.; Franco Sevilla, M.; George, C.; Golf, F.; Gouskos, L.; Gran, J.; Heller, R.; Incandela, J.; Mullin, S. D.; Ovcharova, A.; Qu, H.; Richman, J.; Stuart, D.; Suarez, I.; Yoo, J.; Anderson, D.; Bornheim, A.; Lawhorn, J. M.; Newman, H. B.; Nguyen, T.; Pena, C.; Spiropulu, M.; Vlimant, J. R.; Xie, S.; Zhang, Z.; Zhu, R. Y.; Andrews, M. B.; Ferguson, T.; Mudholkar, T.; Paulini, M.; Russ, J.; Sun, M.; Vogel, H.; Vorobiev, I.; Weinberg, M.; Cumalat, J. P.; Ford, W. T.; Jensen, F.; Johnson, A.; Krohn, M.; Leontsinis, S.; Mulholland, T.; Stenson, K.; Wagner, S. R.; Alexander, J.; Chaves, J.; Chu, J.; Dittmer, S.; Mcdermott, K.; Mirman, N.; Patterson, J. R.; Quach, D.; Rinkevicius, A.; Ryd, A.; Skinnari, L.; Soffi, L.; Tan, S. M.; Tao, Z.; Thom, J.; Tucker, J.; Wittich, P.; Zientek, M.; Abdullin, S.; Albrow, M.; Alyari, M.; Apollinari, G.; Apresyan, A.; Apyan, A.; Banerjee, S.; Bauerdick, L. A. T.; Beretvas, A.; Berryhill, J.; Bhat, P. C.; Bolla, G.; Burkett, K.; Butler, J. N.; Canepa, A.; Cerati, G. B.; Cheung, H. W. K.; Chlebana, F.; Cremonesi, M.; Duarte, J.; Elvira, V. D.; Freeman, J.; Gecse, Z.; Gottschalk, E.; Gray, L.; Green, D.; Grünendahl, S.; Gutsche, O.; Harris, R. M.; Hasegawa, S.; Hirschauer, J.; Hu, Z.; Jayatilaka, B.; Jindariani, S.; Johnson, M.; Joshi, U.; Klima, B.; Kreis, B.; Lammel, S.; Lincoln, D.; Lipton, R.; Liu, M.; Liu, T.; Lopes De Sá, R.; Lykken, J.; Maeshima, K.; Magini, N.; Marraffino, J. M.; Mason, D.; McBride, P.; Merkel, P.; Mrenna, S.; Nahn, S.; O'Dell, V.; Pedro, K.; Prokofyev, O.; Rakness, G.; Ristori, L.; Schneider, B.; Sexton-Kennedy, E.; Soha, A.; Spalding, W. J.; Spiegel, L.; Stoynev, S.; Strait, J.; Strobbe, N.; Taylor, L.; Tkaczyk, S.; Tran, N. V.; Uplegger, L.; Vaandering, E. W.; Vernieri, C.; Verzocchi, M.; Vidal, R.; Wang, M.; Weber, H. A.; Whitbeck, A.; Acosta, D.; Avery, P.; Bortignon, P.; Bourilkov, D.; Brinkerhoff, A.; Carnes, A.; Carver, M.; Curry, D.; Field, R. D.; Furic, I. K.; Gleyzer, S. V.; Joshi, B. M.; Konigsberg, J.; Korytov, A.; Kotov, K.; Ma, P.; Matchev, K.; Mei, H.; Mitselmakher, G.; Rank, D.; Shi, K.; Sperka, D.; Terentyev, N.; Thomas, L.; Wang, J.; Wang, S.; Yelton, J.; Joshi, Y. R.; Linn, S.; Markowitz, P.; Rodriguez, J. L.; Ackert, A.; Adams, T.; Askew, A.; Hagopian, S.; Hagopian, V.; Johnson, K. F.; Kolberg, T.; Martinez, G.; Perry, T.; Prosper, H.; Saha, A.; Santra, A.; Sharma, V.; Yohay, R.; Baarmand, M. M.; Bhopatkar, V.; Colafranceschi, S.; Hohlmann, M.; Noonan, D.; Roy, T.; Yumiceva, F.; Adams, M. R.; Apanasevich, L.; Berry, D.; Betts, R. R.; Cavanaugh, R.; Chen, X.; Evdokimov, O.; Gerber, C. E.; Hangal, D. A.; Hofman, D. J.; Jung, K.; Kamin, J.; Sandoval Gonzalez, I. D.; Tonjes, M. B.; Trauger, H.; Varelas, N.; Wang, H.; Wu, Z.; Zhang, J.; Bilki, B.; Clarida, W.; Dilsiz, K.; Durgut, S.; Gandrajula, R. P.; Haytmyradov, M.; Khristenko, V.; Merlo, J.-P.; Mermerkaya, H.; Mestvirishvili, A.; Moeller, A.; Nachtman, J.; Ogul, H.; Onel, Y.; Ozok, F.; Penzo, A.; Snyder, C.; Tiras, E.; Wetzel, J.; Yi, K.; Blumenfeld, B.; Cocoros, A.; Eminizer, N.; Fehling, D.; Feng, L.; Gritsan, A. V.; Maksimovic, P.; Roskes, J.; Sarica, U.; Swartz, M.; Xiao, M.; You, C.; Al-bataineh, A.; Baringer, P.; Bean, A.; Boren, S.; Bowen, J.; Castle, J.; Khalil, S.; Kropivnitskaya, A.; Majumder, D.; Mcbrayer, W.; Murray, M.; Royon, C.; Sanders, S.; Schmitz, E.; Tapia Takaki, J. D.; Wang, Q.; Ivanov, A.; Kaadze, K.; Maravin, Y.; Mohammadi, A.; Saini, L. K.; Skhirtladze, N.; Toda, S.; Rebassoo, F.; Wright, D.; Anelli, C.; Baden, A.; Baron, O.; Belloni, A.; Calvert, B.; Eno, S. C.; Feng, Y.; Ferraioli, C.; Hadley, N. J.; Jabeen, S.; Jeng, G. Y.; Kellogg, R. G.; Kunkle, J.; Mignerey, A. C.; Ricci-Tam, F.; Shin, Y. H.; Skuja, A.; Tonwar, S. C.; Abercrombie, D.; Allen, B.; Azzolini, V.; Barbieri, R.; Baty, A.; Bi, R.; Brandt, S.; Busza, W.; Cali, I. A.; D'Alfonso, M.; Demiragli, Z.; Gomez Ceballos, G.; Goncharov, M.; Hsu, D.; Hu, M.; Iiyama, Y.; Innocenti, G. M.; Klute, M.; Kovalskyi, D.; Lai, Y. S.; Lee, Y.-J.; Levin, A.; Luckey, P. D.; Maier, B.; Marini, A. C.; Mcginn, C.; Mironov, C.; Narayanan, S.; Niu, X.; Paus, C.; Roland, C.; Roland, G.; Salfeld-Nebgen, J.; Stephans, G. S. F.; Tatar, K.; Velicanu, D.; Wang, J.; Wang, T. W.; Wyslouch, B.; Benvenuti, A. C.; Chatterjee, R. M.; Evans, A.; Hansen, P.; Hiltbrand, J.; Kalafut, S.; Kubota, Y.; Lesko, Z.; Mans, J.; Nourbakhsh, S.; Ruckstuhl, N.; Rusack, R.; Turkewitz, J.; Wadud, M. A.; Acosta, J. G.; Oliveros, S.; Avdeeva, E.; Bloom, K.; Claes, D. R.; Fangmeier, C.; Gonzalez Suarez, R.; Kamalieddin, R.; Kravchenko, I.; Monroy, J.; Siado, J. E.; Snow, G. R.; Stieger, B.; Dolen, J.; Godshalk, A.; Harrington, C.; Iashvili, I.; Nguyen, D.; Parker, A.; Rappoccio, S.; Roozbahani, B.; Alverson, G.; Barberis, E.; Hortiangtham, A.; Massironi, A.; Morse, D. M.; Orimoto, T.; Teixeira De Lima, R.; Trocino, D.; Wood, D.; Bhattacharya, S.; Charaf, O.; Hahn, K. A.; Mucia, N.; Odell, N.; Pollack, B.; Schmitt, M. H.; Sung, K.; Trovato, M.; Velasco, M.; Dev, N.; Hildreth, M.; Hurtado Anampa, K.; Jessop, C.; Karmgard, D. J.; Kellams, N.; Lannon, K.; Loukas, N.; Marinelli, N.; Meng, F.; Mueller, C.; Musienko, Y.; Planer, M.; Reinsvold, A.; Ruchti, R.; Smith, G.; Taroni, S.; Wayne, M.; Wolf, M.; Woodard, A.; Alimena, J.; Antonelli, L.; Bylsma, B.; Durkin, L. S.; Flowers, S.; Francis, B.; Hart, A.; Hill, C.; Ji, W.; Liu, B.; Luo, W.; Puigh, D.; Winer, B. L.; Wulsin, H. W.; Cooperstein, S.; Driga, O.; Elmer, P.; Hardenbrook, J.; Hebda, P.; Higginbotham, S.; Lange, D.; Luo, J.; Marlow, D.; Mei, K.; Ojalvo, I.; Olsen, J.; Palmer, C.; Piroué, P.; Stickland, D.; Tully, C.; Malik, S.; Norberg, S.; Barker, A.; Barnes, V. E.; Das, S.; Folgueras, S.; Gutay, L.; Jha, M. K.; Jones, M.; Jung, A. W.; Khatiwada, A.; Miller, D. H.; Neumeister, N.; Peng, C. C.; Qiu, H.; Schulte, J. F.; Sun, J.; Wang, F.; Xie, W.; Cheng, T.; Parashar, N.; Stupak, J.; Adair, A.; Chen, Z.; Ecklund, K. M.; Freed, S.; Geurts, F. J. M.; Guilbaud, M.; Kilpatrick, M.; Li, W.; Michlin, B.; Northup, M.; Padley, B. P.; Roberts, J.; Rorie, J.; Shi, W.; Tu, Z.; Zabel, J.; Zhang, A.; Bodek, A.; de Barbaro, P.; Demina, R.; Duh, Y. t.; Ferbel, T.; Galanti, M.; Garcia-Bellido, A.; Han, J.; Hindrichs, O.; Khukhunaishvili, A.; Lo, K. H.; Tan, P.; Verzetti, M.; Ciesielski, R.; Goulianos, K.; Mesropian, C.; Agapitos, A.; Chou, J. P.; Gershtein, Y.; Gómez Espinosa, T. A.; Halkiadakis, E.; Heindl, M.; Hughes, E.; Kaplan, S.; Kunnawalkam Elayavalli, R.; Kyriacou, S.; Lath, A.; Montalvo, R.; Nash, K.; Osherson, M.; Saka, H.; Salur, S.; Schnetzer, S.; Sheffield, D.; Somalwar, S.; Stone, R.; Thomas, S.; Thomassen, P.; Walker, M.; Delannoy, A. G.; Foerster, M.; Heideman, J.; Riley, G.; Rose, K.; Spanier, S.; Thapa, K.; Bouhali, O.; Castaneda Hernandez, A.; Celik, A.; Dalchenko, M.; De Mattia, M.; Delgado, A.; Dildick, S.; Eusebi, R.; Gilmore, J.; Huang, T.; Kamon, T.; Mueller, R.; Pakhotin, Y.; Patel, R.; Perloff, A.; Perniè, L.; Rathjens, D.; Safonov, A.; Tatarinov, A.; Ulmer, K. A.; Akchurin, N.; Damgov, J.; De Guio, F.; Dudero, P. R.; Faulkner, J.; Gurpinar, E.; Kunori, S.; Lamichhane, K.; Lee, S. W.; Libeiro, T.; Mengke, T.; Muthumuni, S.; Peltola, T.; Undleeb, S.; Volobouev, I.; Wang, Z.; Greene, S.; Gurrola, A.; Janjam, R.; Johns, W.; Maguire, C.; Melo, A.; Ni, H.; Padeken, K.; Sheldon, P.; Tuo, S.; Velkovska, J.; Xu, Q.; Arenton, M. W.; Barria, P.; Cox, B.; Hirosky, R.; Joyce, M.; Ledovskoy, A.; Li, H.; Neu, C.; Sinthuprasith, T.; Wang, Y.; Wolfe, E.; Xia, F.; Harr, R.; Karchin, P. E.; Poudyal, N.; Sturdy, J.; Thapa, P.; Zaleski, S.; Brodski, M.; Buchanan, J.; Caillol, C.; Dasu, S.; Dodd, L.; Duric, S.; Gomber, B.; Grothe, M.; Herndon, M.; Hervé, A.; Hussain, U.; Klabbers, P.; Lanaro, A.; Levine, A.; Long, K.; Loveless, R.; Polese, G.; Ruggles, T.; Savin, A.; Smith, N.; Smith, W. H.; Taylor, D.; Woods, N.; CMS Collaboration

2018-06-01

Angular distributions of the decay B0 →K*0μ+μ- are studied using a sample of proton-proton collisions at √{ s } = 8TeV collected with the CMS detector at the LHC, corresponding to an integrated luminosity of 20.5fb-1. An angular analysis is performed to determine the P1 and P5‧ parameters, where the P5‧ parameter is of particular interest because of recent measurements that indicate a potential discrepancy with the standard model predictions. Based on a sample of 1397 signal events, the P1 and P5‧ parameters are determined as a function of the dimuon invariant mass squared. The measurements are in agreement with predictions based on the standard model.

Multistate modelling extended by behavioural rules: An application to migration.

PubMed

Klabunde, Anna; Zinn, Sabine; Willekens, Frans; Leuchter, Matthias

2017-10-01

We propose to extend demographic multistate models by adding a behavioural element: behavioural rules explain intentions and thus transitions. Our framework is inspired by the Theory of Planned Behaviour. We exemplify our approach with a model of migration from Senegal to France. Model parameters are determined using empirical data where available. Parameters for which no empirical correspondence exists are determined by calibration. Age- and period-specific migration rates are used for model validation. Our approach adds to the toolkit of demographic projection by allowing for shocks and social influence, which alter behaviour in non-linear ways, while sticking to the general framework of multistate modelling. Our simulations yield that higher income growth in Senegal leads to higher emigration rates in the medium term, while a decrease in fertility yields lower emigration rates.

Inferring pathological states in cortical neuron microcircuits.

PubMed

Rydzewski, Jakub; Nowak, Wieslaw; Nicosia, Giuseppe

2015-12-07

The brain activity is to a large extent determined by states of neural cortex microcircuits. Unfortunately, accuracy of results from neural circuits׳ mathematical models is often biased by the presence of uncertainties in underlying experimental data. Moreover, due to problems with uncertainties identification in a multidimensional parameters space, it is almost impossible to classify states of the neural cortex, which correspond to a particular set of the parameters. Here, we develop a complete methodology for determining uncertainties and the novel protocol for classifying all states in any neuroinformatic model. Further, we test this protocol on the mathematical, nonlinear model of such a microcircuit developed by Giugliano et al. (2008) and applied in the experimental data analysis of Huntington׳s disease. Up to now, the link between parameter domains in the mathematical model of Huntington׳s disease and the pathological states in cortical microcircuits has remained unclear. In this paper we precisely identify all the uncertainties, the most crucial input parameters and domains that drive the system into an unhealthy state. The scheme proposed here is general and can be easily applied to other mathematical models of biological phenomena. Copyright © 2015 Elsevier Ltd. All rights reserved.

Method-independent, Computationally Frugal Convergence Testing for Sensitivity Analysis Techniques

NASA Astrophysics Data System (ADS)

Mai, Juliane; Tolson, Bryan

2017-04-01

The increasing complexity and runtime of environmental models lead to the current situation that the calibration of all model parameters or the estimation of all of their uncertainty is often computationally infeasible. Hence, techniques to determine the sensitivity of model parameters are used to identify most important parameters or model processes. All subsequent model calibrations or uncertainty estimation procedures focus then only on these subsets of parameters and are hence less computational demanding. While the examination of the convergence of calibration and uncertainty methods is state-of-the-art, the convergence of the sensitivity methods is usually not checked. If any, bootstrapping of the sensitivity results is used to determine the reliability of the estimated indexes. Bootstrapping, however, might as well become computationally expensive in case of large model outputs and a high number of bootstraps. We, therefore, present a Model Variable Augmentation (MVA) approach to check the convergence of sensitivity indexes without performing any additional model run. This technique is method- and model-independent. It can be applied either during the sensitivity analysis (SA) or afterwards. The latter case enables the checking of already processed sensitivity indexes. To demonstrate the method independency of the convergence testing method, we applied it to three widely used, global SA methods: the screening method known as Morris method or Elementary Effects (Morris 1991, Campolongo et al., 2000), the variance-based Sobol' method (Solbol' 1993, Saltelli et al. 2010) and a derivative-based method known as Parameter Importance index (Goehler et al. 2013). The new convergence testing method is first scrutinized using 12 analytical benchmark functions (Cuntz & Mai et al. 2015) where the true indexes of aforementioned three methods are known. This proof of principle shows that the method reliably determines the uncertainty of the SA results when different budgets are used for the SA. Subsequently, we focus on the model-independency by testing the frugal method using the hydrologic model mHM (www.ufz.de/mhm) with about 50 model parameters. The results show that the new frugal method is able to test the convergence and therefore the reliability of SA results in an efficient way. The appealing feature of this new technique is the necessity of no further model evaluation and therefore enables checking of already processed (and published) sensitivity results. This is one step towards reliable and transferable, published sensitivity results.

Lumped-parameters equivalent circuit for condenser microphones modeling.

PubMed

Esteves, Josué; Rufer, Libor; Ekeom, Didace; Basrour, Skandar

2017-10-01

This work presents a lumped parameters equivalent model of condenser microphone based on analogies between acoustic, mechanical, fluidic, and electrical domains. Parameters of the model were determined mainly through analytical relations and/or finite element method (FEM) simulations. Special attention was paid to the air gap modeling and to the use of proper boundary condition. Corresponding lumped-parameters were obtained as results of FEM simulations. Because of its simplicity, the model allows a fast simulation and is readily usable for microphone design. This work shows the validation of the equivalent circuit on three real cases of capacitive microphones, including both traditional and Micro-Electro-Mechanical Systems structures. In all cases, it has been demonstrated that the sensitivity and other related data obtained from the equivalent circuit are in very good agreement with available measurement data.

Rate-equation modelling and ensemble approach to extraction of parameters for viral infection-induced cell apoptosis and necrosis

NASA Astrophysics Data System (ADS)

Domanskyi, Sergii; Schilling, Joshua E.; Gorshkov, Vyacheslav; Libert, Sergiy; Privman, Vladimir

2016-09-01

We develop a theoretical approach that uses physiochemical kinetics modelling to describe cell population dynamics upon progression of viral infection in cell culture, which results in cell apoptosis (programmed cell death) and necrosis (direct cell death). Several model parameters necessary for computer simulation were determined by reviewing and analyzing available published experimental data. By comparing experimental data to computer modelling results, we identify the parameters that are the most sensitive to the measured system properties and allow for the best data fitting. Our model allows extraction of parameters from experimental data and also has predictive power. Using the model we describe interesting time-dependent quantities that were not directly measured in the experiment and identify correlations among the fitted parameter values. Numerical simulation of viral infection progression is done by a rate-equation approach resulting in a system of "stiff" equations, which are solved by using a novel variant of the stochastic ensemble modelling approach. The latter was originally developed for coupled chemical reactions.

Rate-equation modelling and ensemble approach to extraction of parameters for viral infection-induced cell apoptosis and necrosis

NASA Astrophysics Data System (ADS)

Domanskyi, Sergii; Schilling, Joshua; Gorshkov, Vyacheslav; Libert, Sergiy; Privman, Vladimir

We develop a theoretical approach that uses physiochemical kinetics modelling to describe cell population dynamics upon progression of viral infection in cell culture, which results in cell apoptosis (programmed cell death) and necrosis (direct cell death). Several model parameters necessary for computer simulation were determined by reviewing and analyzing available published experimental data. By comparing experimental data to computer modelling results, we identify the parameters that are the most sensitive to the measured system properties and allow for the best data fitting. Our model allows extraction of parameters from experimental data and also has predictive power. Using the model we describe interesting time-dependent quantities that were not directly measured in the experiment and identify correlations among the fitted parameter values. Numerical simulation of viral infection progression is done by a rate-equation approach resulting in a system of ``stiff'' equations, which are solved by using a novel variant of the stochastic ensemble modelling approach. The latter was originally developed for coupled chemical reactions.

Use of multilevel modeling for determining optimal parameters of heat supply systems

NASA Astrophysics Data System (ADS)

Stennikov, V. A.; Barakhtenko, E. A.; Sokolov, D. V.

2017-07-01

The problem of finding optimal parameters of a heat-supply system (HSS) is in ensuring the required throughput capacity of a heat network by determining pipeline diameters and characteristics and location of pumping stations. Effective methods for solving this problem, i.e., the method of stepwise optimization based on the concept of dynamic programming and the method of multicircuit optimization, were proposed in the context of the hydraulic circuit theory developed at Melentiev Energy Systems Institute (Siberian Branch, Russian Academy of Sciences). These methods enable us to determine optimal parameters of various types of piping systems due to flexible adaptability of the calculation procedure to intricate nonlinear mathematical models describing features of used equipment items and methods of their construction and operation. The new and most significant results achieved in developing methodological support and software for finding optimal parameters of complex heat supply systems are presented: a new procedure for solving the problem based on multilevel decomposition of a heat network model that makes it possible to proceed from the initial problem to a set of interrelated, less cumbersome subproblems with reduced dimensionality; a new algorithm implementing the method of multicircuit optimization and focused on the calculation of a hierarchical model of a heat supply system; the SOSNA software system for determining optimum parameters of intricate heat-supply systems and implementing the developed methodological foundation. The proposed procedure and algorithm enable us to solve engineering problems of finding the optimal parameters of multicircuit heat supply systems having large (real) dimensionality, and are applied in solving urgent problems related to the optimal development and reconstruction of these systems. The developed methodological foundation and software can be used for designing heat supply systems in the Central and the Admiralty regions in St. Petersburg, the city of Bratsk, and the Magistral'nyi settlement.

Consistency of the free-volume approach to the homogeneous deformation of metallic glasses

NASA Astrophysics Data System (ADS)

Blétry, Marc; Thai, Minh Thanh; Champion, Yannick; Perrière, Loïc; Ochin, Patrick

2014-05-01

One of the most widely used approaches to model metallic-glasses high-temperature homogeneous deformation is the free-volume theory, developed by Cohen and Turnbull and extended by Spaepen. A simple elastoviscoplastic formulation has been proposed that allows one to determine various parameters of such a model. This approach is applied here to the results obtained by de Hey et al. on a Pd-based metallic glass. In their study, de Hey et al. were able to determine some of the parameters used in the elastoviscoplastic formulation through DSC modeling coupled with mechanical tests, and the consistency of the two viewpoints was assessed.

Determination of Geometric and Kinematical Parameters of Coronal Mass Ejections Using STEREO Data

NASA Astrophysics Data System (ADS)

Fainshtein, V. G.; Tsivileva, D. M.; Kashapova, L. K.

2010-03-01

We present a new, relatively simple and fast method to determine true geometric and kinematical CME parameters from simultaneous STEREO A, B observations of CMEs. These parameters are the three-dimensional direction of CME propagation, velocity and acceleration of CME front, CME angular sizes and front position depending on time. The method is based on the assumption that CME shape may be described by a modification of so-called ice-cream cone models. The method has been tested for several CMEs.

Unscented Kalman filter with parameter identifiability analysis for the estimation of multiple parameters in kinetic models

PubMed Central

2011-01-01

In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. What differentiates this approach is the integration of an orthogonal-based local identifiability method into the unscented Kalman filter (UKF), rather than using the more common observability-based method which has inherent limitations. It also introduces a variable step size based on the system uncertainty of the UKF during the sensitivity calculation. This method identified 10 out of 12 parameters as identifiable. These ten parameters were estimated using the UKF, which was run 97 times. Throughout the repetitions the UKF proved to be more consistent than the estimation algorithms used for comparison. PMID:21989173

Method and system for monitoring and displaying engine performance parameters

NASA Technical Reports Server (NTRS)

Abbott, Terence S. (Inventor); Person, Jr., Lee H. (Inventor)

1991-01-01

The invention is a method and system for monitoring and directly displaying the actual thrust produced by a jet aircraft engine under determined operating conditions and the available thrust and predicted (commanded) thrust of a functional model of an ideal engine under the same determined operating conditions. A first set of actual value output signals representative of a plurality of actual performance parameters of the engine under the determined operating conditions is generated and compared with a second set of predicted value output signals representative of the predicted value of corresponding performance parameters of a functional model of the engine under the determined operating conditions to produce a third set of difference value output signals within a range of normal, caution, or warning limit values. A thrust indicator displays when any one of the actual value output signals is in the warning range while shaping function means shape each of the respective difference output signals as each approaches the limit of the respective normal, caution, and warning range limits.

Rapid condition assessment of structural condition after a blast using state-space identification

NASA Astrophysics Data System (ADS)

Eskew, Edward; Jang, Shinae

2015-04-01

After a blast event, it is important to quickly quantify the structural damage for emergency operations. In order improve the speed, accuracy, and efficiency of condition assessments after a blast, the authors have previously performed work to develop a methodology for rapid assessment of the structural condition of a building after a blast. The method involved determining a post-event equivalent stiffness matrix using vibration measurements and a finite element (FE) model. A structural model was built for the damaged structure based on the equivalent stiffness, and inter-story drifts from the blast are determined using numerical simulations, with forces determined from the blast parameters. The inter-story drifts are then compared to blast design conditions to assess the structures damage. This method still involved engineering judgment in terms of determining significant frequencies, which can lead to error, especially with noisy measurements. In an effort to improve accuracy and automate the process, this paper will look into a similar method of rapid condition assessment using subspace state-space identification. The accuracy of the method will be tested using a benchmark structural model, as well as experimental testing. The blast damage assessments will be validated using pressure-impulse (P-I) diagrams, which present the condition limits across blast parameters. Comparisons between P-I diagrams generated using the true system parameters and equivalent parameters will show the accuracy of the rapid condition based blast assessments.

Global Sensitivity Analysis and Parameter Calibration for an Ecosystem Carbon Model

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

Electrostatics of cysteine residues in proteins: Parameterization and validation of a simple model

PubMed Central

Salsbury, Freddie R.; Poole, Leslie B.; Fetrow, Jacquelyn S.

2013-01-01

One of the most popular and simple models for the calculation of pKas from a protein structure is the semi-macroscopic electrostatic model MEAD. This model requires empirical parameters for each residue to calculate pKas. Analysis of current, widely used empirical parameters for cysteine residues showed that they did not reproduce expected cysteine pKas; thus, we set out to identify parameters consistent with the CHARMM27 force field that capture both the behavior of typical cysteines in proteins and the behavior of cysteines which have perturbed pKas. The new parameters were validated in three ways: (1) calculation across a large set of typical cysteines in proteins (where the calculations are expected to reproduce expected ensemble behavior); (2) calculation across a set of perturbed cysteines in proteins (where the calculations are expected to reproduce the shifted ensemble behavior); and (3) comparison to experimentally determined pKa values (where the calculation should reproduce the pKa within experimental error). Both the general behavior of cysteines in proteins and the perturbed pKa in some proteins can be predicted reasonably well using the newly determined empirical parameters within the MEAD model for protein electrostatics. This study provides the first general analysis of the electrostatics of cysteines in proteins, with specific attention paid to capturing both the behavior of typical cysteines in a protein and the behavior of cysteines whose pKa should be shifted, and validation of force field parameters for cysteine residues. PMID:22777874

Agent-Based Model with Asymmetric Trading and Herding for Complex Financial Systems

PubMed Central

Chen, Jun-Jie; Zheng, Bo; Tan, Lei

2013-01-01

Background For complex financial systems, the negative and positive return-volatility correlations, i.e., the so-called leverage and anti-leverage effects, are particularly important for the understanding of the price dynamics. However, the microscopic origination of the leverage and anti-leverage effects is still not understood, and how to produce these effects in agent-based modeling remains open. On the other hand, in constructing microscopic models, it is a promising conception to determine model parameters from empirical data rather than from statistical fitting of the results. Methods To study the microscopic origination of the return-volatility correlation in financial systems, we take into account the individual and collective behaviors of investors in real markets, and construct an agent-based model. The agents are linked with each other and trade in groups, and particularly, two novel microscopic mechanisms, i.e., investors’ asymmetric trading and herding in bull and bear markets, are introduced. Further, we propose effective methods to determine the key parameters in our model from historical market data. Results With the model parameters determined for six representative stock-market indices in the world, respectively, we obtain the corresponding leverage or anti-leverage effect from the simulation, and the effect is in agreement with the empirical one on amplitude and duration. At the same time, our model produces other features of the real markets, such as the fat-tail distribution of returns and the long-term correlation of volatilities. Conclusions We reveal that for the leverage and anti-leverage effects, both the investors’ asymmetric trading and herding are essential generation mechanisms. Among the six markets, however, the investors’ trading is approximately symmetric for the five markets which exhibit the leverage effect, thus contributing very little. These two microscopic mechanisms and the methods for the determination of the key parameters can be applied to other complex systems with similar asymmetries. PMID:24278146

Agent-based model with asymmetric trading and herding for complex financial systems.

PubMed

Chen, Jun-Jie; Zheng, Bo; Tan, Lei

2013-01-01

For complex financial systems, the negative and positive return-volatility correlations, i.e., the so-called leverage and anti-leverage effects, are particularly important for the understanding of the price dynamics. However, the microscopic origination of the leverage and anti-leverage effects is still not understood, and how to produce these effects in agent-based modeling remains open. On the other hand, in constructing microscopic models, it is a promising conception to determine model parameters from empirical data rather than from statistical fitting of the results. To study the microscopic origination of the return-volatility correlation in financial systems, we take into account the individual and collective behaviors of investors in real markets, and construct an agent-based model. The agents are linked with each other and trade in groups, and particularly, two novel microscopic mechanisms, i.e., investors' asymmetric trading and herding in bull and bear markets, are introduced. Further, we propose effective methods to determine the key parameters in our model from historical market data. With the model parameters determined for six representative stock-market indices in the world, respectively, we obtain the corresponding leverage or anti-leverage effect from the simulation, and the effect is in agreement with the empirical one on amplitude and duration. At the same time, our model produces other features of the real markets, such as the fat-tail distribution of returns and the long-term correlation of volatilities. We reveal that for the leverage and anti-leverage effects, both the investors' asymmetric trading and herding are essential generation mechanisms. Among the six markets, however, the investors' trading is approximately symmetric for the five markets which exhibit the leverage effect, thus contributing very little. These two microscopic mechanisms and the methods for the determination of the key parameters can be applied to other complex systems with similar asymmetries.

Development of uncertainty-based work injury model using Bayesian structural equation modelling.

PubMed

Chatterjee, Snehamoy

2014-01-01

This paper proposed a Bayesian method-based structural equation model (SEM) of miners' work injury for an underground coal mine in India. The environmental and behavioural variables for work injury were identified and causal relationships were developed. For Bayesian modelling, prior distributions of SEM parameters are necessary to develop the model. In this paper, two approaches were adopted to obtain prior distribution for factor loading parameters and structural parameters of SEM. In the first approach, the prior distributions were considered as a fixed distribution function with specific parameter values, whereas, in the second approach, prior distributions of the parameters were generated from experts' opinions. The posterior distributions of these parameters were obtained by applying Bayesian rule. The Markov Chain Monte Carlo sampling in the form Gibbs sampling was applied for sampling from the posterior distribution. The results revealed that all coefficients of structural and measurement model parameters are statistically significant in experts' opinion-based priors, whereas, two coefficients are not statistically significant when fixed prior-based distributions are applied. The error statistics reveals that Bayesian structural model provides reasonably good fit of work injury with high coefficient of determination (0.91) and less mean squared error as compared to traditional SEM.

An Algorithm for Efficient Maximum Likelihood Estimation and Confidence Interval Determination in Nonlinear Estimation Problems

NASA Technical Reports Server (NTRS)

Murphy, Patrick Charles

1985-01-01

An algorithm for maximum likelihood (ML) estimation is developed with an efficient method for approximating the sensitivities. The algorithm was developed for airplane parameter estimation problems but is well suited for most nonlinear, multivariable, dynamic systems. The ML algorithm relies on a new optimization method referred to as a modified Newton-Raphson with estimated sensitivities (MNRES). MNRES determines sensitivities by using slope information from local surface approximations of each output variable in parameter space. The fitted surface allows sensitivity information to be updated at each iteration with a significant reduction in computational effort. MNRES determines the sensitivities with less computational effort than using either a finite-difference method or integrating the analytically determined sensitivity equations. MNRES eliminates the need to derive sensitivity equations for each new model, thus eliminating algorithm reformulation with each new model and providing flexibility to use model equations in any format that is convenient. A random search technique for determining the confidence limits of ML parameter estimates is applied to nonlinear estimation problems for airplanes. The confidence intervals obtained by the search are compared with Cramer-Rao (CR) bounds at the same confidence level. It is observed that the degree of nonlinearity in the estimation problem is an important factor in the relationship between CR bounds and the error bounds determined by the search technique. The CR bounds were found to be close to the bounds determined by the search when the degree of nonlinearity was small. Beale's measure of nonlinearity is developed in this study for airplane identification problems; it is used to empirically correct confidence levels for the parameter confidence limits. The primary utility of the measure, however, was found to be in predicting the degree of agreement between Cramer-Rao bounds and search estimates.

Modeling motor vehicle crashes using Poisson-gamma models: examining the effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter.

PubMed

Lord, Dominique

2006-07-01

There has been considerable research conducted on the development of statistical models for predicting crashes on highway facilities. Despite numerous advancements made for improving the estimation tools of statistical models, the most common probabilistic structure used for modeling motor vehicle crashes remains the traditional Poisson and Poisson-gamma (or Negative Binomial) distribution; when crash data exhibit over-dispersion, the Poisson-gamma model is usually the model of choice most favored by transportation safety modelers. Crash data collected for safety studies often have the unusual attributes of being characterized by low sample mean values. Studies have shown that the goodness-of-fit of statistical models produced from such datasets can be significantly affected. This issue has been defined as the "low mean problem" (LMP). Despite recent developments on methods to circumvent the LMP and test the goodness-of-fit of models developed using such datasets, no work has so far examined how the LMP affects the fixed dispersion parameter of Poisson-gamma models used for modeling motor vehicle crashes. The dispersion parameter plays an important role in many types of safety studies and should, therefore, be reliably estimated. The primary objective of this research project was to verify whether the LMP affects the estimation of the dispersion parameter and, if it is, to determine the magnitude of the problem. The secondary objective consisted of determining the effects of an unreliably estimated dispersion parameter on common analyses performed in highway safety studies. To accomplish the objectives of the study, a series of Poisson-gamma distributions were simulated using different values describing the mean, the dispersion parameter, and the sample size. Three estimators commonly used by transportation safety modelers for estimating the dispersion parameter of Poisson-gamma models were evaluated: the method of moments, the weighted regression, and the maximum likelihood method. In an attempt to complement the outcome of the simulation study, Poisson-gamma models were fitted to crash data collected in Toronto, Ont. characterized by a low sample mean and small sample size. The study shows that a low sample mean combined with a small sample size can seriously affect the estimation of the dispersion parameter, no matter which estimator is used within the estimation process. The probability the dispersion parameter becomes unreliably estimated increases significantly as the sample mean and sample size decrease. Consequently, the results show that an unreliably estimated dispersion parameter can significantly undermine empirical Bayes (EB) estimates as well as the estimation of confidence intervals for the gamma mean and predicted response. The paper ends with recommendations about minimizing the likelihood of producing Poisson-gamma models with an unreliable dispersion parameter for modeling motor vehicle crashes.

Selecting Design Parameters for Flying Vehicles

NASA Astrophysics Data System (ADS)

Makeev, V. I.; Strel'nikova, E. A.; Trofimenko, P. E.; Bondar', A. V.

2013-09-01

Studying the influence of a number of design parameters of solid-propellant rockets on the longitudinal and lateral dispersion is an important applied problem. A mathematical model of a rigid body of variable mass moving in a disturbed medium exerting both wave drag and friction is considered. The model makes it possible to determine the coefficients of aerodynamic forces and moments, which affect the motion of vehicles, and to assess the effect of design parameters on their accuracy

Physics-based statistical model and simulation method of RF propagation in urban environments

DOEpatents

Pao, Hsueh-Yuan; Dvorak, Steven L.

2010-09-14

A physics-based statistical model and simulation/modeling method and system of electromagnetic wave propagation (wireless communication) in urban environments. In particular, the model is a computationally efficient close-formed parametric model of RF propagation in an urban environment which is extracted from a physics-based statistical wireless channel simulation method and system. The simulation divides the complex urban environment into a network of interconnected urban canyon waveguides which can be analyzed individually; calculates spectral coefficients of modal fields in the waveguides excited by the propagation using a database of statistical impedance boundary conditions which incorporates the complexity of building walls in the propagation model; determines statistical parameters of the calculated modal fields; and determines a parametric propagation model based on the statistical parameters of the calculated modal fields from which predictions of communications capability may be made.

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.

Identification procedure for epistemic uncertainties using inverse fuzzy arithmetic

NASA Astrophysics Data System (ADS)

Haag, T.; Herrmann, J.; Hanss, M.

2010-10-01

For the mathematical representation of systems with epistemic uncertainties, arising, for example, from simplifications in the modeling procedure, models with fuzzy-valued parameters prove to be a suitable and promising approach. In practice, however, the determination of these parameters turns out to be a non-trivial problem. The identification procedure to appropriately update these parameters on the basis of a reference output (measurement or output of an advanced model) requires the solution of an inverse problem. Against this background, an inverse method for the computation of the fuzzy-valued parameters of a model with epistemic uncertainties is presented. This method stands out due to the fact that it only uses feedforward simulations of the model, based on the transformation method of fuzzy arithmetic, along with the reference output. An inversion of the system equations is not necessary. The advancement of the method presented in this paper consists of the identification of multiple input parameters based on a single reference output or measurement. An optimization is used to solve the resulting underdetermined problems by minimizing the uncertainty of the identified parameters. Regions where the identification procedure is reliable are determined by the computation of a feasibility criterion which is also based on the output data of the transformation method only. For a frequency response function of a mechanical system, this criterion allows a restriction of the identification process to some special range of frequency where its solution can be guaranteed. Finally, the practicability of the method is demonstrated by covering the measured output of a fluid-filled piping system by the corresponding uncertain FE model in a conservative way.

Scattering of 30 MeV He3 from Re185

NASA Astrophysics Data System (ADS)

Garrett, P. E.; Phillips, A. A.; Demand, G. A.; Finlay, P.; Green, K. L.; Leach, K. G.; Schumaker, M. A.; Svensson, C. E.; Wong, J.; Hertenberger, R.; Wirth, H.-F.; Faestermann, T.; Krücken, R.; Burke, D. G.; Bettermann, L.; Braun, N.

2009-01-01

The scattering of 30 MeV He3 from a Re185 target has been investigated. The measured elastic scattering is in disagreement with calculations using common optical model parameter sets found in the literature. A new optical model parameter set has been determined that reproduces the data for both the elastic and the inelastic scattering channels.

Bayesian inference for OPC modeling

NASA Astrophysics Data System (ADS)

Burbine, Andrew; Sturtevant, John; Fryer, David; Smith, Bruce W.

2016-03-01

The use of optical proximity correction (OPC) demands increasingly accurate models of the photolithographic process. Model building and inference techniques in the data science community have seen great strides in the past two decades which make better use of available information. This paper aims to demonstrate the predictive power of Bayesian inference as a method for parameter selection in lithographic models by quantifying the uncertainty associated with model inputs and wafer data. Specifically, the method combines the model builder's prior information about each modelling assumption with the maximization of each observation's likelihood as a Student's t-distributed random variable. Through the use of a Markov chain Monte Carlo (MCMC) algorithm, a model's parameter space is explored to find the most credible parameter values. During parameter exploration, the parameters' posterior distributions are generated by applying Bayes' rule, using a likelihood function and the a priori knowledge supplied. The MCMC algorithm used, an affine invariant ensemble sampler (AIES), is implemented by initializing many walkers which semiindependently explore the space. The convergence of these walkers to global maxima of the likelihood volume determine the parameter values' highest density intervals (HDI) to reveal champion models. We show that this method of parameter selection provides insights into the data that traditional methods do not and outline continued experiments to vet the method.

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.

Identification of the most sensitive parameters in the activated sludge model implemented in BioWin software.

PubMed

Liwarska-Bizukojc, Ewa; Biernacki, Rafal

2010-10-01

In order to simulate biological wastewater treatment processes, data concerning wastewater and sludge composition, process kinetics and stoichiometry are required. Selection of the most sensitive parameters is an important step of model calibration. The aim of this work is to verify the predictability of the activated sludge model, which is implemented in BioWin software, and select its most influential kinetic and stoichiometric parameters with the help of sensitivity analysis approach. Two different measures of sensitivity are applied: the normalised sensitivity coefficient (S(i,j)) and the mean square sensitivity measure (delta(j)(msqr)). It occurs that 17 kinetic and stoichiometric parameters of the BioWin activated sludge (AS) model can be regarded as influential on the basis of S(i,j) calculations. Half of the influential parameters are associated with growth and decay of phosphorus accumulating organisms (PAOs). The identification of the set of the most sensitive parameters should support the users of this model and initiate the elaboration of determination procedures for the parameters, for which it has not been done yet. Copyright 2010 Elsevier Ltd. All rights reserved.

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

Mondy, Lisa Ann; Rao, Rekha Ranjana; Shelden, Bion

We are developing computational models to elucidate the expansion and dynamic filling process of a polyurethane foam, PMDI. The polyurethane of interest is chemically blown, where carbon dioxide is produced via the reaction of water, the blowing agent, and isocyanate. The isocyanate also reacts with polyol in a competing reaction, which produces the polymer. Here we detail the experiments needed to populate a processing model and provide parameters for the model based on these experiments. The model entails solving the conservation equations, including the equations of motion, an energy balance, and two rate equations for the polymerization and foaming reactions,more » following a simplified mathematical formalism that decouples these two reactions. Parameters for the polymerization kinetics model are reported based on infrared spectrophotometry. Parameters describing the gas generating reaction are reported based on measurements of volume, temperature and pressure evolution with time. A foam rheology model is proposed and parameters determined through steady-shear and oscillatory tests. Heat of reaction and heat capacity are determined through differential scanning calorimetry. Thermal conductivity of the foam as a function of density is measured using a transient method based on the theory of the transient plane source technique. Finally, density variations of the resulting solid foam in several simple geometries are directly measured by sectioning and sampling mass, as well as through x-ray computed tomography. These density measurements will be useful for model validation once the complete model is implemented in an engineering code.« less

Analytical and regression models of glass rod drawing process

NASA Astrophysics Data System (ADS)

Alekseeva, L. B.

2018-03-01

The process of drawing glass rods (light guides) is being studied. The parameters of the process affecting the quality of the light guide have been determined. To solve the problem, mathematical models based on general equations of continuum mechanics are used. The conditions for the stable flow of the drawing process have been found, which are determined by the stability of the motion of the glass mass in the formation zone to small uncontrolled perturbations. The sensitivity of the formation zone to perturbations of the drawing speed and viscosity is estimated. Experimental models of the drawing process, based on the regression analysis methods, have been obtained. These models make it possible to customize a specific production process to obtain light guides of the required quality. They allow one to find the optimum combination of process parameters in the chosen area and to determine the required accuracy of maintaining them at a specified level.

Improvement of shallow landslide prediction accuracy using soil parameterisation for a granite area in South Korea

NASA Astrophysics Data System (ADS)

Kim, M. S.; Onda, Y.; Kim, J. K.

2015-01-01

SHALSTAB model applied to shallow landslides induced by rainfall to evaluate soil properties related with the effect of soil depth for a granite area in Jinbu region, Republic of Korea. Soil depth measured by a knocking pole test and two soil parameters from direct shear test (a and b) as well as one soil parameters from a triaxial compression test (c) were collected to determine the input parameters for the model. Experimental soil data were used for the first simulation (Case I) and, soil data represented the effect of measured soil depth and average soil depth from soil data of Case I were used in the second (Case II) and third simulations (Case III), respectively. All simulations were analysed using receiver operating characteristic (ROC) analysis to determine the accuracy of prediction. ROC analysis results for first simulation showed the low ROC values under 0.75 may be due to the internal friction angle and particularly the cohesion value. Soil parameters calculated from a stochastic hydro-geomorphological model were applied to the SHALSTAB model. The accuracy of Case II and Case III using ROC analysis showed higher accuracy values rather than first simulation. Our results clearly demonstrate that the accuracy of shallow landslide prediction can be improved when soil parameters represented the effect of soil thickness.

Trans-dimensional inversion of microtremor array dispersion data with hierarchical autoregressive error models

NASA Astrophysics Data System (ADS)

Dettmer, Jan; Molnar, Sheri; Steininger, Gavin; Dosso, Stan E.; Cassidy, John F.

2012-02-01

This paper applies a general trans-dimensional Bayesian inference methodology and hierarchical autoregressive data-error models to the inversion of microtremor array dispersion data for shear wave velocity (vs) structure. This approach accounts for the limited knowledge of the optimal earth model parametrization (e.g. the number of layers in the vs profile) and of the data-error statistics in the resulting vs parameter uncertainty estimates. The assumed earth model parametrization influences estimates of parameter values and uncertainties due to different parametrizations leading to different ranges of data predictions. The support of the data for a particular model is often non-unique and several parametrizations may be supported. A trans-dimensional formulation accounts for this non-uniqueness by including a model-indexing parameter as an unknown so that groups of models (identified by the indexing parameter) are considered in the results. The earth model is parametrized in terms of a partition model with interfaces given over a depth-range of interest. In this work, the number of interfaces (layers) in the partition model represents the trans-dimensional model indexing. In addition, serial data-error correlations are addressed by augmenting the geophysical forward model with a hierarchical autoregressive error model that can account for a wide range of error processes with a small number of parameters. Hence, the limited knowledge about the true statistical distribution of data errors is also accounted for in the earth model parameter estimates, resulting in more realistic uncertainties and parameter values. Hierarchical autoregressive error models do not rely on point estimates of the model vector to estimate data-error statistics, and have no requirement for computing the inverse or determinant of a data-error covariance matrix. This approach is particularly useful for trans-dimensional inverse problems, as point estimates may not be representative of the state space that spans multiple subspaces of different dimensionalities. The order of the autoregressive process required to fit the data is determined here by posterior residual-sample examination and statistical tests. Inference for earth model parameters is carried out on the trans-dimensional posterior probability distribution by considering ensembles of parameter vectors. In particular, vs uncertainty estimates are obtained by marginalizing the trans-dimensional posterior distribution in terms of vs-profile marginal distributions. The methodology is applied to microtremor array dispersion data collected at two sites with significantly different geology in British Columbia, Canada. At both sites, results show excellent agreement with estimates from invasive measurements.

Use of Bayesian Inference in Crystallographic Structure Refinement via Full Diffraction Profile Analysis

PubMed Central

Fancher, Chris M.; Han, Zhen; Levin, Igor; Page, Katharine; Reich, Brian J.; Smith, Ralph C.; Wilson, Alyson G.; Jones, Jacob L.

2016-01-01

A Bayesian inference method for refining crystallographic structures is presented. The distribution of model parameters is stochastically sampled using Markov chain Monte Carlo. Posterior probability distributions are constructed for all model parameters to properly quantify uncertainty by appropriately modeling the heteroskedasticity and correlation of the error structure. The proposed method is demonstrated by analyzing a National Institute of Standards and Technology silicon standard reference material. The results obtained by Bayesian inference are compared with those determined by Rietveld refinement. Posterior probability distributions of model parameters provide both estimates and uncertainties. The new method better estimates the true uncertainties in the model as compared to the Rietveld method. PMID:27550221

Static shape control for flexible structures

NASA Technical Reports Server (NTRS)

Rodriguez, G.; Scheid, R. E., Jr.

1986-01-01

An integrated methodology is described for defining static shape control laws for large flexible structures. The techniques include modeling, identifying and estimating the control laws of distributed systems characterized in terms of infinite dimensional state and parameter spaces. The models are expressed as interconnected elliptic partial differential equations governing a range of static loads, with the capability of analyzing electromagnetic fields around antenna systems. A second-order analysis is carried out for statistical errors, and model parameters are determined by maximizing an appropriate defined likelihood functional which adjusts the model to observational data. The parameter estimates are derived from the conditional mean of the observational data, resulting in a least squares superposition of shape functions obtained from the structural model.

Estimation of the sea surface's two-scale backscatter parameters

NASA Technical Reports Server (NTRS)

Wentz, F. J.

1978-01-01

The relationship between the sea-surface normalized radar cross section and the friction velocity vector is determined using a parametric two-scale scattering model. The model parameters are found from a nonlinear maximum likelihood estimation. The estimation is based on aircraft scatterometer measurements and the sea-surface anemometer measurements collected during the JONSWAP '75 experiment. The estimates of the ten model parameters converge to realistic values that are in good agreement with the available oceanographic data. The rms discrepancy between the model and the cross section measurements is 0.7 db, which is the rms sum of a 0.3 db average measurement error and a 0.6 db modeling error.

KIDS Nuclear Energy Density Functional: 1st Application in Nuclei

NASA Astrophysics Data System (ADS)

Gil, Hana; Papakonstantinou, Panagiota; Hyun, Chang Ho; Oh, Yongseok

We apply the KIDS (Korea: IBS-Daegu-Sungkyunkwan) nuclear energy density functional model, which is based on the Fermi momentum expansion, to the study of properties of lj-closed nuclei. The parameters of the model are determined by the nuclear properties at the saturation density and theoretical calculations on pure neutron matter. For applying the model to the study of nuclei, we rely on the Skyrme force model, where the Skyrme force parameters are determined through the KIDS energy density functional. Solving Hartree-Fock equations, we obtain the energies per particle and charge radii of closed magic nuclei, namely, 16O, 28O, 40Ca, 48Ca, 60Ca, 90Zr, 132Sn, and 208Pb. The results are compared with the observed data and further improvement of the model is shortly mentioned.

Assessing the applicability of WRF optimal parameters under the different precipitation simulations in the Greater Beijing Area

NASA Astrophysics Data System (ADS)

Di, Zhenhua; Duan, Qingyun; Wang, Chen; Ye, Aizhong; Miao, Chiyuan; Gong, Wei

2018-03-01

Forecasting skills of the complex weather and climate models have been improved by tuning the sensitive parameters that exert the greatest impact on simulated results based on more effective optimization methods. However, whether the optimal parameter values are still work when the model simulation conditions vary, which is a scientific problem deserving of study. In this study, a highly-effective optimization method, adaptive surrogate model-based optimization (ASMO), was firstly used to tune nine sensitive parameters from four physical parameterization schemes of the Weather Research and Forecasting (WRF) model to obtain better summer precipitation forecasting over the Greater Beijing Area in China. Then, to assess the applicability of the optimal parameter values, simulation results from the WRF model with default and optimal parameter values were compared across precipitation events, boundary conditions, spatial scales, and physical processes in the Greater Beijing Area. The summer precipitation events from 6 years were used to calibrate and evaluate the optimal parameter values of WRF model. Three boundary data and two spatial resolutions were adopted to evaluate the superiority of the calibrated optimal parameters to default parameters under the WRF simulations with different boundary conditions and spatial resolutions, respectively. Physical interpretations of the optimal parameters indicating how to improve precipitation simulation results were also examined. All the results showed that the optimal parameters obtained by ASMO are superior to the default parameters for WRF simulations for predicting summer precipitation in the Greater Beijing Area because the optimal parameters are not constrained by specific precipitation events, boundary conditions, and spatial resolutions. The optimal values of the nine parameters were determined from 127 parameter samples using the ASMO method, which showed that the ASMO method is very highly-efficient for optimizing WRF model parameters.

Automatic Parameterization Strategy for Cardiac Electrophysiology Simulations

PubMed Central

Costa, Caroline Mendonca; Hoetzl, Elena; Rocha, Bernardo Martins; Prassl, Anton J; Plank, Gernot

2014-01-01

Driven by recent advances in medical imaging, image segmentation and numerical techniques, computer models of ventricular electrophysiology account for increasingly finer levels of anatomical and biophysical detail. However, considering the large number of model parameters involved parameterization poses a major challenge. A minimum requirement in combined experimental and modeling studies is to achieve good agreement in activation and repolarization sequences between model and experiment or patient data. In this study, we propose basic techniques which aid in determining bidomain parameters to match activation sequences. An iterative parameterization algorithm is implemented which determines appropriate bulk conductivities which yield prescribed velocities. In addition, a method is proposed for splitting the computed bulk conductivities into individual bidomain conductivities by prescribing anisotropy ratios. PMID:24729986

Optimization of hybrid laser - TIG welding of 316LN steel using response surface methodology (RSM)

NASA Astrophysics Data System (ADS)

Ragavendran, M.; Chandrasekhar, N.; Ravikumar, R.; Saxena, Rajesh; Vasudevan, M.; Bhaduri, A. K.

2017-07-01

In the present study, the hybrid laser - TIG welding parameters for welding of 316LN austenitic stainless steel have been investigated by combining a pulsed laser beam with a TIG welding heat source at the weld pool. Laser power, pulse frequency, pulse duration, TIG current were presumed as the welding process parameters whereas weld bead width, weld cross-sectional area and depth of penetration (DOP) were considered as the process responses. Central composite design was used to complete the design matrix and welding experiments were conducted based on the design matrix. Weld bead measurements were then carried out to generate the dataset. Multiple regression models correlating the process parameters with the responses have been developed. The accuracy of the models were found to be good. Then, the desirability approach optimization technique was employed for determining the optimum process parameters to obtain the desired weld bead profile. Validation experiments were then carried out from the determined optimum process parameters. There was good agreement between the predicted and measured values.

Use of a reactive gas transport model to determine rates of hydrocarbon biodegradation in unsaturated porous media

USGS Publications Warehouse

Baehr, Arthur L.; Baker, Ronald J.

1995-01-01

A mathematical model is presented that simulates the transport and reaction of any number of gaseous phase constituents (e.g. CO2, O2, N2, and hydrocarbons) in unsaturated porous media. The model was developed as part of a method to determine rates of hydrocarbon biodegradation associated with natural cleansing at petroleum product spill sites. The one-dimensional model can be applied to analyze data from column experiments or from field sites where gas transport in the unsaturated zone is approximately vertical. A coupled, non-Fickian constitutive relation between fluxes and concentration gradients, together with the capability of incorporating heterogeneity with respect to model parameters, results in model applicability over a wide range of experimental and field conditions. When applied in a calibration mode, the model allows for the determination of constituent production/consumption rates as a function of the spatial coordinate. Alternatively, the model can be applied in a predictive mode to obtain the distribution of constituent concentrations and fluxes on the basis of assumed values of model parameters and a biodegradation hypothesis. Data requirements for the model are illustrated by analyzing data from a column experiment designed to determine the aerobic degradation rate of toluene in sediments collected from a gasoline spill site in Galloway Township, New Jersey.

Reactive flow model development for PBXW-126 using modern nonlinear optimization methods

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

Murphy, M.J.; Simpson, R.L.; Urtiew, P.A.

1995-08-01

The initiation and detonation behavior of PBXW-126 has been characterized and is described. PBXW-126 is a composite explosive consisting of approximately equal amounts of RDX, AP, AL, and NTO with a polyurethane binder. The three term ignition and growth of reaction model parameters (ignition + two growth terms) have been found using nonlinear optimization methods to determine the {open_quotes}best{close_quotes} set of model parameters. The ignition term treats the initiation of up to 0.5% of the RDX The first growth term in the model treats the RDX growth of reaction up to 20% reacted. The second growth term treats the subsequentmore » growth of reaction of the remaining AP/AL/NTO. The unreacted equation of state (EOS) was determined from the wave profiles of embedded gauge tests while the JWL product EOS was determined from cylinder expansion test results. The nonlinear optimization code, NLQPEB/GLO, was used to determine the {open_quotes}best{close_quotes} set of coefficients for the three term Lee-Tarver ignition and growth of reaction model.« less

Global Sensitivity of Simulated Water Balance Indicators Under Future Climate Change in the Colorado Basin

NASA Astrophysics Data System (ADS)

Bennett, Katrina E.; Urrego Blanco, Jorge R.; Jonko, Alexandra; Bohn, Theodore J.; Atchley, Adam L.; Urban, Nathan M.; Middleton, Richard S.

2018-01-01

The Colorado River Basin is a fundamentally important river for society, ecology, and energy in the United States. Streamflow estimates are often provided using modeling tools which rely on uncertain parameters; sensitivity analysis can help determine which parameters impact model results. Despite the fact that simulated flows respond to changing climate and vegetation in the basin, parameter sensitivity of the simulations under climate change has rarely been considered. In this study, we conduct a global sensitivity analysis to relate changes in runoff, evapotranspiration, snow water equivalent, and soil moisture to model parameters in the Variable Infiltration Capacity (VIC) hydrologic model. We combine global sensitivity analysis with a space-filling Latin Hypercube Sampling of the model parameter space and statistical emulation of the VIC model to examine sensitivities to uncertainties in 46 model parameters following a variance-based approach. We find that snow-dominated regions are much more sensitive to uncertainties in VIC parameters. Although baseflow and runoff changes respond to parameters used in previous sensitivity studies, we discover new key parameter sensitivities. For instance, changes in runoff and evapotranspiration are sensitive to albedo, while changes in snow water equivalent are sensitive to canopy fraction and Leaf Area Index (LAI) in the VIC model. It is critical for improved modeling to narrow uncertainty in these parameters through improved observations and field studies. This is important because LAI and albedo are anticipated to change under future climate and narrowing uncertainty is paramount to advance our application of models such as VIC for water resource management.

From global to local: exploring the relationship between parameters and behaviors in models of electrical excitability.

PubMed

Fletcher, Patrick; Bertram, Richard; Tabak, Joel

2016-06-01

Models of electrical activity in excitable cells involve nonlinear interactions between many ionic currents. Changing parameters in these models can produce a variety of activity patterns with sometimes unexpected effects. Further more, introducing new currents will have different effects depending on the initial parameter set. In this study we combined global sampling of parameter space and local analysis of representative parameter sets in a pituitary cell model to understand the effects of adding K (+) conductances, which mediate some effects of hormone action on these cells. Global sampling ensured that the effects of introducing K (+) conductances were captured across a wide variety of contexts of model parameters. For each type of K (+) conductance we determined the types of behavioral transition that it evoked. Some transitions were counterintuitive, and may have been missed without the use of global sampling. In general, the wide range of transitions that occurred when the same current was applied to the model cell at different locations in parameter space highlight the challenge of making accurate model predictions in light of cell-to-cell heterogeneity. Finally, we used bifurcation analysis and fast/slow analysis to investigate why specific transitions occur in representative individual models. This approach relies on the use of a graphics processing unit (GPU) to quickly map parameter space to model behavior and identify parameter sets for further analysis. Acceleration with modern low-cost GPUs is particularly well suited to exploring the moderate-sized (5-20) parameter spaces of excitable cell and signaling models.

Control of DNA-Functionalized Nanoparticle Assembly

NASA Astrophysics Data System (ADS)

Olvera de La Cruz, Monica

Directed crystallization of a large variety of nanoparticles, including proteins, via DNA hybridization kinetics has led to unique materials with a broad range of crystal symmetries. The nanoparticles are functionalized with DNA chains that link neighboring functionalized units. The shape of the nanoparticle, the DNA length, the sequence of the hybridizing DNA linker and the grafting density determine the crystal symmetries and lattice spacing. By carefully selecting these parameters one can, in principle, achieve all the symmetries found for both atomic and colloidal crystals of asymmetric shapes as well as new symmetries, and drive transitions between them. A scale-accurate coarse-grained model with explicit DNA chains provides the design parameters, including degree of hybridization, to achieve specific crystal structures. The model also provides surface energy values to determine the shape of defect-free single crystals with macroscopic anisotropic properties, as well as the parameters to develop colloidal models that reproduce both the shape of single crystals and their growth kinetics.

Empirical flow parameters : a tool for hydraulic model validity

USGS Publications Warehouse

Asquith, William H.; Burley, Thomas E.; Cleveland, Theodore G.

2013-01-01

The objectives of this project were (1) To determine and present from existing data in Texas, relations between observed stream flow, topographic slope, mean section velocity, and other hydraulic factors, to produce charts such as Figure 1 and to produce empirical distributions of the various flow parameters to provide a methodology to "check if model results are way off!"; (2) To produce a statistical regional tool to estimate mean velocity or other selected parameters for storm flows or other conditional discharges at ungauged locations (most bridge crossings) in Texas to provide a secondary way to compare such values to a conventional hydraulic modeling approach. (3.) To present ancillary values such as Froude number, stream power, Rosgen channel classification, sinuosity, and other selected characteristics (readily determinable from existing data) to provide additional information to engineers concerned with the hydraulic-soil-foundation component of transportation infrastructure.

Determination of adsorption parameters in numerical simulation for polymer flooding

NASA Astrophysics Data System (ADS)

Bao, Pengyu; Li, Aifen; Luo, Shuai; Dang, Xu

2018-02-01

A study on the determination of adsorption parameters for polymer flooding simulation was carried out. The study mainly includes polymer static adsorption and dynamic adsorption. The law of adsorption amount changing with polymer concentration and core permeability was presented, and the one-dimensional numerical model of CMG was established under the support of a large number of experimental data. The adsorption laws of adsorption experiments were applied to the one-dimensional numerical model to compare the influence of two adsorption laws on the historical matching results. The results show that the static adsorption and dynamic adsorption abide by different rules, and differ greatly in adsorption. If the static adsorption results were directly applied to the numerical model, the difficulty of the historical matching will increase. Therefore, dynamic adsorption tests in the porous medium are necessary before the process of parameter adjustment in order to achieve the ideal history matching result.

Photospheres of hot stars. IV - Spectral type O4

NASA Technical Reports Server (NTRS)

Bohannan, Bruce; Abbott, David C.; Voels, Stephen A.; Hummer, David G.

1990-01-01

The basic stellar parameters of a supergiant (Zeta Pup) and two main-sequence stars, 9 Sgr and HD 46223, at spectral class O4 are determined using line profile analysis. The stellar parameters are determined by comparing high signal-to-noise hydrogen and helium line profiles with those from stellar atmosphere models which include the effect of radiation scattered back onto the photosphere from an overlying stellar wind, an effect referred to as wind blanketing. At spectral class O4, the inclusion of wind-blanketing in the model atmosphere reduces the effective temperature by an average of 10 percent. This shift in effective temperature is also reflected by shifts in several other stellar parameters relative to previous O4 spectral-type calibrations. It is also shown through the analysis of the two O4 V stars that scatter in spectral type calibrations is introduced by assuming that the observed line profile reflects the photospheric stellar parameters.

Measurement of angular parameters from the decay $$\\mathrm{B}^0 \\to \\mathrm{K}^{*0} \\mu^+ \\mu^-$$ in proton-proton collisions at $$\\sqrt{s} = $$ 8 TeV

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

Sirunyan, Albert M; et al.

Angular distributions of the decaymore » $$\\mathrm{B}^0 \\to \\mathrm{K}^{*0} \\mu^ +\\mu^-$$ are studied using a sample of proton-proton collisions at $$\\sqrt{s} = $$ 8 TeV collected with the CMS detector at the LHC, corresponding to an integrated luminosity of 20.5 fb$$^{-1}$$. An angular analysis is performed to determine the $$P_1$$ and $$P_5'$$ parameters, where the $$P_5'$$ parameter is of particular interest because of recent measurements that indicate a potential discrepancy with the standard model predictions. Based on a sample of 1397 signal events, the $$P_1$$ and $$P_5'$$ parameters are determined as a function of the dimuon invariant mass squared. The measurements are in agreement with predictions based on the standard model.« less

An Open-Source Auto-Calibration Routine Supporting the Stormwater Management Model

NASA Astrophysics Data System (ADS)

Tiernan, E. D.; Hodges, B. R.

2017-12-01

The stormwater management model (SWMM) is a clustered model that relies on subcatchment-averaged parameter assignments to correctly capture catchment stormwater runoff behavior. Model calibration is considered a critical step for SWMM performance, an arduous task that most stormwater management designers undertake manually. This research presents an open-source, automated calibration routine that increases the efficiency and accuracy of the model calibration process. The routine makes use of a preliminary sensitivity analysis to reduce the dimensions of the parameter space, at which point a multi-objective function, genetic algorithm (modified Non-dominated Sorting Genetic Algorithm II) determines the Pareto front for the objective functions within the parameter space. The solutions on this Pareto front represent the optimized parameter value sets for the catchment behavior that could not have been reasonably obtained through manual calibration.

Feasibility of employing model-based optimization of pulse amplitude and electrode distance for effective tumor electropermeabilization.

PubMed

Sel, Davorka; Lebar, Alenka Macek; Miklavcic, Damijan

2007-05-01

In electrochemotherapy (ECT) electropermeabilization, parameters (pulse amplitude, electrode setup) need to be customized in order to expose the whole tumor to electric field intensities above permeabilizing threshold to achieve effective ECT. In this paper, we present a model-based optimization approach toward determination of optimal electropermeabilization parameters for effective ECT. The optimization is carried out by minimizing the difference between the permeabilization threshold and electric field intensities computed by finite element model in selected points of tumor. We examined the feasibility of model-based optimization of electropermeabilization parameters on a model geometry generated from computer tomography images, representing brain tissue with tumor. Continuous parameter subject to optimization was pulse amplitude. The distance between electrode pairs was optimized as a discrete parameter. Optimization also considered the pulse generator constraints on voltage and current. During optimization the two constraints were reached preventing the exposure of the entire volume of the tumor to electric field intensities above permeabilizing threshold. However, despite the fact that with the particular needle array holder and pulse generator the entire volume of the tumor was not permeabilized, the maximal extent of permeabilization for the particular case (electrodes, tissue) was determined with the proposed approach. Model-based optimization approach could also be used for electro-gene transfer, where electric field intensities should be distributed between permeabilizing threshold and irreversible threshold-the latter causing tissue necrosis. This can be obtained by adding constraints on maximum electric field intensity in optimization procedure.

Optimization of multi-environment trials for genomic selection based on crop models.

PubMed

Rincent, R; Kuhn, E; Monod, H; Oury, F-X; Rousset, M; Allard, V; Le Gouis, J

2017-08-01

We propose a statistical criterion to optimize multi-environment trials to predict genotype × environment interactions more efficiently, by combining crop growth models and genomic selection models. Genotype × environment interactions (GEI) are common in plant multi-environment trials (METs). In this context, models developed for genomic selection (GS) that refers to the use of genome-wide information for predicting breeding values of selection candidates need to be adapted. One promising way to increase prediction accuracy in various environments is to combine ecophysiological and genetic modelling thanks to crop growth models (CGM) incorporating genetic parameters. The efficiency of this approach relies on the quality of the parameter estimates, which depends on the environments composing this MET used for calibration. The objective of this study was to determine a method to optimize the set of environments composing the MET for estimating genetic parameters in this context. A criterion called OptiMET was defined to this aim, and was evaluated on simulated and real data, with the example of wheat phenology. The MET defined with OptiMET allowed estimating the genetic parameters with lower error, leading to higher QTL detection power and higher prediction accuracies. MET defined with OptiMET was on average more efficient than random MET composed of twice as many environments, in terms of quality of the parameter estimates. OptiMET is thus a valuable tool to determine optimal experimental conditions to best exploit MET and the phenotyping tools that are currently developed.

Extended Kalman Filter for Estimation of Parameters in Nonlinear State-Space Models of Biochemical Networks

PubMed Central

Sun, Xiaodian; Jin, Li; Xiong, Momiao

2008-01-01

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks. PMID:19018286

A two-fluid approximation for calculating the cosmic microwave background anisotropies

NASA Technical Reports Server (NTRS)

Seljak, Uros

1994-01-01

We present a simplified treatment for calculating the cosmic microwave background anisotropy power spectrum in adiabatic models. It consists of solving for the evolution of a two-fluid model until the epoch of recombination and then integrating over the sources to obtain the cosmic microwave background (CMB) anisotropy power spectrum. The approximation is useful both for a physical understanding of CMB anisotropies as well as for a quantitative analysis of cosmological models. Comparison with exact calculations shows that the accuracy is typically 10%-20% over a large range of angles and cosmological models, including those with curvature and cosmological constant. Using this approximation we investigate the dependence of the CMB anisotropy on the cosmological parameters. We identify six dimensionless parameters that uniquely determine the anisotropy power spectrum within our approximation. CMB experiments on different angular scales could in principle provide information on all these parameters. In particular, mapping of the Doppler peaks would allow an independent determination of baryon mass density, matter mass density, and the Hubble constant.

A square-force cohesion model and its extraction from bulk measurements

NASA Astrophysics Data System (ADS)

Liu, Peiyuan; Lamarche, Casey; Kellogg, Kevin; Hrenya, Christine

2017-11-01

Cohesive particles remain poorly understood, with order of magnitude differences exhibited for prior, physical predictions of agglomerate size. A major obstacle lies in the absence of robust models of particle-particle cohesion, thereby precluding accurate prediction of the behavior of cohesive particles. Rigorous cohesion models commonly contain parameters related to surface roughness, to which cohesion shows extreme sensitivity. However, both roughness measurement and its distillation into these model parameters are challenging. Accordingly, we propose a ``square-force'' model, where cohesive force remains constant until a cut-off separation. Via DEM simulations, we demonstrate validity of the square-force model as surrogate of more rigorous models, when its two parameters are selected to match the two key quantities governing dense and dilute granular flows, namely maximum cohesive force and critical cohesive energy, respectively. Perhaps more importantly, we establish a method to extract the parameters in the square-force model via defluidization, due to its ability to isolate the effects of the two parameters. Thus, instead of relying on complicated scans of individual grains, determination of particle-particle cohesion from simple bulk measurements becomes feasible. Dow Corning Corporation.

Prediction of solubility parameters and miscibility of pharmaceutical compounds by molecular dynamics simulations.

PubMed

Gupta, Jasmine; Nunes, Cletus; Vyas, Shyam; Jonnalagadda, Sriramakamal

2011-03-10

The objectives of this study were (i) to develop a computational model based on molecular dynamics technique to predict the miscibility of indomethacin in carriers (polyethylene oxide, glucose, and sucrose) and (ii) to experimentally verify the in silico predictions by characterizing the drug-carrier mixtures using thermoanalytical techniques. Molecular dynamics (MD) simulations were performed using the COMPASS force field, and the cohesive energy density and the solubility parameters were determined for the model compounds. The magnitude of difference in the solubility parameters of drug and carrier is indicative of their miscibility. The MD simulations predicted indomethacin to be miscible with polyethylene oxide and to be borderline miscible with sucrose and immiscible with glucose. The solubility parameter values obtained using the MD simulations values were in reasonable agreement with those calculated using group contribution methods. Differential scanning calorimetry showed melting point depression of polyethylene oxide with increasing levels of indomethacin accompanied by peak broadening, confirming miscibility. In contrast, thermal analysis of blends of indomethacin with sucrose and glucose verified general immiscibility. The findings demonstrate that molecular modeling is a powerful technique for determining the solubility parameters and predicting miscibility of pharmaceutical compounds. © 2011 American Chemical Society

In situ determination of the static inductance and resistance of a plasma focus capacitor bank.

PubMed

Saw, S H; Lee, S; Roy, F; Chong, P L; Vengadeswaran, V; Sidik, A S M; Leong, Y W; Singh, A

2010-05-01

The static (unloaded) electrical parameters of a capacitor bank are of utmost importance for the purpose of modeling the system as a whole when the capacitor bank is discharged into its dynamic electromagnetic load. Using a physical short circuit across the electromagnetic load is usually technically difficult and is unnecessary. The discharge can be operated at the highest pressure permissible in order to minimize current sheet motion, thus simulating zero dynamic load, to enable bank parameters, static inductance L(0), and resistance r(0) to be obtained using lightly damped sinusoid equations given the bank capacitance C(0). However, for a plasma focus, even at the highest permissible pressure it is found that there is significant residual motion, so that the assumption of a zero dynamic load introduces unacceptable errors into the determination of the circuit parameters. To overcome this problem, the Lee model code is used to fit the computed current trace to the measured current waveform. Hence the dynamics is incorporated into the solution and the capacitor bank parameters are computed using the Lee model code, and more accurate static bank parameters are obtained.

In situ determination of the static inductance and resistance of a plasma focus capacitor bank

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

Saw, S. H.; Institute for Plasma Focus Studies, 32 Oakpark Drive, Chadstone, Victoria 3148; Lee, S.

2010-05-15

The static (unloaded) electrical parameters of a capacitor bank are of utmost importance for the purpose of modeling the system as a whole when the capacitor bank is discharged into its dynamic electromagnetic load. Using a physical short circuit across the electromagnetic load is usually technically difficult and is unnecessary. The discharge can be operated at the highest pressure permissible in order to minimize current sheet motion, thus simulating zero dynamic load, to enable bank parameters, static inductance L{sub 0}, and resistance r{sub 0} to be obtained using lightly damped sinusoid equations given the bank capacitance C{sub 0}. However, formore » a plasma focus, even at the highest permissible pressure it is found that there is significant residual motion, so that the assumption of a zero dynamic load introduces unacceptable errors into the determination of the circuit parameters. To overcome this problem, the Lee model code is used to fit the computed current trace to the measured current waveform. Hence the dynamics is incorporated into the solution and the capacitor bank parameters are computed using the Lee model code, and more accurate static bank parameters are obtained.« less

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

PubMed

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

2017-03-03

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

The Effect of Roughness Model on Scattering Properties of Ice Crystals.

NASA Technical Reports Server (NTRS)

Geogdzhayev, Igor V.; Van Diedenhoven, Bastiaan

2016-01-01

We compare stochastic models of microscale surface roughness assuming uniform and Weibull distributions of crystal facet tilt angles to calculate scattering by roughened hexagonal ice crystals using the geometric optics (GO) approximation. Both distributions are determined by similar roughness parameters, while the Weibull model depends on the additional shape parameter. Calculations were performed for two visible wavelengths (864 nm and 410 nm) for roughness values between 0.2 and 0.7 and Weibull shape parameters between 0 and 1.0 for crystals with aspect ratios of 0.21, 1 and 4.8. For this range of parameters we find that, for a given roughness level, varying the Weibull shape parameter can change the asymmetry parameter by up to about 0.05. The largest effect of the shape parameter variation on the phase function is found in the backscattering region, while the degree of linear polarization is most affected at the side-scattering angles. For high roughness, scattering properties calculated using the uniform and Weibull models are in relatively close agreement for a given roughness parameter, especially when a Weibull shape parameter of 0.75 is used. For smaller roughness values, a shape parameter close to unity provides a better agreement. Notable differences are observed in the phase function over the scattering angle range from 5deg to 20deg, where the uniform roughness model produces a plateau while the Weibull model does not.

Effect of Fault Parameter Uncertainties on PSHA explored by Monte Carlo Simulations: A case study for southern Apennines, Italy

NASA Astrophysics Data System (ADS)

Akinci, A.; Pace, B.

2017-12-01

In this study, we discuss the seismic hazard variability of peak ground acceleration (PGA) at 475 years return period in the Southern Apennines of Italy. The uncertainty and parametric sensitivity are presented to quantify the impact of the several fault parameters on ground motion predictions for 10% exceedance in 50-year hazard. A time-independent PSHA model is constructed based on the long-term recurrence behavior of seismogenic faults adopting the characteristic earthquake model for those sources capable of rupturing the entire fault segment with a single maximum magnitude. The fault-based source model uses the dimensions and slip rates of mapped fault to develop magnitude-frequency estimates for characteristic earthquakes. Variability of the selected fault parameter is given with a truncated normal random variable distribution presented by standard deviation about a mean value. A Monte Carlo approach, based on the random balanced sampling by logic tree, is used in order to capture the uncertainty in seismic hazard calculations. For generating both uncertainty and sensitivity maps, we perform 200 simulations for each of the fault parameters. The results are synthesized both in frequency-magnitude distribution of modeled faults as well as the different maps: the overall uncertainty maps provide a confidence interval for the PGA values and the parameter uncertainty maps determine the sensitivity of hazard assessment to variability of every logic tree branch. These branches of logic tree, analyzed through the Monte Carlo approach, are maximum magnitudes, fault length, fault width, fault dip and slip rates. The overall variability of these parameters is determined by varying them simultaneously in the hazard calculations while the sensitivity of each parameter to overall variability is determined varying each of the fault parameters while fixing others. However, in this study we do not investigate the sensitivity of mean hazard results to the consideration of different GMPEs. Distribution of possible seismic hazard results is illustrated by 95% confidence factor map, which indicates the dispersion about mean value, and coefficient of variation map, which shows percent variability. The results of our study clearly illustrate the influence of active fault parameters to probabilistic seismic hazard maps.

Performance mapping of a 30 cm engineering model thruster

NASA Technical Reports Server (NTRS)

Poeschel, R. L.; Vahrenkamp, R. P.

1975-01-01

A 30 cm thruster representative of the engineering model design has been tested over a wide range of operating parameters to document performance characteristics such as electrical and propellant efficiencies, double ion and beam divergence thrust loss, component equilibrium temperatures, operational stability, etc. Data obtained show that optimum power throttling, in terms of maximum thruster efficiency, is not highly sensitive to parameter selection. Consequently, considerations of stability, discharge chamber erosion, thrust losses, etc. can be made the determining factors for parameter selection in power throttling operations. Options in parameter selection based on these considerations are discussed.

Ionospheric absorption, typical ionization, conductivity, and possible synoptic heating parameters in the upper atmosphere

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

Walker, J.K.; Bhatnagar, V.P.

1989-04-01

Relations for the average energetic particle heating and the typical Hall and Pedersen conductances, as functions of the ground-based Hf radio absorption, are determined. Collis and coworkers used the geosynchronous GEOS 2 particle data to relate or ''calibrate'' the auroral absorption on the same magnetic field lines with five levels of D region ionization. These ionospheric models are related to a Chapman layer that extends these models into the E region. The average energetic particle heating is calculated for each of these models using recent expressions for the effective recombination coefficient. The corresponding height-integrated heating rates are determined and relatedmore » to the absorption with a quadratic expression. The average Hall and Pedersen conductivities are calculated for each of the nominal absorption ionospheric models. The corresponding height-integrated conductances for nighttime conditions are determined and related to the absorption. Expressions for these conductances during disturbed sunlit conditions are also determined. These relations can be used in conjunction with simultaneous ground-based riometric and magnetic observations to determines the average Hall and Pedersen currents and the Joule heating. The typical daily rate of temperature increase in the mesosphere for storm conditions is several 10 K for both the energetic particle and the Joule heating. The increasing importance of these parameters of the upper and middle atmospheres is discussed. It is proposed that northern hemisphere ionospheric, current, and heating synoptic models and parameters be investigated for use on a regular basis. copyright American Geophysical Union 1989« less

Modeling seasonal measles transmission in China

NASA Astrophysics Data System (ADS)

Bai, Zhenguo; Liu, Dan

2015-08-01

A discrete-time deterministic measles model with periodic transmission rate is formulated and studied. The basic reproduction number R0 is defined and used as the threshold parameter in determining the dynamics of the model. It is shown that the disease will die out if R0 < 1 , and the disease will persist in the population if R0 > 1 . Parameters in the model are estimated on the basis of demographic and epidemiological data. Numerical simulations are presented to describe the seasonal fluctuation of measles infection in China.

An analysis of the least-squares problem for the DSN systematic pointing error model

NASA Technical Reports Server (NTRS)

Alvarez, L. S.

1991-01-01

A systematic pointing error model is used to calibrate antennas in the Deep Space Network. The least squares problem is described and analyzed along with the solution methods used to determine the model's parameters. Specifically studied are the rank degeneracy problems resulting from beam pointing error measurement sets that incorporate inadequate sky coverage. A least squares parameter subset selection method is described and its applicability to the systematic error modeling process is demonstrated on Voyager 2 measurement distribution.

Application of Statistically Derived CPAS Parachute Parameters

NASA Technical Reports Server (NTRS)

Romero, Leah M.; Ray, Eric S.

2013-01-01

The Capsule Parachute Assembly System (CPAS) Analysis Team is responsible for determining parachute inflation parameters and dispersions that are ultimately used in verifying system requirements. A model memo is internally released semi-annually documenting parachute inflation and other key parameters reconstructed from flight test data. Dispersion probability distributions published in previous versions of the model memo were uniform because insufficient data were available for determination of statistical based distributions. Uniform distributions do not accurately represent the expected distributions since extreme parameter values are just as likely to occur as the nominal value. CPAS has taken incremental steps to move away from uniform distributions. Model Memo version 9 (MMv9) made the first use of non-uniform dispersions, but only for the reefing cutter timing, for which a large number of sample was available. In order to maximize the utility of the available flight test data, clusters of parachutes were reconstructed individually starting with Model Memo version 10. This allowed for statistical assessment for steady-state drag area (CDS) and parachute inflation parameters such as the canopy fill distance (n), profile shape exponent (expopen), over-inflation factor (C(sub k)), and ramp-down time (t(sub k)) distributions. Built-in MATLAB distributions were applied to the histograms, and parameters such as scale (sigma) and location (mu) were output. Engineering judgment was used to determine the "best fit" distribution based on the test data. Results include normal, log normal, and uniform (where available data remains insufficient) fits of nominal and failure (loss of parachute and skipped stage) cases for all CPAS parachutes. This paper discusses the uniform methodology that was previously used, the process and result of the statistical assessment, how the dispersions were incorporated into Monte Carlo analyses, and the application of the distributions in trajectory benchmark testing assessments with parachute inflation parameters, drag area, and reefing cutter timing used by CPAS.

Equation-free analysis of agent-based models and systematic parameter determination

NASA Astrophysics Data System (ADS)

Thomas, Spencer A.; Lloyd, David J. B.; Skeldon, Anne C.

2016-12-01

Agent based models (ABM)s are increasingly used in social science, economics, mathematics, biology and computer science to describe time dependent systems in circumstances where a description in terms of equations is difficult. Yet few tools are currently available for the systematic analysis of ABM behaviour. Numerical continuation and bifurcation analysis is a well-established tool for the study of deterministic systems. Recently, equation-free (EF) methods have been developed to extend numerical continuation techniques to systems where the dynamics are described at a microscopic scale and continuation of a macroscopic property of the system is considered. To date, the practical use of EF methods has been limited by; (1) the over-head of application-specific implementation; (2) the laborious configuration of problem-specific parameters; and (3) large ensemble sizes (potentially) leading to computationally restrictive run-times. In this paper we address these issues with our tool for the EF continuation of stochastic systems, which includes algorithms to systematically configuration problem specific parameters and enhance robustness to noise. Our tool is generic and can be applied to any 'black-box' simulator and determines the essential EF parameters prior to EF analysis. Robustness is significantly improved using our convergence-constraint with a corrector-repeat (C3R) method. This algorithm automatically detects outliers based on the dynamics of the underlying system enabling both an order of magnitude reduction in ensemble size and continuation of systems at much higher levels of noise than classical approaches. We demonstrate our method with application to several ABM models, revealing parameter dependence, bifurcation and stability analysis of these complex systems giving a deep understanding of the dynamical behaviour of the models in a way that is not otherwise easily obtainable. In each case we demonstrate our systematic parameter determination stage for configuring the system specific EF parameters.

Optimization and Simulation of SLM Process for High Density H13 Tool Steel Parts

NASA Astrophysics Data System (ADS)

Laakso, Petri; Riipinen, Tuomas; Laukkanen, Anssi; Andersson, Tom; Jokinen, Antero; Revuelta, Alejandro; Ruusuvuori, Kimmo

This paper demonstrates the successful printing and optimization of processing parameters of high-strength H13 tool steel by Selective Laser Melting (SLM). D-Optimal Design of Experiments (DOE) approach is used for parameter optimization of laser power, scanning speed and hatch width. With 50 test samples (1×1×1cm) we establish parameter windows for these three parameters in relation to part density. The calculated numerical model is found to be in good agreement with the density data obtained from the samples using image analysis. A thermomechanical finite element simulation model is constructed of the SLM process and validated by comparing the calculated densities retrieved from the model with the experimentally determined densities. With the simulation tool one can explore the effect of different parameters on density before making any printed samples. Establishing a parameter window provides the user with freedom for parameter selection such as choosing parameters that result in fastest print speed.

iSCHRUNK--In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks.

PubMed

Andreozzi, Stefano; Miskovic, Ljubisa; Hatzimanikatis, Vassily

2016-01-01

Accurate determination of physiological states of cellular metabolism requires detailed information about metabolic fluxes, metabolite concentrations and distribution of enzyme states. Integration of fluxomics and metabolomics data, and thermodynamics-based metabolic flux analysis contribute to improved understanding of steady-state properties of metabolism. However, knowledge about kinetics and enzyme activities though essential for quantitative understanding of metabolic dynamics remains scarce and involves uncertainty. Here, we present a computational methodology that allow us to determine and quantify the kinetic parameters that correspond to a certain physiology as it is described by a given metabolic flux profile and a given metabolite concentration vector. Though we initially determine kinetic parameters that involve a high degree of uncertainty, through the use of kinetic modeling and machine learning principles we are able to obtain more accurate ranges of kinetic parameters, and hence we are able to reduce the uncertainty in the model analysis. We computed the distribution of kinetic parameters for glucose-fed E. coli producing 1,4-butanediol and we discovered that the observed physiological state corresponds to a narrow range of kinetic parameters of only a few enzymes, whereas the kinetic parameters of other enzymes can vary widely. Furthermore, this analysis suggests which are the enzymes that should be manipulated in order to engineer the reference state of the cell in a desired way. The proposed approach also sets up the foundations of a novel type of approaches for efficient, non-asymptotic, uniform sampling of solution spaces. Copyright © 2015 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Patient-specific parameter estimation in single-ventricle lumped circulation models under uncertainty

PubMed Central

Schiavazzi, Daniele E.; Baretta, Alessia; Pennati, Giancarlo; Hsia, Tain-Yen; Marsden, Alison L.

2017-01-01

Summary Computational models of cardiovascular physiology can inform clinical decision-making, providing a physically consistent framework to assess vascular pressures and flow distributions, and aiding in treatment planning. In particular, lumped parameter network (LPN) models that make an analogy to electrical circuits offer a fast and surprisingly realistic method to reproduce the circulatory physiology. The complexity of LPN models can vary significantly to account, for example, for cardiac and valve function, respiration, autoregulation, and time-dependent hemodynamics. More complex models provide insight into detailed physiological mechanisms, but their utility is maximized if one can quickly identify patient specific parameters. The clinical utility of LPN models with many parameters will be greatly enhanced by automated parameter identification, particularly if parameter tuning can match non-invasively obtained clinical data. We present a framework for automated tuning of 0D lumped model parameters to match clinical data. We demonstrate the utility of this framework through application to single ventricle pediatric patients with Norwood physiology. Through a combination of local identifiability, Bayesian estimation and maximum a posteriori simplex optimization, we show the ability to automatically determine physiologically consistent point estimates of the parameters and to quantify uncertainty induced by errors and assumptions in the collected clinical data. We show that multi-level estimation, that is, updating the parameter prior information through sub-model analysis, can lead to a significant reduction in the parameter marginal posterior variance. We first consider virtual patient conditions, with clinical targets generated through model solutions, and second application to a cohort of four single-ventricle patients with Norwood physiology. PMID:27155892

Phase diagram and criticality of the two-dimensional prisoner's dilemma model

NASA Astrophysics Data System (ADS)

Santos, M.; Ferreira, A. L.; Figueiredo, W.

2017-07-01

The stationary states of the prisoner's dilemma model are studied on a square lattice taking into account the role of a noise parameter in the decision-making process. Only first neighboring players—defectors and cooperators—are considered in each step of the game. Through Monte Carlo simulations we determined the phase diagrams of the model in the plane noise versus the temptation to defect for a large range of values of the noise parameter. We observed three phases: cooperators and defectors absorbing phases, and a coexistence phase between them. The phase transitions as well as the critical exponents associated with them were determined using both static and dynamical scaling laws.

Modelling fourier regression for time series data- a case study: modelling inflation in foods sector in Indonesia

NASA Astrophysics Data System (ADS)

Prahutama, Alan; Suparti; Wahyu Utami, Tiani

2018-03-01

Regression analysis is an analysis to model the relationship between response variables and predictor variables. The parametric approach to the regression model is very strict with the assumption, but nonparametric regression model isn’t need assumption of model. Time series data is the data of a variable that is observed based on a certain time, so if the time series data wanted to be modeled by regression, then we should determined the response and predictor variables first. Determination of the response variable in time series is variable in t-th (yt), while the predictor variable is a significant lag. In nonparametric regression modeling, one developing approach is to use the Fourier series approach. One of the advantages of nonparametric regression approach using Fourier series is able to overcome data having trigonometric distribution. In modeling using Fourier series needs parameter of K. To determine the number of K can be used Generalized Cross Validation method. In inflation modeling for the transportation sector, communication and financial services using Fourier series yields an optimal K of 120 parameters with R-square 99%. Whereas if it was modeled by multiple linear regression yield R-square 90%.

Estimation and Identifiability of Model Parameters in Human Nociceptive Processing Using Yes-No Detection Responses to Electrocutaneous Stimulation.

PubMed

Yang, Huan; Meijer, Hil G E; Buitenweg, Jan R; van Gils, Stephan A

2016-01-01

Healthy or pathological states of nociceptive subsystems determine different stimulus-response relations measured from quantitative sensory testing. In turn, stimulus-response measurements may be used to assess these states. In a recently developed computational model, six model parameters characterize activation of nerve endings and spinal neurons. However, both model nonlinearity and limited information in yes-no detection responses to electrocutaneous stimuli challenge to estimate model parameters. Here, we address the question whether and how one can overcome these difficulties for reliable parameter estimation. First, we fit the computational model to experimental stimulus-response pairs by maximizing the likelihood. To evaluate the balance between model fit and complexity, i.e., the number of model parameters, we evaluate the Bayesian Information Criterion. We find that the computational model is better than a conventional logistic model regarding the balance. Second, our theoretical analysis suggests to vary the pulse width among applied stimuli as a necessary condition to prevent structural non-identifiability. In addition, the numerically implemented profile likelihood approach reveals structural and practical non-identifiability. Our model-based approach with integration of psychophysical measurements can be useful for a reliable assessment of states of the nociceptive system.

Aeroelastic Modeling of X-56A Stiff-Wing Configuration Flight Test Data

NASA Technical Reports Server (NTRS)

Grauer, Jared A.; Boucher, Matthew J.

2017-01-01

Aeroelastic stability and control derivatives for the X-56A Multi-Utility Technology Testbed (MUTT), in the stiff-wing configuration, were estimated from flight test data using the output-error method. Practical aspects of the analysis are discussed. The orthogonal phase-optimized multisine inputs provided excellent data information for aeroelastic modeling. Consistent parameter estimates were determined using output error in both the frequency and time domains. The frequency domain analysis converged faster and was less sensitive to starting values for the model parameters, which was useful for determining the aeroelastic model structure and obtaining starting values for the time domain analysis. Including a modal description of the structure from a finite element model reduced the complexity of the estimation problem and improved the modeling results. Effects of reducing the model order on the short period stability and control derivatives were investigated.

Neutrino oscillations and Non-Standard Interactions

NASA Astrophysics Data System (ADS)

Farzan, Yasaman; Tórtola, Mariam

2018-02-01

Current neutrino experiments are measuring the neutrino mixing parameters with an unprecedented accuracy. The upcoming generation of neutrino experiments will be sensitive to subdominant oscillation effects that can give information on the yet-unknown neutrino parameters: the Dirac CP-violating phase, the mass ordering and the octant of θ_{23}. Determining the exact values of neutrino mass and mixing parameters is crucial to test neutrino models and flavor symmetries designed to predict these neutrino parameters. In the first part of this review, we summarize the current status of the neutrino oscillation parameter determination. We consider the most recent data from all solar experiments and the atmospheric data from Super-Kamiokande, IceCube and ANTARES. We also implement the data from the reactor neutrino experiments KamLAND, Daya Bay, RENO and Double Chooz as well as the long baseline neutrino data from MINOS, T2K and NOvA. If in addition to the standard interactions, neutrinos have subdominant yet-unknown Non-Standard Interactions (NSI) with matter fields, extracting the values of these parameters will suffer from new degeneracies and ambiguities. We review such effects and formulate the conditions on the NSI parameters under which the precision measurement of neutrino oscillation parameters can be distorted. Like standard weak interactions, the non-standard interaction can be categorized into two groups: Charged Current (CC) NSI and Neutral Current (NC) NSI. Our focus will be mainly on neutral current NSI because it is possible to build a class of models that give rise to sizeable NC NSI with discernible effects on neutrino oscillation. These models are based on new U(1) gauge symmetry with a gauge boson of mass ≲ 10 MeV. The UV complete model should be of course electroweak invariant which in general implies that along with neutrinos, charged fermions also acquire new interactions on which there are strong bounds. We enumerate the bounds that already exist on the electroweak symmetric models and demonstrate that it is possible to build viable models avoiding all these bounds. In the end, we review methods to test these models and suggest approaches to break the degeneracies in deriving neutrino mass parameters caused by NSI.

Efficient polarimetric BRDF model.

PubMed

Renhorn, Ingmar G E; Hallberg, Tomas; Boreman, Glenn D

2015-11-30

The purpose of the present manuscript is to present a polarimetric bidirectional reflectance distribution function (BRDF) model suitable for hyperspectral and polarimetric signature modelling. The model is based on a further development of a previously published four-parameter model that has been generalized in order to account for different types of surface structures (generalized Gaussian distribution). A generalization of the Lambertian diffuse model is presented. The pBRDF-functions are normalized using numerical integration. Using directional-hemispherical reflectance (DHR) measurements, three of the four basic parameters can be determined for any wavelength. This simplifies considerably the development of multispectral polarimetric BRDF applications. The scattering parameter has to be determined from at least one BRDF measurement. The model deals with linear polarized radiation; and in similarity with e.g. the facet model depolarization is not included. The model is very general and can inherently model extreme surfaces such as mirrors and Lambertian surfaces. The complex mixture of sources is described by the sum of two basic models, a generalized Gaussian/Fresnel model and a generalized Lambertian model. Although the physics inspired model has some ad hoc features, the predictive power of the model is impressive over a wide range of angles and scattering magnitudes. The model has been applied successfully to painted surfaces, both dull and glossy and also on metallic bead blasted surfaces. The simple and efficient model should be attractive for polarimetric simulations and polarimetric remote sensing.

A hybrid model of cell cycle in mammals.

PubMed

Behaegel, Jonathan; Comet, Jean-Paul; Bernot, Gilles; Cornillon, Emilien; Delaunay, Franck

2016-02-01

Time plays an essential role in many biological systems, especially in cell cycle. Many models of biological systems rely on differential equations, but parameter identification is an obstacle to use differential frameworks. In this paper, we present a new hybrid modeling framework that extends René Thomas' discrete modeling. The core idea is to associate with each qualitative state "celerities" allowing us to compute the time spent in each state. This hybrid framework is illustrated by building a 5-variable model of the mammalian cell cycle. Its parameters are determined by applying formal methods on the underlying discrete model and by constraining parameters using timing observations on the cell cycle. This first hybrid model presents the most important known behaviors of the cell cycle, including quiescent phase and endoreplication.

A novel and practical approach for determination of the acoustic nonlinearity parameter using a pulse-echo method

NASA Astrophysics Data System (ADS)

Jeong, Hyunjo; Zhang, Shuzeng; Barnard, Dan; Li, Xiongbing

2016-02-01

Measurements of the acoustic nonlinearity parameter β are frequently made for early detection of damage in various materials. The practical implementation of the measurement technique has been limited to the through-transmission setup for determining the nonlinearity parameter of the second harmonic wave. In this work, a feasibility study is performed to assess the possibility of using pulse-echo methods in determining the nonlinearity parameter β of solids with a stress-free boundary. The multi-Gaussian beam model is developed based on the quasilinear theory of the KZK equation. Simulation results and discussion are presented for the reflected beam fields of the fundamental and second harmonic waves, the uncorrected β behavior and the properties of total correction that incorporate reflection, attenuation and diffraction effects.

Implementation of the Global Parameters Determination in Gaia's Astrometric Solution (AGIS)

NASA Astrophysics Data System (ADS)

Raison, F.; Olias, A.; Hobbs, D.; Lindegren, L.

2010-12-01

Gaia is ESA’s space astrometry mission with a foreseen launch date in early 2012. Its main objective is to perform a stellar census of the 1000 Million brightest objects in our galaxy (completeness to V=20 mag) from which an astrometric catalog of micro-arcsec level accuracy will be constructed. A key element in this endeavor is the Astrometric Global Iterative Solution (AGIS). A core part of AGIS is to determine the accurate spacecraft attitude, geometric instrument calibration and astrometric model parameters for a well-behaved subset of all the objects (the ‘primary stars’). In addition, a small number of global parameters will be estimated, one of these being PPN γ. We present here the implementation of the algorithms dedicated to the determination of the global parameters.

Sensitivity Analysis of the Bone Fracture Risk Model

NASA Technical Reports Server (NTRS)

Lewandowski, Beth; Myers, Jerry; Sibonga, Jean Diane

2017-01-01

Introduction: The probability of bone fracture during and after spaceflight is quantified to aid in mission planning, to determine required astronaut fitness standards and training requirements and to inform countermeasure research and design. Probability is quantified with a probabilistic modeling approach where distributions of model parameter values, instead of single deterministic values, capture the parameter variability within the astronaut population and fracture predictions are probability distributions with a mean value and an associated uncertainty. Because of this uncertainty, the model in its current state cannot discern an effect of countermeasures on fracture probability, for example between use and non-use of bisphosphonates or between spaceflight exercise performed with the Advanced Resistive Exercise Device (ARED) or on devices prior to installation of ARED on the International Space Station. This is thought to be due to the inability to measure key contributors to bone strength, for example, geometry and volumetric distributions of bone mass, with areal bone mineral density (BMD) measurement techniques. To further the applicability of model, we performed a parameter sensitivity study aimed at identifying those parameter uncertainties that most effect the model forecasts in order to determine what areas of the model needed enhancements for reducing uncertainty. Methods: The bone fracture risk model (BFxRM), originally published in (Nelson et al) is a probabilistic model that can assess the risk of astronaut bone fracture. This is accomplished by utilizing biomechanical models to assess the applied loads; utilizing models of spaceflight BMD loss in at-risk skeletal locations; quantifying bone strength through a relationship between areal BMD and bone failure load; and relating fracture risk index (FRI), the ratio of applied load to bone strength, to fracture probability. There are many factors associated with these calculations including environmental factors, factors associated with the fall event, mass and anthropometric values of the astronaut, BMD characteristics, characteristics of the relationship between BMD and bone strength and bone fracture characteristics. The uncertainty in these factors is captured through the use of parameter distributions and the fracture predictions are probability distributions with a mean value and an associated uncertainty. To determine parameter sensitivity, a correlation coefficient is found between the sample set of each model parameter and the calculated fracture probabilities. Each parameters contribution to the variance is found by squaring the correlation coefficients, dividing by the sum of the squared correlation coefficients, and multiplying by 100. Results: Sensitivity analyses of BFxRM simulations of preflight, 0 days post-flight and 365 days post-flight falls onto the hip revealed a subset of the twelve factors within the model which cause the most variation in the fracture predictions. These factors include the spring constant used in the hip biomechanical model, the midpoint FRI parameter within the equation used to convert FRI to fracture probability and preflight BMD values. Future work: Plans are underway to update the BFxRM by incorporating bone strength information from finite element models (FEM) into the bone strength portion of the BFxRM. Also, FEM bone strength information along with fracture outcome data will be incorporated into the FRI to fracture probability.

Understanding the mechanisms of solid-water reactions through analysis of surface topography.

PubMed

Bandstra, Joel Z; Brantley, Susan L

2015-12-01

The topography of a reactive surface contains information about the reactions that form or modify the surface and, therefore, it should be possible to characterize reactivity using topography parameters such as surface area, roughness, or fractal dimension. As a test of this idea, we consider a two-dimensional (2D) lattice model for crystal dissolution and examine a suite of topography parameters to determine which may be useful for predicting rates and mechanisms of dissolution. The model is based on the assumption that the reactivity of a surface site decreases with the number of nearest neighbors. We show that the steady-state surface topography in our model system is a function of, at most, two variables: the ratio of the rate of loss of sites with two neighbors versus three neighbors (d(2)/d(3)) and the ratio of the rate of loss of sites with one neighbor versus three neighbors (d(1)/d(3)). This means that relative rates can be determined from two parameters characterizing the topography of a surface provided that the two parameters are independent of one another. It also means that absolute rates cannot be determined from measurements of surface topography alone. To identify independent sets of topography parameters, we simulated surfaces from a broad range of d(1)/d(3) and d(2)/d(3) and computed a suite of common topography parameters for each surface. Our results indicate that the fractal dimension D and the average spacing between steps, E[s], can serve to uniquely determine d(1)/d(3) and d(2)/d(3) provided that sufficiently strong correlations exist between the steps. Sufficiently strong correlations exist in our model system when D>1.5 (which corresponds to D>2.5 for real 3D reactive surfaces). When steps are uncorrelated, surface topography becomes independent of step retreat rate and D is equal to 1.5. Under these conditions, measures of surface topography are not independent and any single topography parameter contains all of the available mechanistic information about the surface. Our results also indicate that root-mean-square roughness cannot be used to reliably characterize the surface topography of fractal surfaces because it is an inherently noisy parameter for such surfaces with the scale of the noise being independent of length scale.

Determining the turnover time of groundwater systems with the aid of environmental tracers. 1. Models and their applicability

NASA Astrophysics Data System (ADS)

Małoszewski, P.; Zuber, A.

1982-06-01

Three new lumped-parameter models have been developed for the interpretation of environmental radioisotope data in groundwater systems. Two of these models combine other simpler models, i.e. the piston flow model is combined either with the exponential model (exponential distribution of transit times) or with the linear model (linear distribution of transit times). The third model is based on a new solution to the dispersion equation which more adequately represents the real systems than the conventional solution generally applied so far. The applicability of models was tested by the reinterpretation of several known case studies (Modry Dul, Cheju Island, Rasche Spring and Grafendorf). It has been shown that two of these models, i.e. the exponential-piston flow model and the dispersive model give better fitting than other simpler models. Thus, the obtained values of turnover times are more reliable, whereas the additional fitting parameter gives some information about the structure of the system. In the examples considered, in spite of a lower number of fitting parameters, the new models gave practically the same fitting as the multiparameter finite state mixing-cell models. It has been shown that in the case of a constant tracer input a prior physical knowledge of the groundwater system is indispensable for determining the turnover time. The piston flow model commonly used for age determinations by the 14C method is an approximation applicable only in the cases of low dispersion. In some cases the stable-isotope method aids in the interpretation of systems containing mixed waters of different ages. However, when 14C method is used for mixed-water systems a serious mistake may arise by neglecting the different bicarbonate contents in particular water components.

Agent-Based Multicellular Modeling for Predictive Toxicology

EPA Science Inventory

Biological modeling is a rapidly growing field that has benefited significantly from recent technological advances, expanding traditional methods with greater computing power, parameter-determination algorithms, and the development of novel computational approaches to modeling bi...

PESTAN: Pesticide Analytical Model Version 4.0 User's Guide

EPA Pesticide Factsheets

The principal objective of this User's Guide to provide essential information on the aspects such as model conceptualization, model theory, assumptions and limitations, determination of input parameters, analysis of results and sensitivity analysis.

Estimation of Transport and Kinetic Parameters of Vanadium Redox Batteries Using Static Cells

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

Lee, Seong Beom; Pratt, III, Harry D.; Anderson, Travis M.

Mathematical models of Redox Flow Batteries (RFBs) can be used to analyze cell performance, optimize battery operation, and control the energy storage system efficiently. Among many other models, physics-based electrochemical models are capable of predicting internal states of the battery, such as temperature, state-of-charge, and state-of-health. In the models, estimating parameters is an important step that can study, analyze, and validate the models using experimental data. A common practice is to determine these parameters either through conducting experiments or based on the information available in the literature. However, it is not easy to investigate all proper parameters for the modelsmore » through this way, and there are occasions when important information, such as diffusion coefficients and rate constants of ions, has not been studied. Also, the parameters needed for modeling charge-discharge are not always available. In this paper, an efficient way to estimate parameters of physics-based redox battery models will be proposed. Furthermore, this paper also demonstrates that the proposed approach can study and analyze aspects of capacity loss/fade, kinetics, and transport phenomena of the RFB system.« less

Estimation of Transport and Kinetic Parameters of Vanadium Redox Batteries Using Static Cells

DOE PAGES

Lee, Seong Beom; Pratt, III, Harry D.; Anderson, Travis M.; ...

2018-03-27

Mathematical models of Redox Flow Batteries (RFBs) can be used to analyze cell performance, optimize battery operation, and control the energy storage system efficiently. Among many other models, physics-based electrochemical models are capable of predicting internal states of the battery, such as temperature, state-of-charge, and state-of-health. In the models, estimating parameters is an important step that can study, analyze, and validate the models using experimental data. A common practice is to determine these parameters either through conducting experiments or based on the information available in the literature. However, it is not easy to investigate all proper parameters for the modelsmore » through this way, and there are occasions when important information, such as diffusion coefficients and rate constants of ions, has not been studied. Also, the parameters needed for modeling charge-discharge are not always available. In this paper, an efficient way to estimate parameters of physics-based redox battery models will be proposed. Furthermore, this paper also demonstrates that the proposed approach can study and analyze aspects of capacity loss/fade, kinetics, and transport phenomena of the RFB system.« less

On the in vivo photochemical rate parameters for PDT reactive oxygen species modeling

NASA Astrophysics Data System (ADS)

Kim, Michele M.; Ghogare, Ashwini A.; Greer, Alexander; Zhu, Timothy C.

2017-03-01

Photosensitizer photochemical parameters are crucial data in accurate dosimetry for photodynamic therapy (PDT) based on photochemical modeling. Progress has been made in the last few decades in determining the photochemical properties of commonly used photosensitizers (PS), but mostly in solution or in vitro. Recent developments allow for the estimation of some of these photochemical parameters in vivo. This review will cover the currently available in vivo photochemical properties of photosensitizers as well as the techniques for measuring those parameters. Furthermore, photochemical parameters that are independent of environmental factors or are universal for different photosensitizers will be examined. Most photosensitizers discussed in this review are of the type II (singlet oxygen) photooxidation category, although type I photosensitizers that involve other reactive oxygen species (ROS) will be discussed as well. The compilation of these parameters will be essential for ROS modeling of PDT.

On the in-vivo photochemical rate parameters for PDT reactive oxygen species modeling

PubMed Central

Kim, Michele M.; Ghogare, Ashwini A.; Greer, Alexander; Zhu, Timothy C.

2017-01-01

Photosensitizer photochemical parameters are crucial data in accurate dosimetry for photodynamic therapy (PDT) based on photochemical modeling. Progress has been made in the last few decades in determining the photochemical properties of commonly used photosensitizers (PS), but mostly in solution or in-vitro. Recent developments allow for the estimation of some of these photochemical parameters in-vivo. This review will cover the currently available in-vivo photochemical properties of photosensitizers as well as the techniques for measuring those parameters. Furthermore, photochemical parameters that are independent of environmental factors or are universal for different photosensitizers will be examined. Most photosensitizers discussed in this review are of the type II (singlet oxygen) photooxidation category, although type I photosensitizers that involve other reactive oxygen species (ROS) will be discussed as well. The compilation of these parameters will be essential for ROS modeling of PDT. PMID:28166056

Determining fundamental properties of matter created in ultrarelativistic heavy-ion collisions

NASA Astrophysics Data System (ADS)

Novak, J.; Novak, K.; Pratt, S.; Vredevoogd, J.; Coleman-Smith, C. E.; Wolpert, R. L.

2014-03-01

Posterior distributions for physical parameters describing relativistic heavy-ion collisions, such as the viscosity of the quark-gluon plasma, are extracted through a comparison of hydrodynamic-based transport models to experimental results from 100AGeV+100AGeV Au +Au collisions at the Relativistic Heavy Ion Collider. By simultaneously varying six parameters and by evaluating several classes of observables, we are able to explore the complex intertwined dependencies of observables on model parameters. The methods provide a full multidimensional posterior distribution for the model output, including a range of acceptable values for each parameter, and reveal correlations between them. The breadth of observables and the number of parameters considered here go beyond previous studies in this field. The statistical tools, which are based upon Gaussian process emulators, are tested in detail and should be extendable to larger data sets and a higher number of parameters.

The Rangeland Hydrology and Erosion Model: A Dynamic Approach for Predicting Soil Loss on Rangelands

NASA Astrophysics Data System (ADS)

Hernandez, Mariano; Nearing, Mark A.; Al-Hamdan, Osama Z.; Pierson, Frederick B.; Armendariz, Gerardo; Weltz, Mark A.; Spaeth, Kenneth E.; Williams, C. Jason; Nouwakpo, Sayjro K.; Goodrich, David C.; Unkrich, Carl L.; Nichols, Mary H.; Holifield Collins, Chandra D.

2017-11-01

In this study, we present the improved Rangeland Hydrology and Erosion Model (RHEM V2.3), a process-based erosion prediction tool specific for rangeland application. The article provides the mathematical formulation of the model and parameter estimation equations. Model performance is assessed against data collected from 23 runoff and sediment events in a shrub-dominated semiarid watershed in Arizona, USA. To evaluate the model, two sets of primary model parameters were determined using the RHEM V2.3 and RHEM V1.0 parameter estimation equations. Testing of the parameters indicated that RHEM V2.3 parameter estimation equations provided a 76% improvement over RHEM V1.0 parameter estimation equations. Second, the RHEM V2.3 model was calibrated to measurements from the watershed. The parameters estimated by the new equations were within the lowest and highest values of the calibrated parameter set. These results suggest that the new parameter estimation equations can be applied for this environment to predict sediment yield at the hillslope scale. Furthermore, we also applied the RHEM V2.3 to demonstrate the response of the model as a function of foliar cover and ground cover for 124 data points across Arizona and New Mexico. The dependence of average sediment yield on surface ground cover was moderately stronger than that on foliar cover. These results demonstrate that RHEM V2.3 predicts runoff volume, peak runoff, and sediment yield with sufficient accuracy for broad application to assess and manage rangeland systems.

Characterization of chemical agent transport in paints.

PubMed

Willis, Matthew P; Gordon, Wesley; Lalain, Teri; Mantooth, Brent

2013-09-15

A combination of vacuum-based vapor emission measurements with a mass transport model was employed to determine the interaction of chemical warfare agents with various materials, including transport parameters of agents in paints. Accurate determination of mass transport parameters enables the simulation of the chemical agent distribution in a material for decontaminant performance modeling. The evaluation was performed with the chemical warfare agents bis(2-chloroethyl) sulfide (distilled mustard, known as the chemical warfare blister agent HD) and O-ethyl S-[2-(diisopropylamino)ethyl] methylphosphonothioate (VX), an organophosphate nerve agent, deposited on to two different types of polyurethane paint coatings. The results demonstrated alignment between the experimentally measured vapor emission flux and the predicted vapor flux. Mass transport modeling demonstrated rapid transport of VX into the coatings; VX penetrated through the aliphatic polyurethane-based coating (100 μm) within approximately 107 min. By comparison, while HD was more soluble in the coatings, the penetration depth in the coatings was approximately 2× lower than VX. Applications of mass transport parameters include the ability to predict agent uptake, and subsequent long-term vapor emission or contact transfer where the agent could present exposure risks. Additionally, these parameters and model enable the ability to perform decontamination modeling to predict how decontaminants remove agent from these materials. Published by Elsevier B.V.

Modeling and Optimization of Class-E Amplifier at Subnominal Condition in a Wireless Power Transfer System for Biomedical Implants.

PubMed

Liu, Hao; Shao, Qi; Fang, Xuelin

2017-02-01

For the class-E amplifier in a wireless power transfer (WPT) system, the design parameters are always determined by the nominal model. However, this model neglects the conduction loss and voltage stress of MOSFET and cannot guarantee the highest efficiency in the WPT system for biomedical implants. To solve this problem, this paper proposes a novel circuit model of the subnominal class-E amplifier. On a WPT platform for capsule endoscope, the proposed model was validated to be effective and the relationship between the amplifier's design parameters and its characteristics was analyzed. At a given duty ratio, the design parameters with the highest efficiency and safe voltage stress are derived and the condition is called 'optimal subnominal condition.' The amplifier's efficiency can reach the highest of 99.3% at the 0.097 duty ratio. Furthermore, at the 0.5 duty ratio, the measured efficiency of the optimal subnominal condition can reach 90.8%, which is 15.2% higher than that of the nominal condition. Then, a WPT experiment with a receiving unit was carried out to validate the feasibility of the optimized amplifier. In general, the design parameters of class-E amplifier in a WPT system for biomedical implants can be determined with the proposed optimization method in this paper.

Estimation of Filling and Afterload Conditions by Pump Intrinsic Parameters in a Pulsatile Total Artificial Heart.

PubMed

Cuenca-Navalon, Elena; Laumen, Marco; Finocchiaro, Thomas; Steinseifer, Ulrich

2016-07-01

A physiological control algorithm is being developed to ensure an optimal physiological interaction between the ReinHeart total artificial heart (TAH) and the circulatory system. A key factor for that is the long-term, accurate determination of the hemodynamic state of the cardiovascular system. This study presents a method to determine estimation models for predicting hemodynamic parameters (pump chamber filling and afterload) from both left and right cardiovascular circulations. The estimation models are based on linear regression models that correlate filling and afterload values with pump intrinsic parameters derived from measured values of motor current and piston position. Predictions for filling lie in average within 5% from actual values, predictions for systemic afterload (AoPmean , AoPsys ) and mean pulmonary afterload (PAPmean ) lie in average within 9% from actual values. Predictions for systolic pulmonary afterload (PAPsys ) present an average deviation of 14%. The estimation models show satisfactory prediction and confidence intervals and are thus suitable to estimate hemodynamic parameters. This method and derived estimation models are a valuable alternative to implanted sensors and are an essential step for the development of a physiological control algorithm for a fully implantable TAH. Copyright © 2015 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Constraints on pulsed emission model for repeating FRB 121102

NASA Astrophysics Data System (ADS)

Kisaka, Shota; Enoto, Teruaki; Shibata, Shinpei

2017-12-01

Recent localization of the repeating fast radio burst (FRB) 121102 revealed the distance of its host galaxy and luminosities of the bursts. We investigated constraints on the young neutron star (NS) model, that (a) the FRB intrinsic luminosity is supported by the spin-down energy, and (b) the FRB duration is shorter than the NS rotation period. In the case of a circular cone emission geometry, conditions (a) and (b) determine the NS parameters within very small ranges, compared with that from only condition (a) discussed in previous works. Anisotropy of the pulsed emission does not affect the area of the allowed parameter region by virtue of condition (b). The determined parameters are consistent with those independently limited by the properties of the possible persistent radio counterpart and the circumburst environments such as surrounding materials. Since the NS in the allowed parameter region is older than the spin-down timescale, the hypothetical GRP (giant radio pulse)-like model expects a rapid radio flux decay of ≲1 Jy within a few years as the spin-down luminosity decreases. The continuous monitoring will provide constraints on the young NS models. If no flux evolution is seen, we need to consider an alternative model, e.g., the magnetically powered flare.

Definition and solution of a stochastic inverse problem for the Manning’s n parameter field in hydrodynamic models

DOE PAGES

Butler, Troy; Graham, L.; Estep, D.; ...

2015-02-03

The uncertainty in spatially heterogeneous Manning’s n fields is quantified using a novel formulation and numerical solution of stochastic inverse problems for physics-based models. The uncertainty is quantified in terms of a probability measure and the physics-based model considered here is the state-of-the-art ADCIRC model although the presented methodology applies to other hydrodynamic models. An accessible overview of the formulation and solution of the stochastic inverse problem in a mathematically rigorous framework based on measure theory is presented in this paper. Technical details that arise in practice by applying the framework to determine the Manning’s n parameter field in amore » shallow water equation model used for coastal hydrodynamics are presented and an efficient computational algorithm and open source software package are developed. A new notion of “condition” for the stochastic inverse problem is defined and analyzed as it relates to the computation of probabilities. Finally, this notion of condition is investigated to determine effective output quantities of interest of maximum water elevations to use for the inverse problem for the Manning’s n parameter and the effect on model predictions is analyzed.« less

Estimating short-period dynamics using an extended Kalman filter

NASA Technical Reports Server (NTRS)

Bauer, Jeffrey E.; Andrisani, Dominick

1990-01-01

An extended Kalman filter (EKF) is used to estimate the parameters of a low-order model from aircraft transient response data. The low-order model is a state space model derived from the short-period approximation of the longitudinal aircraft dynamics. The model corresponds to the pitch rate to stick force transfer function currently used in flying qualities analysis. Because of the model chosen, handling qualities information is also obtained. The parameters are estimated from flight data as well as from a six-degree-of-freedom, nonlinear simulation of the aircraft. These two estimates are then compared and the discrepancies noted. The low-order model is able to satisfactorily match both flight data and simulation data from a high-order computer simulation. The parameters obtained from the EKF analysis of flight data are compared to those obtained using frequency response analysis of the flight data. Time delays and damping ratios are compared and are in agreement. This technique demonstrates the potential to determine, in near real time, the extent of differences between computer models and the actual aircraft. Precise knowledge of these differences can help to determine the flying qualities of a test aircraft and lead to more efficient envelope expansion.

A Model Parameter Extraction Method for Dielectric Barrier Discharge Ozone Chamber using Differential Evolution

NASA Astrophysics Data System (ADS)

Amjad, M.; Salam, Z.; Ishaque, K.

2014-04-01

In order to design an efficient resonant power supply for ozone gas generator, it is necessary to accurately determine the parameters of the ozone chamber. In the conventional method, the information from Lissajous plot is used to estimate the values of these parameters. However, the experimental setup for this purpose can only predict the parameters at one operating frequency and there is no guarantee that it results in the highest ozone gas yield. This paper proposes a new approach to determine the parameters using a search and optimization technique known as Differential Evolution (DE). The desired objective function of DE is set at the resonance condition and the chamber parameter values can be searched regardless of experimental constraints. The chamber parameters obtained from the DE technique are validated by experiment.

Determination of effective electromagnetic parameters of concentrated suspensions of ellipsoidal particles using Generalized Differential Effective Medium approximation

NASA Astrophysics Data System (ADS)

Markov, M.; Levin, V.; Markova, I.

2018-02-01

The paper presents an approach to determine the effective electromagnetic parameters of suspensions of ellipsoidal dielectric particles with surface conductivity. This approach takes into account the existence of critical porosity that corresponds to the maximum packing volume fraction of solid inclusions. The approach is based on the Generalized Differential Effective Medium (GDEM) method. We have introduced a model of suspensions containing ellipsoidal inclusions of two types. Inclusions of the first type (phase 1) represent solid grains, and inclusions of the second type (phase 2) contain material with the same physical properties as the host (phase 0). In this model, with increasing porosity the concentration of the host decreases, and it tends to zero near the critical porosity. The proposed model has been used to simulate the effective electromagnetic parameters of concentrated suspensions. We have compared the modeling results for electrical conductivity and dielectric permittivity with the empirical equations. The results obtained have shown that the GDEM model describes the effective electrical conductivity and dielectric permittivity of suspensions in a wide range of inclusion concentrations.

Evaluation of MEDALUS model for desertification hazard zonation using GIS; study area: Iyzad Khast plain, Iran.

PubMed

Farajzadeh, Manuchehr; Egbal, Mahbobeh Nik

2007-08-15

In this study, the MEDALUS model along with GIS mapping techniques are used to determine desertification hazards for a province of Iran to determine the desertification hazard. After creating a desertification database including 20 parameters, the first steps consisted of developing maps of four indices for the MEDALUS model including climate, soil, vegetation and land use were prepared. Since these parameters have mostly been presented for the Mediterranean region in the past, the next step included the addition of other indicators such as ground water and wind erosion. Then all of the layers weighted by environmental conditions present in the area were used (following the same MEDALUS framework) before a desertification map was prepared. The comparison of two maps based on the original and modified MEDALUS models indicates that the addition of more regionally-specific parameters into the model allows for a more accurate representation of desertification processes across the Iyzad Khast plain. The major factors affecting desertification in the area are climate, wind erosion and low land quality management, vegetation degradation and the salinization of soil and water resources.

Electrostatics of cysteine residues in proteins: parameterization and validation of a simple model.

PubMed

Salsbury, Freddie R; Poole, Leslie B; Fetrow, Jacquelyn S

2012-11-01

One of the most popular and simple models for the calculation of pK(a) s from a protein structure is the semi-macroscopic electrostatic model MEAD. This model requires empirical parameters for each residue to calculate pK(a) s. Analysis of current, widely used empirical parameters for cysteine residues showed that they did not reproduce expected cysteine pK(a) s; thus, we set out to identify parameters consistent with the CHARMM27 force field that capture both the behavior of typical cysteines in proteins and the behavior of cysteines which have perturbed pK(a) s. The new parameters were validated in three ways: (1) calculation across a large set of typical cysteines in proteins (where the calculations are expected to reproduce expected ensemble behavior); (2) calculation across a set of perturbed cysteines in proteins (where the calculations are expected to reproduce the shifted ensemble behavior); and (3) comparison to experimentally determined pK(a) values (where the calculation should reproduce the pK(a) within experimental error). Both the general behavior of cysteines in proteins and the perturbed pK(a) in some proteins can be predicted reasonably well using the newly determined empirical parameters within the MEAD model for protein electrostatics. This study provides the first general analysis of the electrostatics of cysteines in proteins, with specific attention paid to capturing both the behavior of typical cysteines in a protein and the behavior of cysteines whose pK(a) should be shifted, and validation of force field parameters for cysteine residues. Copyright © 2012 Wiley Periodicals, Inc.

Parameter dependences of the separatrix density in nitrogen seeded ASDEX Upgrade H-mode discharges

NASA Astrophysics Data System (ADS)

Kallenbach, A.; Sun, H. J.; Eich, T.; Carralero, D.; Hobirk, J.; Scarabosio, A.; Siccinio, M.; ASDEX Upgrade Team; EUROfusion MST1 Team

2018-04-01

The upstream separatrix electron density is an important interface parameter for core performance and divertor power exhaust. It has been measured in ASDEX Upgrade H-mode discharges by means of Thomson scattering using a self-consistent estimate of the upstream electron temperature under the assumption of Spitzer-Härm electron conduction. Its dependence on various plasma parameters has been tested for different plasma conditions in H-mode. The leading parameter determining n e,sep was found to be the neutral divertor pressure, which can be considered as an engineering parameter since it is determined mainly by the gas puff rate and the pumping speed. The experimentally found parameter dependence of n e,sep, which is dominated by the divertor neutral pressure, could be approximately reconciled by 2-point modelling.

Modeling of laser interactions with composite materials

DOE PAGES

Rubenchik, Alexander M.; Boley, Charles D.

2013-05-07

In this study, we develop models of laser interactions with composite materials consisting of fibers embedded within a matrix. A ray-trace model is shown to determine the absorptivity, absorption depth, and optical power enhancement within the material, as well as the angular distribution of the reflected light. We also develop a macroscopic model, which provides physical insight and overall results. We show that the parameters in this model can be determined from the ray trace model.

Rate-equation modelling and ensemble approach to extraction of parameters for viral infection-induced cell apoptosis and necrosis

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

Domanskyi, Sergii; Schilling, Joshua E.; Privman, Vladimir, E-mail: privman@clarkson.edu

We develop a theoretical approach that uses physiochemical kinetics modelling to describe cell population dynamics upon progression of viral infection in cell culture, which results in cell apoptosis (programmed cell death) and necrosis (direct cell death). Several model parameters necessary for computer simulation were determined by reviewing and analyzing available published experimental data. By comparing experimental data to computer modelling results, we identify the parameters that are the most sensitive to the measured system properties and allow for the best data fitting. Our model allows extraction of parameters from experimental data and also has predictive power. Using the model wemore » describe interesting time-dependent quantities that were not directly measured in the experiment and identify correlations among the fitted parameter values. Numerical simulation of viral infection progression is done by a rate-equation approach resulting in a system of “stiff” equations, which are solved by using a novel variant of the stochastic ensemble modelling approach. The latter was originally developed for coupled chemical reactions.« less

Developing a methodology for the inverse estimation of root architectural parameters from field based sampling schemes

NASA Astrophysics Data System (ADS)

Morandage, Shehan; Schnepf, Andrea; Vanderborght, Jan; Javaux, Mathieu; Leitner, Daniel; Laloy, Eric; Vereecken, Harry

2017-04-01

Root traits are increasingly important in breading of new crop varieties. E.g., longer and fewer lateral roots are suggested to improve drought resistance of wheat. Thus, detailed root architectural parameters are important. However, classical field sampling of roots only provides more aggregated information such as root length density (coring), root counts per area (trenches) or root arrival curves at certain depths (rhizotubes). We investigate the possibility of obtaining the information about root system architecture of plants using field based classical root sampling schemes, based on sensitivity analysis and inverse parameter estimation. This methodology was developed based on a virtual experiment where a root architectural model was used to simulate root system development in a field, parameterized for winter wheat. This information provided the ground truth which is normally unknown in a real field experiment. The three sampling schemes coring, trenching, and rhizotubes where virtually applied to and aggregated information computed. Morris OAT global sensitivity analysis method was then performed to determine the most sensitive parameters of root architecture model for the three different sampling methods. The estimated means and the standard deviation of elementary effects of a total number of 37 parameters were evaluated. Upper and lower bounds of the parameters were obtained based on literature and published data of winter wheat root architectural parameters. Root length density profiles of coring, arrival curve characteristics observed in rhizotubes, and root counts in grids of trench profile method were evaluated statistically to investigate the influence of each parameter using five different error functions. Number of branches, insertion angle inter-nodal distance, and elongation rates are the most sensitive parameters and the parameter sensitivity varies slightly with the depth. Most parameters and their interaction with the other parameters show highly nonlinear effect to the model output. The most sensitive parameters will be subject to inverse estimation from the virtual field sampling data using DREAMzs algorithm. The estimated parameters can then be compared with the ground truth in order to determine the suitability of the sampling schemes to identify specific traits or parameters of the root growth model.

A model for flexi-bar to evaluate intervertebral disc and muscle forces in exercises.

PubMed

Abdollahi, Masoud; Nikkhoo, Mohammad; Ashouri, Sajad; Asghari, Mohsen; Parnianpour, Mohamad; Khalaf, Kinda

2016-10-01

This study developed and validated a lumped parameter model for the FLEXI-BAR, a popular training instrument that provides vibration stimulation. The model which can be used in conjunction with musculoskeletal-modeling software for quantitative biomechanical analyses, consists of 3 rigid segments, 2 torsional springs, and 2 torsional dashpots. Two different sets of experiments were conducted to determine the model's key parameters including the stiffness of the springs and the damping ratio of the dashpots. In the first set of experiments, the free vibration of the FLEXI-BAR with an initial displacement at its end was considered, while in the second set, forced oscillations of the bar were studied. The properties of the mechanical elements in the lumped parameter model were derived utilizing a non-linear optimization algorithm which minimized the difference between the model's prediction and the experimental data. The results showed that the model is valid (8% error) and can be used for simulating exercises with the FLEXI-BAR for excitations in the range of the natural frequency. The model was then validated in combination with AnyBody musculoskeletal modeling software, where various lumbar disc, spinal muscles and hand muscles forces were determined during different FLEXI-BAR exercise simulations. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

Model parameter extraction of lateral propagating surface acoustic waves with coupling on SiO2/grating/LiNbO3 structure

NASA Astrophysics Data System (ADS)

Zhang, Benfeng; Han, Tao; Li, Xinyi; Huang, Yulin; Omori, Tatsuya; Hashimoto, Ken-ya

2018-07-01

This paper investigates how lateral propagation of Rayleigh and shear horizontal (SH) surface acoustic waves (SAWs) changes with rotation angle θ and SiO2 and electrode thicknesses, h SiO2 and h Cu, respectively. The extended thin plate model is used for purpose. First, the extraction method is presented for determining parameters appearing in the extended thin plate model. Then, the model parameters are expressed in polynomials in terms of h SiO2, h Cu, and θ. Finally, a piston mode structure without phase shifters is designed using the extracted parameters. The possible piston mode structures can be searched automatically by use of the polynomial expression. The resonance characteristics are analyzed by both the extended thin plate model and three-dimensional (3D) finite element method (FEM). Agreement between the results of both methods confirms validity and effectiveness of the parameter extraction process and the design technique.

Analysis of the statistical thermodynamic model for nonlinear binary protein adsorption equilibria.

PubMed

Zhou, Xiao-Peng; Su, Xue-Li; Sun, Yan

2007-01-01

The statistical thermodynamic (ST) model was used to study nonlinear binary protein adsorption equilibria on an anion exchanger. Single-component and binary protein adsorption isotherms of bovine hemoglobin (Hb) and bovine serum albumin (BSA) on DEAE Spherodex M were determined by batch adsorption experiments in 10 mM Tris-HCl buffer containing a specific NaCl concentration (0.05, 0.10, and 0.15 M) at pH 7.40. The ST model was found to depict the effect of ionic strength on the single-component equilibria well, with model parameters depending on ionic strength. Moreover, the ST model gave acceptable fitting to the binary adsorption data with the fitted single-component model parameters, leading to the estimation of the binary ST model parameter. The effects of ionic strength on the model parameters are reasonably interpreted by the electrostatic and thermodynamic theories. The effective charge of protein in adsorption phase can be separately calculated from the two categories of the model parameters, and the values obtained from the two methods are consistent. The results demonstrate the utility of the ST model for describing nonlinear binary protein adsorption equilibria.

HUMAN EYE OPTICS: Determination of positions of optical elements of the human eye

NASA Astrophysics Data System (ADS)

Galetskii, S. O.; Cherezova, T. Yu

2009-02-01

An original method for noninvasive determining the positions of elements of intraocular optics is proposed. The analytic dependence of the measurement error on the optical-scheme parameters and the restriction in distance from the element being measured are determined within the framework of the method proposed. It is shown that the method can be efficiently used for determining the position of elements in the classical Gullstrand eye model and personalised eye models. The positions of six optical surfaces of the Gullstrand eye model and four optical surfaces of the personalised eye model can be determined with an error of less than 0.25 mm.

The application of remote sensing to the development and formulation of hydrologic planning models

NASA Technical Reports Server (NTRS)

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

1977-01-01

The development of a remote sensing model and its efficiency in determining parameters of hydrologic models are reviewed. Procedures for extracting hydrologic data from LANDSAT imagery, and the visual analysis of composite imagery are presented. A hydrologic planning model is developed and applied to determine seasonal variations in watershed conditions. The transfer of this technology to a user community and contract arrangements are discussed.

Simulating settlement during waste placement at a landfill with waste lifts placed under frozen conditions.

PubMed

Van Geel, Paul J; Murray, Kathleen E

2015-12-01

Twelve instrument bundles were placed within two waste profiles as waste was placed in an operating landfill in Ste. Sophie, Quebec, Canada. The settlement data were simulated using a three-component model to account for primary or instantaneous compression, secondary compression or mechanical creep and biodegradation induced settlement. The regressed model parameters from the first waste layer were able to predict the settlement of the remaining four waste layers with good agreement. The model parameters were compared to values published in the literature. A MSW landfill scenario referenced in the literature was used to illustrate how the parameter values from the different studies predicted settlement. The parameters determined in this study and other studies with total waste heights between 15 and 60 m provided similar estimates of total settlement in the long term while the settlement rates and relative magnitudes of the three components varied. The parameters determined based on studies with total waste heights less than 15m resulted in larger secondary compression indices and lower biodegradation induced settlements. When these were applied to a MSW landfill scenario with a total waste height of 30 m, the settlement was overestimated and provided unrealistic values. This study concludes that more field studies are needed to measure waste settlement during the filling stage of landfill operations and more field data are needed to assess different settlement models and their respective parameters. Copyright © 2015 Elsevier Ltd. All rights reserved.

Inverting multiple suites of thermal indicator data to constrain the heat flow history: A case study from east Kalimantan, Indonesia

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

Mudford, B.S.

1996-12-31

The determination of an appropriate thermal history in an exploration area is of fundamental importance when attempting to understand the evolution of the petroleum system. In this talk we present the results of a single-well modelling study in which bottom hole temperature data, vitrinite reflectance data and three different biomarker ratio datasets were available to constrain the modelling. Previous modelling studies using biomarker ratios have been hampered by the wide variety of published kinetic parameters for biomarker evolution. Generally, these parameters have been determined either from measurements in the laboratory and extrapolation to the geological setting, or from downhole measurementsmore » where the heat flow history is assumed to be known. In the first case serious errors can arise because the heating rate is being extrapolated over many orders of magnitude, while in the second case errors can arise if the assumed heat flow history is incorrect. To circumvent these problems we carried out a parameter optimization in which the heat flow history was treated as an unknown in addition to the biomarker ratio kinetic parameters. This method enabled the heat flow history for the area to be determined together with appropriate kinetic parameters for the three measured biomarker ratios. Within the resolution of the data, the heat flow since the early Miocene has been relatively constant at levels required to yield good agreement between predicted and measured subsurface temperatures.« less

Inverting multiple suites of thermal indicator data to constrain the heat flow history: A case study from east Kalimantan, Indonesia

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

Mudford, B.S.

1996-01-01

The determination of an appropriate thermal history in an exploration area is of fundamental importance when attempting to understand the evolution of the petroleum system. In this talk we present the results of a single-well modelling study in which bottom hole temperature data, vitrinite reflectance data and three different biomarker ratio datasets were available to constrain the modelling. Previous modelling studies using biomarker ratios have been hampered by the wide variety of published kinetic parameters for biomarker evolution. Generally, these parameters have been determined either from measurements in the laboratory and extrapolation to the geological setting, or from downhole measurementsmore » where the heat flow history is assumed to be known. In the first case serious errors can arise because the heating rate is being extrapolated over many orders of magnitude, while in the second case errors can arise if the assumed heat flow history is incorrect. To circumvent these problems we carried out a parameter optimization in which the heat flow history was treated as an unknown in addition to the biomarker ratio kinetic parameters. This method enabled the heat flow history for the area to be determined together with appropriate kinetic parameters for the three measured biomarker ratios. Within the resolution of the data, the heat flow since the early Miocene has been relatively constant at levels required to yield good agreement between predicted and measured subsurface temperatures.« less

Determination of Phobos' rotational parameters by an inertial frame bundle block adjustment

NASA Astrophysics Data System (ADS)

Burmeister, Steffi; Willner, Konrad; Schmidt, Valentina; Oberst, Jürgen

2018-01-01

A functional model for a bundle block adjustment in the inertial reference frame was developed, implemented and tested. This approach enables the determination of rotation parameters of planetary bodies on the basis of photogrammetric observations. Tests with a self-consistent synthetic data set showed that the implementation converges reliably toward the expected values of the introduced unknown parameters of the adjustment, e.g., spin pole orientation, and that it can cope with typical observational errors in the data. We applied the model to a data set of Phobos using images from the Mars Express and the Viking mission. With Phobos being in a locked rotation, we computed a forced libration amplitude of 1.14^circ ± 0.03^circ together with a control point network of 685 points.

Bottom friction models for shallow water equations: Manning’s roughness coefficient and small-scale bottom heterogeneity

NASA Astrophysics Data System (ADS)

Dyakonova, Tatyana; Khoperskov, Alexander

2018-03-01

The correct description of the surface water dynamics in the model of shallow water requires accounting for friction. To simulate a channel flow in the Chezy model the constant Manning roughness coefficient is frequently used. The Manning coefficient nM is an integral parameter which accounts for a large number of physical factors determining the flow braking. We used computational simulations in a shallow water model to determine the relationship between the Manning coefficient and the parameters of small-scale perturbations of a bottom in a long channel. Comparing the transverse water velocity profiles in the channel obtained in the models with a perturbed bottom without bottom friction and with bottom friction on a smooth bottom, we constructed the dependence of nM on the amplitude and spatial scale of perturbation of the bottom relief.

State and Parameter Estimation for a Coupled Ocean--Atmosphere Model

NASA Astrophysics Data System (ADS)

Ghil, M.; Kondrashov, D.; Sun, C.

2006-12-01

The El-Nino/Southern-Oscillation (ENSO) dominates interannual climate variability and plays, therefore, a key role in seasonal-to-interannual prediction. Much is known by now about the main physical mechanisms that give rise to and modulate ENSO, but the values of several parameters that enter these mechanisms are an important unknown. We apply Extended Kalman Filtering (EKF) for both model state and parameter estimation in an intermediate, nonlinear, coupled ocean--atmosphere model of ENSO. The coupled model consists of an upper-ocean, reduced-gravity model of the Tropical Pacific and a steady-state atmospheric response to the sea surface temperature (SST). The model errors are assumed to be mainly in the atmospheric wind stress, and assimilated data are equatorial Pacific SSTs. Model behavior is very sensitive to two key parameters: (i) μ, the ocean-atmosphere coupling coefficient between SST and wind stress anomalies; and (ii) δs, the surface-layer coefficient. Previous work has shown that δs determines the period of the model's self-sustained oscillation, while μ measures the degree of nonlinearity. Depending on the values of these parameters, the spatio-temporal pattern of model solutions is either that of a delayed oscillator or of a westward propagating mode. Estimation of these parameters is tested first on synthetic data and allows us to recover the delayed-oscillator mode starting from model parameter values that correspond to the westward-propagating case. Assimilation of SST data from the NCEP-NCAR Reanalysis-2 shows that the parameters can vary on fairly short time scales and switch between values that approximate the two distinct modes of ENSO behavior. Rapid adjustments of these parameters occur, in particular, during strong ENSO events. Ways to apply EKF parameter estimation efficiently to state-of-the-art coupled ocean--atmosphere GCMs will be discussed.

Parameter sensitivity analysis and optimization for a satellite-based evapotranspiration model across multiple sites using Moderate Resolution Imaging Spectroradiometer and flux data

NASA Astrophysics Data System (ADS)

Zhang, Kun; Ma, Jinzhu; Zhu, Gaofeng; Ma, Ting; Han, Tuo; Feng, Li Li

2017-01-01

Global and regional estimates of daily evapotranspiration are essential to our understanding of the hydrologic cycle and climate change. In this study, we selected the radiation-based Priestly-Taylor Jet Propulsion Laboratory (PT-JPL) model and assessed it at a daily time scale by using 44 flux towers. These towers distributed in a wide range of ecological systems: croplands, deciduous broadleaf forest, evergreen broadleaf forest, evergreen needleleaf forest, grasslands, mixed forests, savannas, and shrublands. A regional land surface evapotranspiration model with a relatively simple structure, the PT-JPL model largely uses ecophysiologically-based formulation and parameters to relate potential evapotranspiration to actual evapotranspiration. The results using the original model indicate that the model always overestimates evapotranspiration in arid regions. This likely results from the misrepresentation of water limitation and energy partition in the model. By analyzing physiological processes and determining the sensitive parameters, we identified a series of parameter sets that can increase model performance. The model with optimized parameters showed better performance (R2 = 0.2-0.87; Nash-Sutcliffe efficiency (NSE) = 0.1-0.87) at each site than the original model (R2 = 0.19-0.87; NSE = -12.14-0.85). The results of the optimization indicated that the parameter β (water control of soil evaporation) was much lower in arid regions than in relatively humid regions. Furthermore, the optimized value of parameter m1 (plant control of canopy transpiration) was mostly between 1 to 1.3, slightly lower than the original value. Also, the optimized parameter Topt correlated well to the actual environmental temperature at each site. We suggest that using optimized parameters with the PT-JPL model could provide an efficient way to improve the model performance.

Physical and numerical studies of a fracture system model

NASA Astrophysics Data System (ADS)

Piggott, Andrew R.; Elsworth, Derek

1989-03-01

Physical and numerical studies of transient flow in a model of discretely fractured rock are presented. The physical model is a thermal analogue to fractured media flow consisting of idealized disc-shaped fractures. The numerical model is used to predict the behavior of the physical model. The use of different insulating materials to encase the physical model allows the effects of differing leakage magnitudes to be examined. A procedure for determining appropriate leakage parameters is documented. These parameters are used in forward analysis to predict the thermal response of the physical model. Knowledge of the leakage parameters and of the temporal variation of boundary conditions are shown to be essential to an accurate prediction. Favorable agreement is illustrated between numerical and physical results. The physical model provides a data source for the benchmarking of alternative numerical algorithms.

A viable dark fluid model

NASA Astrophysics Data System (ADS)

Elkhateeb, Esraa

2018-01-01

We consider a cosmological model based on a generalization of the equation of state proposed by Nojiri and Odintsov (2004) and Štefančić (2005, 2006). We argue that this model works as a dark fluid model which can interpolate between dust equation of state and the dark energy equation of state. We show how the asymptotic behavior of the equation of state constrained the parameters of the model. The causality condition for the model is also studied to constrain the parameters and the fixed points are tested to determine different solution classes. Observations of Hubble diagram of SNe Ia supernovae are used to further constrain the model. We present an exact solution of the model and calculate the luminosity distance and the energy density evolution. We also calculate the deceleration parameter to test the state of the universe expansion.

Radar cross section models for limited aspect angle windows

NASA Astrophysics Data System (ADS)

Robinson, Mark C.

1992-12-01

This thesis presents a method for building Radar Cross Section (RCS) models of aircraft based on static data taken from limited aspect angle windows. These models statistically characterize static RCS. This is done to show that a limited number of samples can be used to effectively characterize static aircraft RCS. The optimum models are determined by performing both a Kolmogorov and a Chi-Square goodness-of-fit test comparing the static RCS data with a variety of probability density functions (pdf) that are known to be effective at approximating the static RCS of aircraft. The optimum parameter estimator is also determined by the goodness of-fit tests if there is a difference in pdf parameters obtained by the Maximum Likelihood Estimator (MLE) and the Method of Moments (MoM) estimators.

Selection of optimum ionic liquid solvents for flavonoid and phenolic acids extraction

NASA Astrophysics Data System (ADS)

Rahman, N. R. A.; Yunus, N. A.; Mustaffa, A. A.

2017-06-01

Phytochemicals are important in improving human health with their functions as antioxidants, antimicrobials and anticancer agents. However, the quality of phytochemicals extract relies on the efficiency of extraction process. Ionic liquids (ILs) have become a research phenomenal as extraction solvent due to their unique properties such as unlimited range of ILs, non-volatile, strongly solvating and may become either polarity. In phytochemical extraction, the determination of the best solvent that can extract highest yield of solute (phytochemical) is very important. Therefore, this study is conducted to determine the best IL solvent to extract flavonoids and phenolic acids through a property prediction modeling approach. ILs were selected from the imidazolium-based anion for alkyl chains ranging from ethyl > octyl and cations consisting of Br, Cl, [PF6], BF4], [H2PO4], [SO4], [CF3SO3], [TF2N] and [HSO4]. This work are divided into several stages. In Stage 1, a Microsoft Excel-based database containing available solubility parameter values of phytochemicals and ILs including its prediction models and their parameters has been established. The database also includes available solubility data of phytochemicals in IL, and activity coefficient models, for solid-liquid phase equilibrium (SLE) calculations. In Stage 2, the solubility parameter values of the flavonoids (e.g. kaempferol, quercetin and myricetin) and phenolic acids (e.g. gallic acid and caffeic acid) are determined either directly from database or predicted using Stefanis and Marrero-Gani group contribution model for the phytochemicals. A cation-anion contribution model is used for IL. In Stage 3, the amount of phytochemicals extracted can be determined by using SLE relationship involving UNIFAC-IL model. For missing parameters (UNIFAC-IL), they are regressed using available solubility data. Finally, in Stage 4, the solvent candidates are ranked and five ILs, ([OMIM] [TF2N], [HeMIM] [TF2N], [HMIM] [TF2N], [HeMIM] [CF3SO3] and [HMIM] [CF3SO3]) were identified and selected.

Gradient parameter and axial and field rays in the gradient-index crystalline lens model

NASA Astrophysics Data System (ADS)

Pérez, M. V.; Bao, C.; Flores-Arias, M. T.; Rama, M. A.; Gómez-Reino, C.

2003-09-01

Gradient-index models of the human lens have received wide attention in optometry and vision sciences for considering how changes in the refractive index profile with age and accommodation may affect refractive power. This paper uses the continuous asymmetric bi-elliptical model to determine gradient parameter and axial and field rays of the human lens in order to study the paraxial propagation of light through the crystalline lens of the eye.

TH-E-BRF-06: Kinetic Modeling of Tumor Response to Fractionated Radiotherapy

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

Zhong, H; Gordon, J; Chetty, I

2014-06-15

Purpose: Accurate calibration of radiobiological parameters is crucial to predicting radiation treatment response. Modeling differences may have a significant impact on calibrated parameters. In this study, we have integrated two existing models with kinetic differential equations to formulate a new tumor regression model for calibrating radiobiological parameters for individual patients. Methods: A system of differential equations that characterizes the birth-and-death process of tumor cells in radiation treatment was analytically solved. The solution of this system was used to construct an iterative model (Z-model). The model consists of three parameters: tumor doubling time Td, half-life of dying cells Tr and cellmore » survival fraction SFD under dose D. The Jacobian determinant of this model was proposed as a constraint to optimize the three parameters for six head and neck cancer patients. The derived parameters were compared with those generated from the two existing models, Chvetsov model (C-model) and Lim model (L-model). The C-model and L-model were optimized with the parameter Td fixed. Results: With the Jacobian-constrained Z-model, the mean of the optimized cell survival fractions is 0.43±0.08, and the half-life of dying cells averaged over the six patients is 17.5±3.2 days. The parameters Tr and SFD optimized with the Z-model differ by 1.2% and 20.3% from those optimized with the Td-fixed C-model, and by 32.1% and 112.3% from those optimized with the Td-fixed L-model, respectively. Conclusion: The Z-model was analytically constructed from the cellpopulation differential equations to describe changes in the number of different tumor cells during the course of fractionated radiation treatment. The Jacobian constraints were proposed to optimize the three radiobiological parameters. The developed modeling and optimization methods may help develop high-quality treatment regimens for individual patients.« less

Mixed Poisson distributions in exact solutions of stochastic autoregulation models.

PubMed

Iyer-Biswas, Srividya; Jayaprakash, C

2014-11-01

In this paper we study the interplay between stochastic gene expression and system design using simple stochastic models of autoactivation and autoinhibition. Using the Poisson representation, a technique whose particular usefulness in the context of nonlinear gene regulation models we elucidate, we find exact results for these feedback models in the steady state. Further, we exploit this representation to analyze the parameter spaces of each model, determine which dimensionless combinations of rates are the shape determinants for each distribution, and thus demarcate where in the parameter space qualitatively different behaviors arise. These behaviors include power-law-tailed distributions, bimodal distributions, and sub-Poisson distributions. We also show how these distribution shapes change when the strength of the feedback is tuned. Using our results, we reexamine how well the autoinhibition and autoactivation models serve their conventionally assumed roles as paradigms for noise suppression and noise exploitation, respectively.

Experimental validation of analytical models for a rapid determination of cycle parameters in thermoplastic injection molding

NASA Astrophysics Data System (ADS)

Pignon, Baptiste; Sobotka, Vincent; Boyard, Nicolas; Delaunay, Didier

2017-10-01

Two different analytical models were presented to determine cycle parameters of thermoplastics injection process. The aim of these models was to provide quickly a first set of data for mold temperature and cooling time. The first model is specific to amorphous polymers and the second one is dedicated to semi-crystalline polymers taking the crystallization into account. In both cases, the nature of the contact between the polymer and the mold could be considered as perfect or not (thermal contact resistance was considered). Results from models are compared with experimental data obtained with an instrumented mold for an acrylonitrile butadiene styrene (ABS) and a polypropylene (PP). Good agreements were obtained for mold temperature variation and for heat flux. In the case of the PP, the analytical crystallization times were compared with those given by a coupled model between heat transfer and crystallization kinetics.

Drag coefficients for modeling flow through emergent vegetation in the Florida Everglades

USGS Publications Warehouse

Lee, J.K.; Roig, L.C.; Jenter, H.L.; Visser, H.M.

2004-01-01

Hydraulic data collected in a flume fitted with pans of sawgrass were analyzed to determine the vertically averaged drag coefficient as a function of vegetation characteristics. The drag coefficient is required for modeling flow through emergent vegetation at low Reynolds numbers in the Florida Everglades. Parameters of the vegetation, such as the stem population per unit bed area and the average stem/leaf width, were measured for five fixed vegetation layers. The vertically averaged vegetation parameters for each experiment were then computed by weighted average over the submerged portion of the vegetation. Only laminar flow through emergent vegetation was considered, because this is the dominant flow regime of the inland Everglades. A functional form for the vegetation drag coefficient was determined by linear regression of the logarithmic transforms of measured resistance force and Reynolds number. The coefficients of the drag coefficient function were then determined for the Everglades, using extensive flow and vegetation measurements taken in the field. The Everglades data show that the stem spacing and the Reynolds number are important parameters for the determination of vegetation drag coefficient. ?? 2004 Elsevier B.V. All rights reserved.

Linear prediction and single-channel recording.

PubMed

Carter, A A; Oswald, R E

1995-08-01

The measurement of individual single-channel events arising from the gating of ion channels provides a detailed data set from which the kinetic mechanism of a channel can be deduced. In many cases, the pattern of dwells in the open and closed states is very complex, and the kinetic mechanism and parameters are not easily determined. Assuming a Markov model for channel kinetics, the probability density function for open and closed time dwells should consist of a sum of decaying exponentials. One method of approaching the kinetic analysis of such a system is to determine the number of exponentials and the corresponding parameters which comprise the open and closed dwell time distributions. These can then be compared to the relaxations predicted from the kinetic model to determine, where possible, the kinetic constants. We report here the use of a linear technique, linear prediction/singular value decomposition, to determine the number of exponentials and the exponential parameters. Using simulated distributions and comparing with standard maximum-likelihood analysis, the singular value decomposition techniques provide advantages in some situations and are a useful adjunct to other single-channel analysis techniques.

A dual-process approach to exploring the role of delay discounting in obesity.

PubMed

Price, Menna; Higgs, Suzanne; Maw, James; Lee, Michelle

2016-08-01

Delay discounting of financial rewards has been related to overeating and obesity. Neuropsychological evidence supports a dual-system account of both discounting and overeating behaviour where the degree of impulsive decision making is determined by the relative strength of reward desire and executive control. A dual-parameter model of discounting behaviour is consistent with this theory. In this study, the fit of the commonly used one-parameter model was compared to a new dual-parameter model for the first time in a sample of adults with wide ranging BMI. Delay discounting data from 79 males and females (males=26) across a wide age (M=28.44years (SD=8.81)) and BMI range (M=25.42 (SD=5.16)) was analysed. A dual-parameter model (saturating-hyperbolic; Doya, [Doya (2008) ]) was applied to the data and compared on model fit indices to the single-parameter model. Discounting was significantly greater in the overweight/obese participants using both models, however, the two parameter model showed a superior fit to data (p<0.0001). The two parameters were shown to be related yet distinct measures consistent with a dual-system account of inter-temporal choice behaviour. The dual-parameter model showed superior fit to data and the two parameters were shown to be related yet distinct indices sensitive to differences between weight groups. Findings are discussed in terms of the impulsive reward and executive control systems that contribute to unhealthy food choice and within the context of obesity related research. Copyright © 2016 Elsevier Inc. All rights reserved.

Maximum entropy approach to statistical inference for an ocean acoustic waveguide.

PubMed

Knobles, D P; Sagers, J D; Koch, R A

2012-02-01

A conditional probability distribution suitable for estimating the statistical properties of ocean seabed parameter values inferred from acoustic measurements is derived from a maximum entropy principle. The specification of the expectation value for an error function constrains the maximization of an entropy functional. This constraint determines the sensitivity factor (β) to the error function of the resulting probability distribution, which is a canonical form that provides a conservative estimate of the uncertainty of the parameter values. From the conditional distribution, marginal distributions for individual parameters can be determined from integration over the other parameters. The approach is an alternative to obtaining the posterior probability distribution without an intermediary determination of the likelihood function followed by an application of Bayes' rule. In this paper the expectation value that specifies the constraint is determined from the values of the error function for the model solutions obtained from a sparse number of data samples. The method is applied to ocean acoustic measurements taken on the New Jersey continental shelf. The marginal probability distribution for the values of the sound speed ratio at the surface of the seabed and the source levels of a towed source are examined for different geoacoustic model representations. © 2012 Acoustical Society of America

Ground water flow modeling with sensitivity analyses to guide field data collection in a mountain watershed

USGS Publications Warehouse

Johnson, Raymond H.

2007-01-01

In mountain watersheds, the increased demand for clean water resources has led to an increased need for an understanding of ground water flow in alpine settings. In Prospect Gulch, located in southwestern Colorado, understanding the ground water flow system is an important first step in addressing metal loads from acid-mine drainage and acid-rock drainage in an area with historical mining. Ground water flow modeling with sensitivity analyses are presented as a general tool to guide future field data collection, which is applicable to any ground water study, including mountain watersheds. For a series of conceptual models, the observation and sensitivity capabilities of MODFLOW-2000 are used to determine composite scaled sensitivities, dimensionless scaled sensitivities, and 1% scaled sensitivity maps of hydraulic head. These sensitivities determine the most important input parameter(s) along with the location of observation data that are most useful for future model calibration. The results are generally independent of the conceptual model and indicate recharge in a high-elevation recharge zone as the most important parameter, followed by the hydraulic conductivities in all layers and recharge in the next lower-elevation zone. The most important observation data in determining these parameters are hydraulic heads at high elevations, with a depth of less than 100 m being adequate. Evaluation of a possible geologic structure with a different hydraulic conductivity than the surrounding bedrock indicates that ground water discharge to individual stream reaches has the potential to identify some of these structures. Results of these sensitivity analyses can be used to prioritize data collection in an effort to reduce time and money spend by collecting the most relevant model calibration data.

Maximum likelihood-based analysis of single-molecule photon arrival trajectories

NASA Astrophysics Data System (ADS)

Hajdziona, Marta; Molski, Andrzej

2011-02-01

In this work we explore the statistical properties of the maximum likelihood-based analysis of one-color photon arrival trajectories. This approach does not involve binning and, therefore, all of the information contained in an observed photon strajectory is used. We study the accuracy and precision of parameter estimates and the efficiency of the Akaike information criterion and the Bayesian information criterion (BIC) in selecting the true kinetic model. We focus on the low excitation regime where photon trajectories can be modeled as realizations of Markov modulated Poisson processes. The number of observed photons is the key parameter in determining model selection and parameter estimation. For example, the BIC can select the true three-state model from competing two-, three-, and four-state kinetic models even for relatively short trajectories made up of 2 × 103 photons. When the intensity levels are well-separated and 104 photons are observed, the two-state model parameters can be estimated with about 10% precision and those for a three-state model with about 20% precision.

Practical limits for reverse engineering of dynamical systems: a statistical analysis of sensitivity and parameter inferability in systems biology models.

PubMed

Erguler, Kamil; Stumpf, Michael P H

2011-05-01

The size and complexity of cellular systems make building predictive models an extremely difficult task. In principle dynamical time-course data can be used to elucidate the structure of the underlying molecular mechanisms, but a central and recurring problem is that many and very different models can be fitted to experimental data, especially when the latter are limited and subject to noise. Even given a model, estimating its parameters remains challenging in real-world systems. Here we present a comprehensive analysis of 180 systems biology models, which allows us to classify the parameters with respect to their contribution to the overall dynamical behaviour of the different systems. Our results reveal candidate elements of control in biochemical pathways that differentially contribute to dynamics. We introduce sensitivity profiles that concisely characterize parameter sensitivity and demonstrate how this can be connected to variability in data. Systematically linking data and model sloppiness allows us to extract features of dynamical systems that determine how well parameters can be estimated from time-course measurements, and associates the extent of data required for parameter inference with the model structure, and also with the global dynamical state of the system. The comprehensive analysis of so many systems biology models reaffirms the inability to estimate precisely most model or kinetic parameters as a generic feature of dynamical systems, and provides safe guidelines for performing better inferences and model predictions in the context of reverse engineering of mathematical models for biological systems.

Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection

PubMed Central

Alam, Maksudul; Deng, Xinwei; Philipson, Casandra; Bassaganya-Riera, Josep; Bisset, Keith; Carbo, Adria; Eubank, Stephen; Hontecillas, Raquel; Hoops, Stefan; Mei, Yongguo; Abedi, Vida; Marathe, Madhav

2015-01-01

Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close “neighborhood” of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa. PMID:26327290

Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection.

PubMed

Alam, Maksudul; Deng, Xinwei; Philipson, Casandra; Bassaganya-Riera, Josep; Bisset, Keith; Carbo, Adria; Eubank, Stephen; Hontecillas, Raquel; Hoops, Stefan; Mei, Yongguo; Abedi, Vida; Marathe, Madhav

2015-01-01

Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close "neighborhood" of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.

Investigating the relationship between a soils classification and the spatial parameters of a conceptual catchment-scale hydrological model

NASA Astrophysics Data System (ADS)

Dunn, S. M.; Lilly, A.

2001-10-01

There are now many examples of hydrological models that utilise the capabilities of Geographic Information Systems to generate spatially distributed predictions of behaviour. However, the spatial variability of hydrological parameters relating to distributions of soils and vegetation can be hard to establish. In this paper, the relationship between a soil hydrological classification Hydrology of Soil Types (HOST) and the spatial parameters of a conceptual catchment-scale model is investigated. A procedure involving inverse modelling using Monte-Carlo simulations on two catchments is developed to identify relative values for soil related parameters of the DIY model. The relative values determine the internal variability of hydrological processes as a function of the soil type. For three out of the four soil parameters studied, the variability between HOST classes was found to be consistent across two catchments when tested independently. Problems in identifying values for the fourth 'fast response distance' parameter have highlighted a potential limitation with the present structure of the model. The present assumption that this parameter can be related simply to soil type rather than topography appears to be inadequate. With the exclusion of this parameter, calibrated parameter sets from one catchment can be converted into equivalent parameter sets for the alternate catchment on the basis of their HOST distributions, to give a reasonable simulation of flow. Following further testing on different catchments, and modifications to the definition of the fast response distance parameter, the technique provides a methodology whereby it is possible to directly derive spatial soil parameters for new catchments.

Incorporation of prior information on parameters into nonlinear regression groundwater flow models: 2. Applications

USGS Publications Warehouse

Cooley, Richard L.

1983-01-01

This paper investigates factors influencing the degree of improvement in estimates of parameters of a nonlinear regression groundwater flow model by incorporating prior information of unknown reliability. Consideration of expected behavior of the regression solutions and results of a hypothetical modeling problem lead to several general conclusions. First, if the parameters are properly scaled, linearized expressions for the mean square error (MSE) in parameter estimates of a nonlinear model will often behave very nearly as if the model were linear. Second, by using prior information, the MSE in properly scaled parameters can be reduced greatly over the MSE of ordinary least squares estimates of parameters. Third, plots of estimated MSE and the estimated standard deviation of MSE versus an auxiliary parameter (the ridge parameter) specifying the degree of influence of the prior information on regression results can help determine the potential for improvement of parameter estimates. Fourth, proposed criteria can be used to make appropriate choices for the ridge parameter and another parameter expressing degree of overall bias in the prior information. Results of a case study of Truckee Meadows, Reno-Sparks area, Washoe County, Nevada, conform closely to the results of the hypothetical problem. In the Truckee Meadows case, incorporation of prior information did not greatly change the parameter estimates from those obtained by ordinary least squares. However, the analysis showed that both sets of estimates are more reliable than suggested by the standard errors from ordinary least squares.

Rational Design of Glucose-Responsive Insulin Using Pharmacokinetic Modeling.

PubMed

Bakh, Naveed A; Bisker, Gili; Lee, Michael A; Gong, Xun; Strano, Michael S

2017-11-01

A glucose responsive insulin (GRI) is a therapeutic that modulates its potency, concentration, or dosing of insulin in relation to a patient's dynamic glucose concentration, thereby approximating aspects of a normally functioning pancreas. Current GRI design lacks a theoretical basis on which to base fundamental design parameters such as glucose reactivity, dissociation constant or potency, and in vivo efficacy. In this work, an approach to mathematically model the relevant parameter space for effective GRIs is induced, and design rules for linking GRI performance to therapeutic benefit are developed. Well-developed pharmacokinetic models of human glucose and insulin metabolism coupled to a kinetic model representation of a freely circulating GRI are used to determine the desired kinetic parameters and dosing for optimal glycemic control. The model examines a subcutaneous dose of GRI with kinetic parameters in an optimal range that results in successful glycemic control within prescribed constraints over a 24 h period. Additionally, it is demonstrated that the modeling approach can find GRI parameters that enable stable glucose levels that persist through a skipped meal. The results provide a framework for exploring the parameter space of GRIs, potentially without extensive, iterative in vivo animal testing. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Dispersion Modeling in Complex Urban Systems

EPA Science Inventory

Models are used to represent real systems in an understandable way. They take many forms. A conceptual model explains the way a system works. In environmental studies, for example, a conceptual model may delineate all the factors and parameters for determining how a particle move...

Recommended direct simulation Monte Carlo collision model parameters for modeling ionized air transport processes

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

Swaminathan-Gopalan, Krishnan; Stephani, Kelly A., E-mail: ksteph@illinois.edu

2016-02-15

A systematic approach for calibrating the direct simulation Monte Carlo (DSMC) collision model parameters to achieve consistency in the transport processes is presented. The DSMC collision cross section model parameters are calibrated for high temperature atmospheric conditions by matching the collision integrals from DSMC against ab initio based collision integrals that are currently employed in the Langley Aerothermodynamic Upwind Relaxation Algorithm (LAURA) and Data Parallel Line Relaxation (DPLR) high temperature computational fluid dynamics solvers. The DSMC parameter values are computed for the widely used Variable Hard Sphere (VHS) and the Variable Soft Sphere (VSS) models using the collision-specific pairing approach.more » The recommended best-fit VHS/VSS parameter values are provided over a temperature range of 1000-20 000 K for a thirteen-species ionized air mixture. Use of the VSS model is necessary to achieve consistency in transport processes of ionized gases. The agreement of the VSS model transport properties with the transport properties as determined by the ab initio collision integral fits was found to be within 6% in the entire temperature range, regardless of the composition of the mixture. The recommended model parameter values can be readily applied to any gas mixture involving binary collisional interactions between the chemical species presented for the specified temperature range.« less

Predictive control of thermal state of blast furnace

NASA Astrophysics Data System (ADS)

Barbasova, T. A.; Filimonova, A. A.

2018-05-01

The work describes the structure of the model for predictive control of the thermal state of a blast furnace. The proposed model contains the following input parameters: coke rate; theoretical combustion temperature, comprising: natural gas consumption, blasting temperature, humidity, oxygen, blast furnace cooling water; blast furnace gas utilization rate. The output parameter is the cast iron temperature. The results for determining the cast iron temperature were obtained following the identification using the Hammerstein-Wiener model. The result of solving the cast iron temperature stabilization problem was provided for the calculated values of process parameters of the target area of the respective blast furnace operation mode.

Modeling of natural acoustic frequencies of a gas-turbine plant combustion chamber

NASA Astrophysics Data System (ADS)

Zubrilin, I. A.; Gurakov, N. I.; Zubrilin, R. A.; Matveev, S. G.

2017-05-01

The paper presents results of determination of natural acoustic frequencies of a gas-turbine plant annular combustion chamber model using 3D-simulation. At the beginning, a calculation procedure for determining natural acoustic frequencies of the gas-turbine plant combustion chamber was worked out. The effect of spatial inhomogeneity of the flow parameters (fluid composition, pressure, temperature) arising in combustion and some geometrical parameters (cooling holes of the flame tube walls) on the calculation results is studied. It is found that the change of the fluid composition in combustion affects the acoustic velocity not more than 5%; therefore, the air with a volume variable temperature can be taken as a working fluid in the calculation of natural acoustic frequencies. It is also shown that the cooling holes of the flame tube walls with diameter less than 2 mm can be neglected in the determination of the acoustic modes in the frequency range of up to 1000 Hz. This reduces the number of the grid-model elements by a factor of six in comparison with a model that considers all of the holes. Furthermore, a method of export of spatial inhomogeneity of the flow parameters from a CFD solver sector model to the annular combustion chamber model in a modal solver is presented. As a result of the obtained model calculation, acoustic modes of the combustion chamber in the frequency range of up to 1000 Hz are determined. For a standard engine condition, a potentially dangerous acoustic mode with a frequency close to the ripple frequency of the precessing vortex core, which is formed behind the burner device of this combustion chamber, is detected.

Determination of heat capacity of ionic liquid based nanofluids using group method of data handling technique

NASA Astrophysics Data System (ADS)

Sadi, Maryam

2018-01-01

In this study a group method of data handling model has been successfully developed to predict heat capacity of ionic liquid based nanofluids by considering reduced temperature, acentric factor and molecular weight of ionic liquids, and nanoparticle concentration as input parameters. In order to accomplish modeling, 528 experimental data points extracted from the literature have been divided into training and testing subsets. The training set has been used to predict model coefficients and the testing set has been applied for model validation. The ability and accuracy of developed model, has been evaluated by comparison of model predictions with experimental values using different statistical parameters such as coefficient of determination, mean square error and mean absolute percentage error. The mean absolute percentage error of developed model for training and testing sets are 1.38% and 1.66%, respectively, which indicate excellent agreement between model predictions and experimental data. Also, the results estimated by the developed GMDH model exhibit a higher accuracy when compared to the available theoretical correlations.

Photospheric properties and fundamental parameters of M dwarfs

NASA Astrophysics Data System (ADS)

Rajpurohit, A. S.; Allard, F.; Teixeira, G. D. C.; Homeier, D.; Rajpurohit, S.; Mousis, O.

2018-02-01

Context. M dwarfs are an important source of information when studying and probing the lower end of the Hertzsprung-Russell (HR) diagram, down to the hydrogen-burning limit. Being the most numerous and oldest stars in the galaxy, they carry fundamental information on its chemical history. The presence of molecules in their atmospheres, along with various condensed species, complicates our understanding of their physical properties and thus makes the determination of their fundamental stellar parameters more challenging and difficult. Aim. The aim of this study is to perform a detailed spectroscopic analysis of the high-resolution H-band spectra of M dwarfs in order to determine their fundamental stellar parameters and to validate atmospheric models. The present study will also help us to understand various processes, including dust formation and depletion of metals onto dust grains in M dwarf atmospheres. The high spectral resolution also provides a unique opportunity to constrain other chemical and physical processes that occur in a cool atmosphere. Methods: The high-resolution APOGEE spectra of M dwarfs, covering the entire H-band, provide a unique opportunity to measure their fundamental parameters. We have performed a detailed spectral synthesis by comparing these high-resolution H-band spectra to that of the most recent BT-Settl model and have obtained fundamental parameters such as effective temperature, surface gravity, and metallicity (Teff, log g, and [Fe/H]), respectively. Results: We have determined Teff, log g, and [Fe/H] for 45 M dwarfs using high-resolution H-band spectra. The derived Teff for the sample ranges from 3100 to 3900 K, values of log g lie in the range 4.5 ≤ log g ≤ 5.5, and the resulting metallicities lie in the range ‑0.5 ≤ [Fe/H] ≤ +0.5. We have explored systematic differences between effective temperature and metallicity calibrations with other studies using the same sample of M dwarfs. We have also shown that the stellar parameters determined using the BT-Settl model are more accurate and reliable compared to other comparative studies using alternative models.

Stability margin of linear systems with parameters described by fuzzy numbers.

PubMed

Husek, Petr

2011-10-01

This paper deals with the linear systems with uncertain parameters described by fuzzy numbers. The problem of determining the stability margin of those systems with linear affine dependence of the coefficients of a characteristic polynomial on system parameters is studied. Fuzzy numbers describing the system parameters are allowed to be characterized by arbitrary nonsymmetric membership functions. An elegant solution, graphical in nature, based on generalization of the Tsypkin-Polyak plot is presented. The advantage of the presented approach over the classical robust concept is demonstrated on a control of the Fiat Dedra engine model and a control of the quarter car suspension model.

Systematic parameter inference in stochastic mesoscopic modeling

NASA Astrophysics Data System (ADS)

Lei, Huan; Yang, Xiu; Li, Zhen; Karniadakis, George Em

2017-02-01

We propose a method to efficiently determine the optimal coarse-grained force field in mesoscopic stochastic simulations of Newtonian fluid and polymer melt systems modeled by dissipative particle dynamics (DPD) and energy conserving dissipative particle dynamics (eDPD). The response surfaces of various target properties (viscosity, diffusivity, pressure, etc.) with respect to model parameters are constructed based on the generalized polynomial chaos (gPC) expansion using simulation results on sampling points (e.g., individual parameter sets). To alleviate the computational cost to evaluate the target properties, we employ the compressive sensing method to compute the coefficients of the dominant gPC terms given the prior knowledge that the coefficients are "sparse". The proposed method shows comparable accuracy with the standard probabilistic collocation method (PCM) while it imposes a much weaker restriction on the number of the simulation samples especially for systems with high dimensional parametric space. Fully access to the response surfaces within the confidence range enables us to infer the optimal force parameters given the desirable values of target properties at the macroscopic scale. Moreover, it enables us to investigate the intrinsic relationship between the model parameters, identify possible degeneracies in the parameter space, and optimize the model by eliminating model redundancies. The proposed method provides an efficient alternative approach for constructing mesoscopic models by inferring model parameters to recover target properties of the physics systems (e.g., from experimental measurements), where those force field parameters and formulation cannot be derived from the microscopic level in a straight forward way.

Identification of Trends into Dose Calculations for Astronauts through Performing Sensitivity Analysis on Calculational Models Used by the Radiation Health Office

NASA Technical Reports Server (NTRS)

Adams, Thomas; VanBaalen, Mary

2009-01-01

The Radiation Health Office (RHO) determines each astronaut s cancer risk by using models to associate the amount of radiation dose that astronauts receive from spaceflight missions. The baryon transport codes (BRYNTRN), high charge (Z) and energy transport codes (HZETRN), and computer risk models are used to determine the effective dose received by astronauts in Low Earth orbit (LEO). This code uses an approximation of the Boltzman transport formula. The purpose of the project is to run this code for various International Space Station (ISS) flight parameters in order to gain a better understanding of how this code responds to different scenarios. The project will determine how variations in one set of parameters such as, the point of the solar cycle and altitude can affect the radiation exposure of astronauts during ISS missions. This project will benefit NASA by improving mission dosimetry.

Regionalization of response routine parameters

NASA Astrophysics Data System (ADS)

Tøfte, Lena S.; Sultan, Yisak A.

2013-04-01

When area distributed hydrological models are to be calibrated or updated, fewer calibration parameters is of a considerable advantage. Based on, among others, Kirchner, we have developed a simple non-threshold response model for drainage in natural catchments, to be used in the gridded hydrological model ENKI. The new response model takes only the hydrogram into account, it has one state and two parameters, and is adapted to catchments that are dominated by terrain drainage. The method is based on the assumption that in catchments where precipitation, evaporation and snowmelt is neglect able, the discharge is entirely determined by the amount of stored water. It can then be characterized as a simple first-order nonlinear dynamical system, where the governing equations can be found directly from measured stream flow fluctuations. This means that the response in the catchment can be modelled by using hydrogram data where all data from periods with rain, snowmelt or evaporation is left out, and adjust these series to a two or three parameter equation. A large number of discharge series from catchments in different regions in Norway are analyzed, and parameters found for all the series. By combining the computed parameters and known catchments characteristics, we try to regionalize the parameters. Then the parameters in the response routine can easily be found also for ungauged catchments, from maps or data bases.

AAA gunnermodel based on observer theory. [predicting a gunner's tracking response

NASA Technical Reports Server (NTRS)

Kou, R. S.; Glass, B. C.; Day, C. N.; Vikmanis, M. M.

1978-01-01

The Luenberger observer theory is used to develop a predictive model of a gunner's tracking response in antiaircraft artillery systems. This model is composed of an observer, a feedback controller and a remnant element. An important feature of the model is that the structure is simple, hence a computer simulation requires only a short execution time. A parameter identification program based on the least squares curve fitting method and the Gauss Newton gradient algorithm is developed to determine the parameter values of the gunner model. Thus, a systematic procedure exists for identifying model parameters for a given antiaircraft tracking task. Model predictions of tracking errors are compared with human tracking data obtained from manned simulation experiments. Model predictions are in excellent agreement with the empirical data for several flyby and maneuvering target trajectories.

Comparison of methods applied in photoinduced transient spectroscopy to determining the defect center parameters: The correlation procedure and the signal analysis based on inverse Laplace transformation

NASA Astrophysics Data System (ADS)

Suproniuk, M.; Pawłowski, M.; Wierzbowski, M.; Majda-Zdancewicz, E.; Pawłowski, Ma.

2018-04-01

The procedure for determination of trap parameters by photo-induced transient spectroscopy is based on the Arrhenius plot that illustrates a thermal dependence of the emission rate. In this paper, we show that the Arrhenius plot obtained by the correlation method is shifted toward lower temperatures as compared to the one obtained with the inverse Laplace transformation. This shift is caused by the model adequacy error of the correlation method and introduces errors to a calculation procedure of defect center parameters. The effect is exemplified by comparing the results of the determination of trap parameters with both methods based on photocurrent transients for defect centers observed in tin-doped neutron-irradiated silicon crystals and in gallium arsenide grown with the Vertical Gradient Freeze method.

A "total parameter estimation" method in the varification of distributed hydrological models

NASA Astrophysics Data System (ADS)

Wang, M.; Qin, D.; Wang, H.

2011-12-01

Conventionally hydrological models are used for runoff or flood forecasting, hence the determination of model parameters are common estimated based on discharge measurements at the catchment outlets. With the advancement in hydrological sciences and computer technology, distributed hydrological models based on the physical mechanism such as SWAT, MIKESHE, and WEP, have gradually become the mainstream models in hydrology sciences. However, the assessments of distributed hydrological models and model parameter determination still rely on runoff and occasionally, groundwater level measurements. It is essential in many countries, including China, to understand the local and regional water cycle: not only do we need to simulate the runoff generation process and for flood forecasting in wet areas, we also need to grasp the water cycle pathways and consumption process of transformation in arid and semi-arid regions for the conservation and integrated water resources management. As distributed hydrological model can simulate physical processes within a catchment, we can get a more realistic representation of the actual water cycle within the simulation model. Runoff is the combined result of various hydrological processes, using runoff for parameter estimation alone is inherits problematic and difficult to assess the accuracy. In particular, in the arid areas, such as the Haihe River Basin in China, runoff accounted for only 17% of the rainfall, and very concentrated during the rainy season from June to August each year. During other months, many of the perennial rivers within the river basin dry up. Thus using single runoff simulation does not fully utilize the distributed hydrological model in arid and semi-arid regions. This paper proposed a "total parameter estimation" method to verify the distributed hydrological models within various water cycle processes, including runoff, evapotranspiration, groundwater, and soil water; and apply it to the Haihe river basin in China. The application results demonstrate that this comprehensive testing method is very useful in the development of a distributed hydrological model and it provides a new way of thinking in hydrological sciences.

Parameterization guidelines and considerations for hydrologic models

Treesearch

 R. W. Malone; G. Yagow; C. Baffaut; M.W  Gitau; Z. Qi; Devendra Amatya; P.B.   Parajuli; J.V. Bonta; T.R.  Green

2015-01-01

 Imparting knowledge of the physical processes of a system to a model and determining a set of parameter values for a hydrologic or water quality model application (i.e., parameterization) are important and difficult tasks. An exponential...

Multiscale Modeling of Grain Boundaries in ZrB2: Structure, Energetics, and Thermal Resistance

NASA Technical Reports Server (NTRS)

Lawson, John W.; Daw, Murray S.; Squire, Thomas H.; Bauschlicher, Charles W., Jr.

2012-01-01

A combination of ab initio, atomistic and finite element methods (FEM) were used to investigate the structures, energetics and lattice thermal conductance of grain boundaries for the ultra high temperature ceramic ZrB2. Atomic models of idealized boundaries were relaxed using density functional theory. Information about bonding across the interfaces was determined from the electron localization function. The Kapitza conductance of larger scale versions of the boundary models were computed using non-equilibrium molecular dynamics. The interfacial thermal parameters together with single crystal thermal conductivities were used as parameters in microstructural computations. FEM meshes were constructed on top of microstructural images. From these computations, the effective thermal conductivity of the polycrystalline structure was determined.

Enhanced orbit determination filter: Inclusion of ground system errors as filter parameters

NASA Technical Reports Server (NTRS)

Masters, W. C.; Scheeres, D. J.; Thurman, S. W.

1994-01-01

The theoretical aspects of an orbit determination filter that incorporates ground-system error sources as model parameters for use in interplanetary navigation are presented in this article. This filter, which is derived from sequential filtering theory, allows a systematic treatment of errors in calibrations of transmission media, station locations, and earth orientation models associated with ground-based radio metric data, in addition to the modeling of the spacecraft dynamics. The discussion includes a mathematical description of the filter and an analytical comparison of its characteristics with more traditional filtering techniques used in this application. The analysis in this article shows that this filter has the potential to generate navigation products of substantially greater accuracy than more traditional filtering procedures.

Chemical short-range order and lattice deformations in MgyTi1-yHx thin films probed by hydrogenography

NASA Astrophysics Data System (ADS)

Gremaud, R.; Baldi, A.; Gonzalez-Silveira, M.; Dam, B.; Griessen, R.

2008-04-01

A multisite lattice gas approach is used to model pressure-optical-transmission isotherms (PTIs) recorded by hydrogenography on MgyTi1-yHx sputtered thin films. The model reproduces the measured PTIs well and allows us to determine the chemical short-range order parameter s . The s values are in good agreement with those determined from extended x-ray absorption fine structure measurements. Additionally, the PTI multisite modeling yields a parameter L that accounts for the local lattice deformations with respect to the average MgyTi1-y lattice given by Vegard’s law. It is thus possible to extract two essential characteristics of a metastable alloy from hydrogenographic data.

Black hole algorithm for determining model parameter in self-potential data

NASA Astrophysics Data System (ADS)

Sungkono; Warnana, Dwa Desa

2018-01-01

Analysis of self-potential (SP) data is increasingly popular in geophysical method due to its relevance in many cases. However, the inversion of SP data is often highly nonlinear. Consequently, local search algorithms commonly based on gradient approaches have often failed to find the global optimum solution in nonlinear problems. Black hole algorithm (BHA) was proposed as a solution to such problems. As the name suggests, the algorithm was constructed based on the black hole phenomena. This paper investigates the application of BHA to solve inversions of field and synthetic self-potential (SP) data. The inversion results show that BHA accurately determines model parameters and model uncertainty. This indicates that BHA is highly potential as an innovative approach for SP data inversion.

Reactive flow model development for PBXW-126 using modern nonlinear optimization methods

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

Murphy, M.J.; Simpson, R.L.; Urtiew, P.A.

1996-05-01

The initiation and detonation behavior of PBXW-126 has been characterized and is described. PBXW-126 is a composite explosive consisting of approximately equal amounts of RDX, AP, AL, and NTO with a polyurethane binder. The three term ignition and growth of reaction model parameters (ignition+two growth terms) have been found using nonlinear optimization methods to determine the {open_quotes}best{close_quotes} set of model parameters. The ignition term treats the initiation of up to 0.5{percent} of the RDX. The first growth term in the model treats the RDX growth of reaction up to 20{percent} reacted. The second growth term treats the subsequent growth ofmore » reaction of the remaining AP/AL/NTO. The unreacted equation of state (EOS) was determined from the wave profiles of embedded gauge tests while the JWL product EOS was determined from cylinder expansion test results. The nonlinear optimization code, NLQPEB/GLO, was used to determine the {open_quotes}best{close_quotes} set of coefficients for the three term Lee-Tarver ignition and growth of reaction model. {copyright} {ital 1996 American Institute of Physics.}« less

Model parameter learning using Kullback-Leibler divergence

NASA Astrophysics Data System (ADS)

Lin, Chungwei; Marks, Tim K.; Pajovic, Milutin; Watanabe, Shinji; Tung, Chih-kuan

2018-02-01

In this paper, we address the following problem: For a given set of spin configurations whose probability distribution is of the Boltzmann type, how do we determine the model coupling parameters? We demonstrate that directly minimizing the Kullback-Leibler divergence is an efficient method. We test this method against the Ising and XY models on the one-dimensional (1D) and two-dimensional (2D) lattices, and provide two estimators to quantify the model quality. We apply this method to two types of problems. First, we apply it to the real-space renormalization group (RG). We find that the obtained RG flow is sufficiently good for determining the phase boundary (within 1% of the exact result) and the critical point, but not accurate enough for critical exponents. The proposed method provides a simple way to numerically estimate amplitudes of the interactions typically truncated in the real-space RG procedure. Second, we apply this method to the dynamical system composed of self-propelled particles, where we extract the parameter of a statistical model (a generalized XY model) from a dynamical system described by the Viscek model. We are able to obtain reasonable coupling values corresponding to different noise strengths of the Viscek model. Our method is thus able to provide quantitative analysis of dynamical systems composed of self-propelled particles.

Parametric diagnosis of the adaptive gas path in the automatic control system of the aircraft engine

NASA Astrophysics Data System (ADS)

Kuznetsova, T. A.

2017-01-01

The paper dwells on the adaptive multimode mathematical model of the gas-turbine aircraft engine (GTE) embedded in the automatic control system (ACS). The mathematical model is based on the throttle performances, and is characterized by high accuracy of engine parameters identification in stationary and dynamic modes. The proposed on-board engine model is the state space linearized low-level simulation. The engine health is identified by the influence of the coefficient matrix. The influence coefficient is determined by the GTE high-level mathematical model based on measurements of gas-dynamic parameters. In the automatic control algorithm, the sum of squares of the deviation between the parameters of the mathematical model and real GTE is minimized. The proposed mathematical model is effectively used for gas path defects detecting in on-line GTE health monitoring. The accuracy of the on-board mathematical model embedded in ACS determines the quality of adaptive control and reliability of the engine. To improve the accuracy of identification solutions and sustainability provision, the numerical method of Monte Carlo was used. The parametric diagnostic algorithm based on the LPτ - sequence was developed and tested. Analysis of the results suggests that the application of the developed algorithms allows achieving higher identification accuracy and reliability than similar models used in practice.

Analysis of multidimensional difference-of-Gaussians filters in terms of directly observable parameters.

PubMed

Cope, Davis; Blakeslee, Barbara; McCourt, Mark E

2013-05-01

The difference-of-Gaussians (DOG) filter is a widely used model for the receptive field of neurons in the retina and lateral geniculate nucleus (LGN) and is a potential model in general for responses modulated by an excitatory center with an inhibitory surrounding region. A DOG filter is defined by three standard parameters: the center and surround sigmas (which define the variance of the radially symmetric Gaussians) and the balance (which defines the linear combination of the two Gaussians). These parameters are not directly observable and are typically determined by nonlinear parameter estimation methods applied to the frequency response function. DOG filters show both low-pass (optimal response at zero frequency) and bandpass (optimal response at a nonzero frequency) behavior. This paper reformulates the DOG filter in terms of a directly observable parameter, the zero-crossing radius, and two new (but not directly observable) parameters. In the two-dimensional parameter space, the exact region corresponding to bandpass behavior is determined. A detailed description of the frequency response characteristics of the DOG filter is obtained. It is also found that the directly observable optimal frequency and optimal gain (the ratio of the response at optimal frequency to the response at zero frequency) provide an alternate coordinate system for the bandpass region. Altogether, the DOG filter and its three standard implicit parameters can be determined by three directly observable values. The two-dimensional bandpass region is a potential tool for the analysis of populations of DOG filters (for example, populations of neurons in the retina or LGN), because the clustering of points in this parameter space may indicate an underlying organizational principle. This paper concentrates on circular Gaussians, but the results generalize to multidimensional radially symmetric Gaussians and are given as an appendix.

Optimization of GATE and PHITS Monte Carlo code parameters for uniform scanning proton beam based on simulation with FLUKA general-purpose code

NASA Astrophysics Data System (ADS)

Kurosu, Keita; Takashina, Masaaki; Koizumi, Masahiko; Das, Indra J.; Moskvin, Vadim P.

2014-10-01

Although three general-purpose Monte Carlo (MC) simulation tools: Geant4, FLUKA and PHITS have been used extensively, differences in calculation results have been reported. The major causes are the implementation of the physical model, preset value of the ionization potential or definition of the maximum step size. In order to achieve artifact free MC simulation, an optimized parameters list for each simulation system is required. Several authors have already proposed the optimized lists, but those studies were performed with a simple system such as only a water phantom. Since particle beams have a transport, interaction and electromagnetic processes during beam delivery, establishment of an optimized parameters-list for whole beam delivery system is therefore of major importance. The purpose of this study was to determine the optimized parameters list for GATE and PHITS using proton treatment nozzle computational model. The simulation was performed with the broad scanning proton beam. The influences of the customizing parameters on the percentage depth dose (PDD) profile and the proton range were investigated by comparison with the result of FLUKA, and then the optimal parameters were determined. The PDD profile and the proton range obtained from our optimized parameters list showed different characteristics from the results obtained with simple system. This led to the conclusion that the physical model, particle transport mechanics and different geometry-based descriptions need accurate customization in planning computational experiments for artifact-free MC simulation.

Performance Model and Sensitivity Analysis for a Solar Thermoelectric Generator

NASA Astrophysics Data System (ADS)

Rehman, Naveed Ur; Siddiqui, Mubashir Ali

2017-03-01

In this paper, a regression model for evaluating the performance of solar concentrated thermoelectric generators (SCTEGs) is established and the significance of contributing parameters is discussed in detail. The model is based on several natural, design and operational parameters of the system, including the thermoelectric generator (TEG) module and its intrinsic material properties, the connected electrical load, concentrator attributes, heat transfer coefficients, solar flux, and ambient temperature. The model is developed by fitting a response curve, using the least-squares method, to the results. The sample points for the model were obtained by simulating a thermodynamic model, also developed in this paper, over a range of values of input variables. These samples were generated employing the Latin hypercube sampling (LHS) technique using a realistic distribution of parameters. The coefficient of determination was found to be 99.2%. The proposed model is validated by comparing the predicted results with those in the published literature. In addition, based on the elasticity for parameters in the model, sensitivity analysis was performed and the effects of parameters on the performance of SCTEGs are discussed in detail. This research will contribute to the design and performance evaluation of any SCTEG system for a variety of applications.

An investigation of the thermoviscoplastic behavior of a metal matrix composite at elevated temperatures

NASA Technical Reports Server (NTRS)

Rogacki, John R.; Tuttle, Mark E.

1992-01-01

This research investigates the response of a fiberless 13 layer hot isostatically pressed Ti-15-3 laminate to creep, constant strain rate, and cyclic constant strain rate loading at temperatures ranging from 482C to 649C. Creep stresses from 48 to 260 MPa and strain rates of .0001 to .01 m/m/sec were used. Material parameters for three unified constitutive models (Bodner-Partom, Miller, and Walker models) were determined for Ti-15-3 from the experimental data. Each of the three models was subsequently incorporated into a rule of mixtures and evaluated for accuracy and ease of use in predicting the thermoviscoplastic response of unidirectional metal matrix composite laminates (both 0 and 90). The laminates were comprised of a Ti-15-3 matrix with 29 volume percent SCS6 fibers. The predicted values were compared to experimentally determined creep and constant strain rate data. It was found that all three models predicted the viscoplastic response of the 0 specimens reasonably well, but seriously underestimated the viscoplastic response of the 90 specimens. It is believed that this discrepancy is due to compliant and/or weak fiber-matrix interphase. In general, it was found that of the three models studied, the Bodner-Partom model was easiest to implement, primarily because this model does not require the use of cyclic constant strain rate tests to determine the material parameters involved. However, the version of the Bodner-Partom model used in this study does not include back stress as an internal state variable, and hence may not be suitable for use with materials which exhibit a pronounced Baushinger effect. The back stress is accounted for in both the Walker and Miller models; determination of the material parameters associated with the Walker model was somewhat easier than in the Miller model.

Sideways fall-induced impact force and its effect on hip fracture risk: a review.

PubMed

Nasiri Sarvi, M; Luo, Y

2017-10-01

Osteoporotic hip fracture, mostly induced in falls among the elderly, is a major health burden over the world. The impact force applied to the hip is an important factor in determining the risk of hip fracture. However, biomechanical researches have yielded conflicting conclusions about whether the fall-induced impact force can be accurately predicted by the available models. It also has been debated whether or not the effect of impact force has been considered appropriately in hip fracture risk assessment tools. This study aimed to provide a state-of-the-art review of the available methods for predicting the impact force, investigate their strengths/limitations, and suggest further improvements in modeling of human body falling. We divided the effective parameters on impact force to two categories: (1) the parameters that can be determined subject-specifically and (2) the parameters that may significantly vary from fall to fall for an individual and cannot be considered subject-specifically. The parameters in the first category can be investigated in human body fall experiments. Video capture of real-life falls was reported as a valuable method to investigate the parameters in the second category that significantly affect the impact force and cannot be determined in human body fall experiments. The analysis of the gathered data revealed that there is a need to develop modified biomechanical models for more accurate prediction of the impact force and appropriately adopt them in hip fracture risk assessment tools in order to achieve a better precision in identifying high-risk patients. Graphical abstract Impact force to the hip induced in sideways falls is affected by many parameters and may remarkably vary from subject to subject.

Z/sub n/ Baxter model: symmetries and the Belavin parametrization

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

Richey, M.P.; Tracy, C.A.

1986-02-01

The Z/sub n/ Baxter model is an exactly solvable lattice model in the special case of the Belavin parametrization. For this parametrization the authors calculate the partition function in an antiferromagnetic region and the order parameter in a ferromagnetic region. They find that the order parameter is expressible in terms of a modular function of level n which for n=2 is the Onsager-Yang-Baxter result. In addition they determine the symmetry group of the finite lattice partition function for the general Z/sub n/ Baxter model.

A Model for the Vortex Pair Associated with a Jet in a Cross Flow

NASA Technical Reports Server (NTRS)

Sellers, William L.

1975-01-01

A model is presented for the contrarotating vortex pair that is formed by a round, turbulent, subsonic jet directed normally into a uniform, subsonic cross flow. The model consists of a set of algebraic equations that describe the properties of the vortex pair as a function of their location in the jet plume. The parameters of the model are physical characteristics of the vortices such as the vortex strength, spacing, and core size. These parameters are determined by velocity measurements at selective points in the jet plume.

A novel compact model for on-chip stacked transformers in RF-CMOS technology

NASA Astrophysics Data System (ADS)

Jun, Liu; Jincai, Wen; Qian, Zhao; Lingling, Sun

2013-08-01

A novel compact model for on-chip stacked transformers is presented. The proposed model topology gives a clear distinction to the eddy current, resistive and capacitive losses of the primary and secondary coils in the substrate. A method to analytically determine the non-ideal parasitics between the primary coil and substrate is provided. The model is further verified by the excellent match between the measured and simulated S -parameters on the extracted parameters for a 1 : 1 stacked transformer manufactured in a commercial RF-CMOS technology.

Hydrogen maser frequency standard computer model for automatic cavity tuning servo simulations

NASA Technical Reports Server (NTRS)

Potter, P. D.; Finnie, C.

1978-01-01

A computer model of the JPL hydrogen maser frequency standard was developed. This model allows frequency stability data to be generated, as a function of various maser parameters, many orders of magnitude faster than these data can be obtained by experimental test. In particular, the maser performance as a function of the various automatic tuning servo parameters may be readily determined. Areas of discussion include noise sources, first-order autotuner loop, second-order autotuner loop, and a comparison of the loops.

Gravitational orientation of the orbital complex, Salyut-6--Soyuz

NASA Technical Reports Server (NTRS)

Grecho, G. M.; Sarychev, V. A.; Legostayev, V. P.; Sazonov, V. V.; Gansvind, I. N.

1983-01-01

A simple mathematical model is proposed for the Salyut-6-Soyuz orbital complex motion with respect to the center of mass under the one-axis gravity-gradient orientation regime. This model was used for processing the measurements of the orbital complex motion parameters when the above orientation region was implemented. Some actual satellite motions are simulated and the satellite's aerodynamic parameters are determined. Estimates are obtained for the accuracy of measurements as well as that of the mathematical model.

Intelligent Modeling Combining Adaptive Neuro Fuzzy Inference System and Genetic Algorithm for Optimizing Welding Process Parameters

NASA Astrophysics Data System (ADS)

Gowtham, K. N.; Vasudevan, M.; Maduraimuthu, V.; Jayakumar, T.

2011-04-01

Modified 9Cr-1Mo ferritic steel is used as a structural material for steam generator components of power plants. Generally, tungsten inert gas (TIG) welding is preferred for welding of these steels in which the depth of penetration achievable during autogenous welding is limited. Therefore, activated flux TIG (A-TIG) welding, a novel welding technique, has been developed in-house to increase the depth of penetration. In modified 9Cr-1Mo steel joints produced by the A-TIG welding process, weld bead width, depth of penetration, and heat-affected zone (HAZ) width play an important role in determining the mechanical properties as well as the performance of the weld joints during service. To obtain the desired weld bead geometry and HAZ width, it becomes important to set the welding process parameters. In this work, adaptative neuro fuzzy inference system is used to develop independent models correlating the welding process parameters like current, voltage, and torch speed with weld bead shape parameters like depth of penetration, bead width, and HAZ width. Then a genetic algorithm is employed to determine the optimum A-TIG welding process parameters to obtain the desired weld bead shape parameters and HAZ width.

Determination of effective thoracic mass.

DOT National Transportation Integrated Search

1996-02-01

Effective thoracic mass is a critical parameter in specifying mathematical and mechanical models (such as crash dummies) of humans exposed to impact conditions. A method is developed using a numerical optimizer to determine effective thoracic mass (a...

A Formal Approach to Empirical Dynamic Model Optimization and Validation

NASA Technical Reports Server (NTRS)

Crespo, Luis G; Morelli, Eugene A.; Kenny, Sean P.; Giesy, Daniel P.

2014-01-01

A framework was developed for the optimization and validation of empirical dynamic models subject to an arbitrary set of validation criteria. The validation requirements imposed upon the model, which may involve several sets of input-output data and arbitrary specifications in time and frequency domains, are used to determine if model predictions are within admissible error limits. The parameters of the empirical model are estimated by finding the parameter realization for which the smallest of the margins of requirement compliance is as large as possible. The uncertainty in the value of this estimate is characterized by studying the set of model parameters yielding predictions that comply with all the requirements. Strategies are presented for bounding this set, studying its dependence on admissible prediction error set by the analyst, and evaluating the sensitivity of the model predictions to parameter variations. This information is instrumental in characterizing uncertainty models used for evaluating the dynamic model at operating conditions differing from those used for its identification and validation. A practical example based on the short period dynamics of the F-16 is used for illustration.

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.

Investigation of stability in a two-delay model of the ultradian oscillations in glucose-insulin regulation

NASA Astrophysics Data System (ADS)

Huard, B.; Easton, J. F.; Angelova, M.

2015-09-01

In this paper, a two-delay model for the ultradian oscillatory behaviour of the glucose-insulin regulation system is studied. Hill functions are introduced to model nonlinear physiological interactions within this system and ranges on parameters reproducing biological oscillations are determined on the basis of analytical and numerical considerations. Local and global stability are investigated and delay-dependent conditions are obtained through the construction of Lyapunov-Krasovskii functionals. The effect of Hill parameters on these conditions, as well as the boundary of the stability region in the delay domain, are established for the first time. Numerical simulations demonstrate that the model with Hill functions represents well the oscillatory behaviour of the system with the advantage of incorporating new meaningful parameters. The influence of the time delays on the period of oscillations and the sensitivity of the latter to model parameters, in particular glucose infusion, are investigated. The model can contribute to the better understanding and treatment of diabetes.

Determination of Global Stability of the Slosh Motion in a Spacecraft via Num Erical Experiment

NASA Astrophysics Data System (ADS)

Kang, Ja-Young

2003-12-01

The global stability of the attitude motion of a spin-stabilized space vehicle is investigated by performing numerical experiment. In the previous study, a stationary solution and a particular resonant condition for a given model were found by using analytical method but failed to represent the system stability over parameter values near and off the stationary points. Accordingly, as an extension of the previous work, this study performs numerical experiment to investigate the stability of the system across the parameter space and determines stable and unstable regions of the design parameters of the system.

Heart rate variability as determinism with jump stochastic parameters.

PubMed

Zheng, Jiongxuan; Skufca, Joseph D; Bollt, Erik M

2013-08-01

We use measured heart rate information (RR intervals) to develop a one-dimensional nonlinear map that describes short term deterministic behavior in the data. Our study suggests that there is a stochastic parameter with persistence which causes the heart rate and rhythm system to wander about a bifurcation point. We propose a modified circle map with a jump process noise term as a model which can qualitatively capture such this behavior of low dimensional transient determinism with occasional (stochastically defined) jumps from one deterministic system to another within a one parameter family of deterministic systems.

Light radiation pressure upon an optically orthotropic surface

NASA Astrophysics Data System (ADS)

Nerovny, Nikolay A.; Lapina, Irina E.; Grigorjev, Anton S.

2017-11-01

In this paper, we discuss the problem of determination of light radiation pressure force upon an anisotropic surface. The optical parameters of such a surface are considered to have major and minor axes, so the model is called an orthotropic model. We derive the equations for force components from emission, absorption, and reflection, utilizing a modified Maxwell's specular-diffuse model. The proposed model can be used to model a flat solar sail with wrinkles. By performing Bayesian analysis for example of a wrinkled surface, we show that there are cases in which an orthotropic model of the optical parameters of a surface may be more accurate than an isotropic model.

Revealing the physical insight of a length-scale parameter in metamaterials by exploiting the variational formulation

NASA Astrophysics Data System (ADS)

Abali, B. Emek

2018-04-01

For micro-architectured materials with a substructure, called metamaterials, we can realize a direct numerical simulation in the microscale by using classical mechanics. This method is accurate, however, computationally costly. Instead, a solution of the same problem in the macroscale is possible by means of the generalized mechanics. In this case, no detailed modeling of the substructure is necessary; however, new parameters emerge. A physical interpretation of these metamaterial parameters is challenging leading to a lack of experimental strategies for their determination. In this work, we exploit the variational formulation based on action principles and obtain a direct relation between a parameter used in the kinetic energy and a metamaterial parameter in the case of a viscoelastic model.

Temperature and velocity conditions of air flow in vertical channel of hinged ventilated facade of a multistory building.

NASA Astrophysics Data System (ADS)

Statsenko, Elena; Ostrovaia, Anastasia; Pigurin, Andrey

2018-03-01

This article considers the influence of the building's tallness and the presence of mounting grooved lines on the parameters of heat transfer in the gap of a hinged ventilated facade. A numerical description of the processes occurring in a heat-gravitational flow is given. The average velocity and temperature of the heat-gravitational flow of a structure with open and sealed rusts are determined with unchanged geometric parameters of the gap. The dependence of the parameters influencing the thermomechanical characteristics of the enclosing structure is derived depending on the internal parameters of the system. Physical modeling of real multistory structures is performed by projecting actual parameters onto a reduced laboratory model (scaling).

A new universal dynamic model to describe eating rate and cumulative intake curves123

PubMed Central

Paynter, Jonathan; Peterson, Courtney M; Heymsfield, Steven B

2017-01-01

Background: Attempts to model cumulative intake curves with quadratic functions have not simultaneously taken gustatory stimulation, satiation, and maximal food intake into account. Objective: Our aim was to develop a dynamic model for cumulative intake curves that captures gustatory stimulation, satiation, and maximal food intake. Design: We developed a first-principles model describing cumulative intake that universally describes gustatory stimulation, satiation, and maximal food intake using 3 key parameters: 1) the initial eating rate, 2) the effective duration of eating, and 3) the maximal food intake. These model parameters were estimated in a study (n = 49) where eating rates were deliberately changed. Baseline data was used to determine the quality of model's fit to data compared with the quadratic model. The 3 parameters were also calculated in a second study consisting of restrained and unrestrained eaters. Finally, we calculated when the gustatory stimulation phase is short or absent. Results: The mean sum squared error for the first-principles model was 337.1 ± 240.4 compared with 581.6 ± 563.5 for the quadratic model, or a 43% improvement in fit. Individual comparison demonstrated lower errors for 94% of the subjects. Both sex (P = 0.002) and eating duration (P = 0.002) were associated with the initial eating rate (adjusted R2 = 0.23). Sex was also associated (P = 0.03 and P = 0.012) with the effective eating duration and maximum food intake (adjusted R2 = 0.06 and 0.11). In participants directed to eat as much as they could compared with as much as they felt comfortable with, the maximal intake parameter was approximately double the amount. The model found that certain parameter regions resulted in both stimulation and satiation phases, whereas others only produced a satiation phase. Conclusions: The first-principles model better quantifies interindividual differences in food intake, shows how aspects of food intake differ across subpopulations, and can be applied to determine how eating behavior factors influence total food intake. PMID:28077377

Evaluation of pulsed streamer corona experiments to determine the O* radical yield

NASA Astrophysics Data System (ADS)

van Heesch, E. J. M.; Winands, G. J. J.; Pemen, A. J. M.

2008-12-01

The production of O* radicals in air by a pulsed streamer plasma is studied by integration of a large set of precise experimental data and the chemical kinetics of ozone production. The measured data comprise ozone production, plasma energy, streamer volume, streamer length, streamer velocity, humidity and gas-flow rate. Instead of entering input parameters into a kinetic model to calculate the end products the opposite strategy is followed. Since the amount of end-products (ozone) is known from the measurements the model had to be applied in the reverse direction to determine the input parameters, i.e. the O* radical concentration.

Heat transfer modelling of pulsed laser-tissue interaction

NASA Astrophysics Data System (ADS)

Urzova, J.; Jelinek, M.

2018-03-01

Due to their attributes, the application of medical lasers is on the rise in numerous medical fields. From a biomedical point of view, the most interesting applications are the thermal interactions and the photoablative interactions, which effectively remove tissue without excessive heat damage to the remaining tissue. The objective of this work is to create a theoretical model for heat transfer in the tissue following its interaction with the laser beam to predict heat transfer during medical laser surgery procedures. The dimensions of the ablated crater (shape and ablation depth) were determined by computed tomography imaging. COMSOL Multiphysics software was used for temperature modelling. The parameters of tissue and blood, such as density, specific heat capacity, thermal conductivity and diffusivity, were calculated from the chemical ratio. The parameters of laser-tissue interaction, such as absorption and reflection coefficients, were experimentally determined. The parameters of the laser beam were power density, repetition frequency, pulse length and spot dimensions. Heat spreading after laser interaction with tissue was captured using a Fluke thermal camera. The model was verified for adipose tissue, skeletal muscle tissue and heart muscle tissue.

Scalar field cosmology in f(R,T) gravity via Noether symmetry

NASA Astrophysics Data System (ADS)

Sharif, M.; Nawazish, Iqra

2018-04-01

This paper investigates the existence of Noether symmetries of isotropic universe model in f(R,T) gravity admitting minimal coupling of matter and scalar fields. The scalar field incorporates two dark energy models such as quintessence and phantom models. We determine symmetry generators and corresponding conserved quantities for two particular f(R,T) models. We also evaluate exact solutions and investigate their physical behavior via different cosmological parameters. For the first model, the graphical behavior of these parameters indicate consistency with recent observations representing accelerated expansion of the universe. For the second model, these parameters identify a transition form accelerated to decelerated expansion of the universe. The potential function is found to be constant for the first model while it becomes V(φ )≈ φ 2 for the second model. We conclude that the Noether symmetry generators and corresponding conserved quantities appear in all cases.

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.

Understanding system dynamics of an adaptive enzyme network from globally profiled kinetic parameters.

PubMed

Chiang, Austin W T; Liu, Wei-Chung; Charusanti, Pep; Hwang, Ming-Jing

2014-01-15

A major challenge in mathematical modeling of biological systems is to determine how model parameters contribute to systems dynamics. As biological processes are often complex in nature, it is desirable to address this issue using a systematic approach. Here, we propose a simple methodology that first performs an enrichment test to find patterns in the values of globally profiled kinetic parameters with which a model can produce the required system dynamics; this is then followed by a statistical test to elucidate the association between individual parameters and different parts of the system's dynamics. We demonstrate our methodology on a prototype biological system of perfect adaptation dynamics, namely the chemotaxis model for Escherichia coli. Our results agreed well with those derived from experimental data and theoretical studies in the literature. Using this model system, we showed that there are motifs in kinetic parameters and that these motifs are governed by constraints of the specified system dynamics. A systematic approach based on enrichment statistical tests has been developed to elucidate the relationships between model parameters and the roles they play in affecting system dynamics of a prototype biological network. The proposed approach is generally applicable and therefore can find wide use in systems biology modeling research.

A rigorous multiple independent binding site model for determining cell-based equilibrium dissociation constants.

PubMed

Drake, Andrew W; Klakamp, Scott L

2007-01-10

A new 4-parameter nonlinear equation based on the standard multiple independent binding site model (MIBS) is presented for fitting cell-based ligand titration data in order to calculate the ligand/cell receptor equilibrium dissociation constant and the number of receptors/cell. The most commonly used linear (Scatchard Plot) or nonlinear 2-parameter model (a single binding site model found in commercial programs like Prism(R)) used for analysis of ligand/receptor binding data assumes only the K(D) influences the shape of the titration curve. We demonstrate using simulated data sets that, depending upon the cell surface receptor expression level, the number of cells titrated, and the magnitude of the K(D) being measured, this assumption of always being under K(D)-controlled conditions can be erroneous and can lead to unreliable estimates for the binding parameters. We also compare and contrast the fitting of simulated data sets to the commonly used cell-based binding equation versus our more rigorous 4-parameter nonlinear MIBS model. It is shown through these simulations that the new 4-parameter MIBS model, when used for cell-based titrations under optimal conditions, yields highly accurate estimates of all binding parameters and hence should be the preferred model to fit cell-based experimental nonlinear titration data.

Kalman filter data assimilation: targeting observations and parameter estimation.

PubMed

Bellsky, Thomas; Kostelich, Eric J; Mahalov, Alex

2014-06-01

This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.

Origin of the sensitivity in modeling the glide behaviour of dislocations

DOE PAGES

Pei, Zongrui; Stocks, George Malcolm

2018-03-26

The sensitivity in predicting glide behaviour of dislocations has been a long-standing problem in the framework of the Peierls-Nabarro model. The predictions of both the model itself and the analytic formulas based on it are too sensitive to the input parameters. In order to reveal the origin of this important problem in materials science, a new empirical-parameter-free formulation is proposed in the same framework. Unlike previous formulations, it includes only a limited small set of parameters all of which can be determined by convergence tests. Under special conditions the new formulation is reduced to its classic counterpart. In the lightmore » of this formulation, new relationships between Peierls stresses and the input parameters are identified, where the sensitivity is greatly reduced or even removed.« less

Kalman filter data assimilation: Targeting observations and parameter estimation

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

Bellsky, Thomas, E-mail: bellskyt@asu.edu; Kostelich, Eric J.; Mahalov, Alex

2014-06-15

This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly locatedmore » observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.« less

Set-base dynamical parameter estimation and model invalidation for biochemical reaction networks.

PubMed

Rumschinski, Philipp; Borchers, Steffen; Bosio, Sandro; Weismantel, Robert; Findeisen, Rolf

2010-05-25

Mathematical modeling and analysis have become, for the study of biological and cellular processes, an important complement to experimental research. However, the structural and quantitative knowledge available for such processes is frequently limited, and measurements are often subject to inherent and possibly large uncertainties. This results in competing model hypotheses, whose kinetic parameters may not be experimentally determinable. Discriminating among these alternatives and estimating their kinetic parameters is crucial to improve the understanding of the considered process, and to benefit from the analytical tools at hand. In this work we present a set-based framework that allows to discriminate between competing model hypotheses and to provide guaranteed outer estimates on the model parameters that are consistent with the (possibly sparse and uncertain) experimental measurements. This is obtained by means of exact proofs of model invalidity that exploit the polynomial/rational structure of biochemical reaction networks, and by making use of an efficient strategy to balance solution accuracy and computational effort. The practicability of our approach is illustrated with two case studies. The first study shows that our approach allows to conclusively rule out wrong model hypotheses. The second study focuses on parameter estimation, and shows that the proposed method allows to evaluate the global influence of measurement sparsity, uncertainty, and prior knowledge on the parameter estimates. This can help in designing further experiments leading to improved parameter estimates.

Set-base dynamical parameter estimation and model invalidation for biochemical reaction networks

PubMed Central

2010-01-01

Background Mathematical modeling and analysis have become, for the study of biological and cellular processes, an important complement to experimental research. However, the structural and quantitative knowledge available for such processes is frequently limited, and measurements are often subject to inherent and possibly large uncertainties. This results in competing model hypotheses, whose kinetic parameters may not be experimentally determinable. Discriminating among these alternatives and estimating their kinetic parameters is crucial to improve the understanding of the considered process, and to benefit from the analytical tools at hand. Results In this work we present a set-based framework that allows to discriminate between competing model hypotheses and to provide guaranteed outer estimates on the model parameters that are consistent with the (possibly sparse and uncertain) experimental measurements. This is obtained by means of exact proofs of model invalidity that exploit the polynomial/rational structure of biochemical reaction networks, and by making use of an efficient strategy to balance solution accuracy and computational effort. Conclusions The practicability of our approach is illustrated with two case studies. The first study shows that our approach allows to conclusively rule out wrong model hypotheses. The second study focuses on parameter estimation, and shows that the proposed method allows to evaluate the global influence of measurement sparsity, uncertainty, and prior knowledge on the parameter estimates. This can help in designing further experiments leading to improved parameter estimates. PMID:20500862

Parameter-induced uncertainty quantification of crop yields, soil N2O and CO2 emission for 8 arable sites across Europe using the LandscapeDNDC model

NASA Astrophysics Data System (ADS)

Santabarbara, Ignacio; Haas, Edwin; Kraus, David; Herrera, Saul; Klatt, Steffen; Kiese, Ralf

2014-05-01

When using biogeochemical models to estimate greenhouse gas emissions at site to regional/national levels, the assessment and quantification of the uncertainties of simulation results are of significant importance. The uncertainties in simulation results of process-based ecosystem models may result from uncertainties of the process parameters that describe the processes of the model, model structure inadequacy as well as uncertainties in the observations. Data for development and testing of uncertainty analisys were corp yield observations, measurements of soil fluxes of nitrous oxide (N2O) and carbon dioxide (CO2) from 8 arable sites across Europe. Using the process-based biogeochemical model LandscapeDNDC for simulating crop yields, N2O and CO2 emissions, our aim is to assess the simulation uncertainty by setting up a Bayesian framework based on Metropolis-Hastings algorithm. Using Gelman statistics convergence criteria and parallel computing techniques, enable multi Markov Chains to run independently in parallel and create a random walk to estimate the joint model parameter distribution. Through means distribution we limit the parameter space, get probabilities of parameter values and find the complex dependencies among them. With this parameter distribution that determines soil-atmosphere C and N exchange, we are able to obtain the parameter-induced uncertainty of simulation results and compare them with the measurements data.

Improved battery parameter estimation method considering operating scenarios for HEV/EV applications

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

Yang, Jufeng; Xia, Bing; Shang, Yunlong

This study presents an improved battery parameter estimation method based on typical operating scenarios in hybrid electric vehicles and pure electric vehicles. Compared with the conventional estimation methods, the proposed method takes both the constant-current charging and the dynamic driving scenarios into account, and two separate sets of model parameters are estimated through different parts of the pulse-rest test. The model parameters for the constant-charging scenario are estimated from the data in the pulse-charging periods, while the model parameters for the dynamic driving scenario are estimated from the data in the rest periods, and the length of the fitted datasetmore » is determined by the spectrum analysis of the load current. In addition, the unsaturated phenomenon caused by the long-term resistor-capacitor (RC) network is analyzed, and the initial voltage expressions of the RC networks in the fitting functions are improved to ensure a higher model fidelity. Simulation and experiment results validated the feasibility of the developed estimation method.« less

Sensitivity studies with a coupled ice-ocean model of the marginal ice zone

NASA Technical Reports Server (NTRS)

Roed, L. P.

1983-01-01

An analytical coupled ice-ocean model is considered which is forced by a specified wind stress acting on the open ocean as well as the ice. The analysis supports the conjecture that the upwelling dynamics at ice edges can be understood by means of a simple analytical model. In similarity with coastal problems it is shown that the ice edge upwelling is determined by the net mass flux at the boundaries of the considered region. The model is used to study the sensitivity of the upwelling dynamics in the marginal ice zone to variation in the controlling parameters. These parameters consist of combinations of the drag coefficients used in the parameterization of the stresses on the three interfaces atmosphere-ice, atmosphere-ocean, and ice-ocean. The response is shown to be sensitive to variations in these parameters in that one set of parameters may give upwelling while a slightly different set of parameters may give downwelling.

Improved battery parameter estimation method considering operating scenarios for HEV/EV applications

DOE PAGES

Yang, Jufeng; Xia, Bing; Shang, Yunlong; ...

2016-12-22

This study presents an improved battery parameter estimation method based on typical operating scenarios in hybrid electric vehicles and pure electric vehicles. Compared with the conventional estimation methods, the proposed method takes both the constant-current charging and the dynamic driving scenarios into account, and two separate sets of model parameters are estimated through different parts of the pulse-rest test. The model parameters for the constant-charging scenario are estimated from the data in the pulse-charging periods, while the model parameters for the dynamic driving scenario are estimated from the data in the rest periods, and the length of the fitted datasetmore » is determined by the spectrum analysis of the load current. In addition, the unsaturated phenomenon caused by the long-term resistor-capacitor (RC) network is analyzed, and the initial voltage expressions of the RC networks in the fitting functions are improved to ensure a higher model fidelity. Simulation and experiment results validated the feasibility of the developed estimation method.« less

Modeling multivariate time series on manifolds with skew radial basis functions.

PubMed

Jamshidi, Arta A; Kirby, Michael J

2011-01-01

We present an approach for constructing nonlinear empirical mappings from high-dimensional domains to multivariate ranges. We employ radial basis functions and skew radial basis functions for constructing a model using data that are potentially scattered or sparse. The algorithm progresses iteratively, adding a new function at each step to refine the model. The placement of the functions is driven by a statistical hypothesis test that accounts for correlation in the multivariate range variables. The test is applied on training and validation data and reveals nonstatistical or geometric structure when it fails. At each step, the added function is fit to data contained in a spatiotemporally defined local region to determine the parameters--in particular, the scale of the local model. The scale of the function is determined by the zero crossings of the autocorrelation function of the residuals. The model parameters and the number of basis functions are determined automatically from the given data, and there is no need to initialize any ad hoc parameters save for the selection of the skew radial basis functions. Compactly supported skew radial basis functions are employed to improve model accuracy, order, and convergence properties. The extension of the algorithm to higher-dimensional ranges produces reduced-order models by exploiting the existence of correlation in the range variable data. Structure is tested not just in a single time series but between all pairs of time series. We illustrate the new methodologies using several illustrative problems, including modeling data on manifolds and the prediction of chaotic time series.

Modal Damping Ratio and Optimal Elastic Moduli of Human Body Segments for Anthropometric Vibratory Model of Standing Subjects.

PubMed

Gupta, Manoj; Gupta, T C

2017-10-01

The present study aims to accurately estimate inertial, physical, and dynamic parameters of human body vibratory model consistent with physical structure of the human body that also replicates its dynamic response. A 13 degree-of-freedom (DOF) lumped parameter model for standing person subjected to support excitation is established. Model parameters are determined from anthropometric measurements, uniform mass density, elastic modulus of individual body segments, and modal damping ratios. Elastic moduli of ellipsoidal body segments are initially estimated by comparing stiffness of spring elements, calculated from a detailed scheme, and values available in literature for same. These values are further optimized by minimizing difference between theoretically calculated platform-to-head transmissibility ratio (TR) and experimental measurements. Modal damping ratios are estimated from experimental transmissibility response using two dominant peaks in the frequency range of 0-25 Hz. From comparison between dynamic response determined form modal analysis and experimental results, a set of elastic moduli for different segments of human body and a novel scheme to determine modal damping ratios from TR plots, are established. Acceptable match between transmissibility values calculated from the vibratory model and experimental measurements for 50th percentile U.S. male, except at very low frequencies, establishes the human body model developed. Also, reasonable agreement obtained between theoretical response curve and experimental response envelop for average Indian male, affirms the technique used for constructing vibratory model of a standing person. Present work attempts to develop effective technique for constructing subject specific damped vibratory model based on its physical measurements.

A SCR Model Calibration Approach with Spatially Resolved Measurements and NH 3 Storage Distributions

DOE PAGES

Song, Xiaobo; Parker, Gordon G.; Johnson, John H.; ...

2014-11-27

The selective catalytic reduction (SCR) is a technology used for reducing NO x emissions in the heavy-duty diesel (HDD) engine exhaust. In this study, the spatially resolved capillary inlet infrared spectroscopy (Spaci-IR) technique was used to study the gas concentration and NH 3 storage distributions in a SCR catalyst, and to provide data for developing a SCR model to analyze the axial gaseous concentration and axial distributions of NH 3 storage. A two-site SCR model is described for simulating the reaction mechanisms. The model equations and a calculation method was developed using the Spaci-IR measurements to determine the NH 3more » storage capacity and the relationships between certain kinetic parameters of the model. Moreover, a calibration approach was then applied for tuning the kinetic parameters using the spatial gaseous measurements and calculated NH3 storage as a function of axial position instead of inlet and outlet gaseous concentrations of NO, NO 2, and NH 3. The equations and the approach for determining the NH 3 storage capacity of the catalyst and a method of dividing the NH 3 storage capacity between the two storage sites are presented. It was determined that the kinetic parameters of the adsorption and desorption reactions have to follow certain relationships for the model to simulate the experimental data. Finally, the modeling results served as a basis for developing full model calibrations to SCR lab reactor and engine data and state estimator development as described in the references (Song et al. 2013a, b; Surenahalli et al. 2013).« less

A Model of Regularization Parameter Determination in Low-Dose X-Ray CT Reconstruction Based on Dictionary Learning.

PubMed

Zhang, Cheng; Zhang, Tao; Zheng, Jian; Li, Ming; Lu, Yanfei; You, Jiali; Guan, Yihui

2015-01-01

In recent years, X-ray computed tomography (CT) is becoming widely used to reveal patient's anatomical information. However, the side effect of radiation, relating to genetic or cancerous diseases, has caused great public concern. The problem is how to minimize radiation dose significantly while maintaining image quality. As a practical application of compressed sensing theory, one category of methods takes total variation (TV) minimization as the sparse constraint, which makes it possible and effective to get a reconstruction image of high quality in the undersampling situation. On the other hand, a preliminary attempt of low-dose CT reconstruction based on dictionary learning seems to be another effective choice. But some critical parameters, such as the regularization parameter, cannot be determined by detecting datasets. In this paper, we propose a reweighted objective function that contributes to a numerical calculation model of the regularization parameter. A number of experiments demonstrate that this strategy performs well with better reconstruction images and saving of a large amount of time.

Thermodynamic, electronic and magnetic properties of intermetallic compounds through statistical models

NASA Astrophysics Data System (ADS)

Cadeville, M. C.; Pierron-Bohnes, V.; Bouzidi, L.; Sanchez, J. M.

1993-01-01

Local and average electronic and magnetic properties of transition metal alloys are strongly correlated to the distribution of atoms on the lattice sites. The ability of some systems to form long range ordered structures at low temperature allows to discuss their properties in term of well identified occupation operators as those related to long range order (LRO) parameters. We show that using theoretical determinations of these LRO parameters through statistical models like the cluster variation method (CVM) developed to simulate the experimental phase diagrams, we are able to reproduce a lot of physical properties. In this paper we focus on two points: (i) a comparison between CVM results and an experimental determination of the LRO parameter by NMR at 59Co in a CoPt3 compound, and (ii) the modelling of the resistivity of ferromagnetic and paramagnetic intermetallic compounds belonging to Co-Pt, Ni-Pt and Fe-Al systems. All experiments were performed on samples in identified thermodynamic states, implying that kinetic effects are thoroughly taken into account.

SU-F-J-187: The Statistical NTCP and TCP Models in the Proton Therapy

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

Jang, S; Frometa, T; Pyakuryal, A

2016-06-15

Purpose: The statistical models (SM) are typically used as a subjective description of a population for which there is only limited sample data, and especially in cases where the relationship between variables is known. The normal tissue complications and tumor control are frequently stochastic effects in the Radiotherapy (RT). Based on probabilistic treatments, it recently has been formulated new NTCP and TCP models for the RT. Investigating the particular requirements for their clinical use in the proton therapy (PT) is the goal of this work. Methods: The SM can be used as phenomenological or mechanistic models. The former way allowsmore » fitting real data and getting theirparameters. In the latter one, we should do efforts for determining the parameters through the acceptable estimations, measurements, and/or simulation experiments. Experimental methodologies for determination of the parameters have been developed from the fraction cells surviving the proton irradiation curves in tumor and OAR, and precise RBE models are used for calculating the variable of effective dose. As the executions of these methodologies have a high costs, so we have developed computer tools enable to perform simulation experiments as complement to limitations of the real ones. Results: The requirements for the use of the SM in the PT, such as validation and improvement of the elaborated and existent methodologies for determining the SM parameters and effective dose respectively, were determined. Conclusion: The SM realistically simulates the main processes in the PT, and for this reason these can be implemented in this therapy, which are simples, computable and they have other advantages over some current models. It has been determined some negative aspects for some currently used probabilistic models in the RT, like the LKB NTCP and others derived from logistic functions; which can be improved with the proposed methods in this study.« less

Model independent constraints on transition redshift

NASA Astrophysics Data System (ADS)

Jesus, J. F.; Holanda, R. F. L.; Pereira, S. H.

2018-05-01

This paper aims to put constraints on the transition redshift zt, which determines the onset of cosmic acceleration, in cosmological-model independent frameworks. In order to perform our analyses, we consider a flat universe and assume a parametrization for the comoving distance DC(z) up to third degree on z, a second degree parametrization for the Hubble parameter H(z) and a linear parametrization for the deceleration parameter q(z). For each case, we show that type Ia supernovae and H(z) data complement each other on the parameter space and tighter constrains for the transition redshift are obtained. By combining the type Ia supernovae observations and Hubble parameter measurements it is possible to constrain the values of zt, for each approach, as 0.806± 0.094, 0.870± 0.063 and 0.973± 0.058 at 1σ c.l., respectively. Then, such approaches provide cosmological-model independent estimates for this parameter.

Noncommutative Jackiw-Pi model: One-loop renormalization

NASA Astrophysics Data System (ADS)

Bufalo, R.; Ghasemkhani, M.; Alipour, M.

2018-06-01

In this paper, we study the quantum behavior of the noncommutative Jackiw-Pi model. After establishing the Becchi-Rouet-Store-Tyutin (BRST) invariant action, the perturbative renormalizability is discussed, allowing us to introduce the renormalized mass and gauge coupling. We then proceed to compute the one-loop correction to the basic 1PI functions, necessary to determine the renormalized parameters (mass and charge), next we discuss the physical behavior of these parameters.

Atherosclerotic plaque delamination: Experiments and 2D finite element model to simulate plaque peeling in two strains of transgenic mice.

PubMed

Merei, Bilal; Badel, Pierre; Davis, Lindsey; Sutton, Michael A; Avril, Stéphane; Lessner, Susan M

2017-03-01

Finite element analyses using cohesive zone models (CZM) can be used to predict the fracture of atherosclerotic plaques but this requires setting appropriate values of the model parameters. In this study, material parameters of a CZM were identified for the first time on two groups of mice (ApoE -/- and ApoE -/- Col8 -/- ) using the measured force-displacement curves acquired during delamination tests. To this end, a 2D finite-element model of each plaque was solved using an explicit integration scheme. Each constituent of the plaque was modeled with a neo-Hookean strain energy density function and a CZM was used for the interface. The model parameters were calibrated by minimizing the quadratic deviation between the experimental force displacement curves and the model predictions. The elastic parameter of the plaque and the CZM interfacial parameter were successfully identified for a cohort of 11 mice. The results revealed that only the elastic parameter was significantly different between the two groups, ApoE -/- Col8 -/- plaques being less stiff than ApoE -/- plaques. Finally, this study demonstrated that a simple 2D finite element model with cohesive elements can reproduce fairly well the plaque peeling global response. Future work will focus on understanding the main biological determinants of regional and inter-individual variations of the material parameters used in the model. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predicting the Activity Coefficients of Free-Solvent for Concentrated Globular Protein Solutions Using Independently Determined Physical Parameters

PubMed Central

McBride, Devin W.; Rodgers, Victor G. J.

2013-01-01

The activity coefficient is largely considered an empirical parameter that was traditionally introduced to correct the non-ideality observed in thermodynamic systems such as osmotic pressure. Here, the activity coefficient of free-solvent is related to physically realistic parameters and a mathematical expression is developed to directly predict the activity coefficients of free-solvent, for aqueous protein solutions up to near-saturation concentrations. The model is based on the free-solvent model, which has previously been shown to provide excellent prediction of the osmotic pressure of concentrated and crowded globular proteins in aqueous solutions up to near-saturation concentrations. Thus, this model uses only the independently determined, physically realizable quantities: mole fraction, solvent accessible surface area, and ion binding, in its prediction. Predictions are presented for the activity coefficients of free-solvent for near-saturated protein solutions containing either bovine serum albumin or hemoglobin. As a verification step, the predictability of the model for the activity coefficient of sucrose solutions was evaluated. The predicted activity coefficients of free-solvent are compared to the calculated activity coefficients of free-solvent based on osmotic pressure data. It is observed that the predicted activity coefficients are increasingly dependent on the solute-solvent parameters as the protein concentration increases to near-saturation concentrations. PMID:24324733

The physics of golf

NASA Astrophysics Data System (ADS)

Penner, A. Raymond

2003-02-01

An overview of the application of physics to the game of golf is given. The golf swing is modelled as a double pendulum. This model and its variations have been used extensively by researchers in determining the effect that various swing parameters have on clubhead speed. These results as well as examples of three-link models are discussed. Kinematic and kinetic measurements taken on the recorded downswings of golfers as well as force measurements are reviewed. These measurements highlight differences between the swings of skilled and unskilled golfers. Several aspects of the behaviour of a golf ball are examined. Measurements and models of the impact of golf balls with barriers are reviewed. Such measurements have allowed researchers to determine the effect that different golf ball constructions have on their launch parameters. The launch parameters determine not only the length of the golf shot but also the behaviour of the golf ball on impact with the turf. The effect of dimples on the aerodynamics of a golf ball and the length of the golf shot is discussed. Models of the bounce and roll of a golf ball after impact with the turf as well as models of the motion of a putted ball are presented. Researchers have measured and modelled the behaviour of both the shaft and the clubhead during the downswing and at impact. The effect that clubhead mass and loft as well as the shaft length and mass have on the length of a golf shot are considered. Models and measurements of the flexing of the shaft as well as research into the flexing of the clubface and the effects of its surface roughness are presented. An important consideration in clubhead design is its behaviour during off-centre impacts. In line with this, the effects that the curvature of a clubface and the moments of inertia of the clubhead have on the launch parameters and trajectory of an off-centred impacted golf ball are examined.

Interpretation of environmental tracers in groundwater systems with stagnant water zones.

PubMed

Maloszewski, Piotr; Stichler, Willibald; Zuber, Andrzej

2004-03-01

Lumped-parameter models are commonly applied for determining the age of water from time records of transient environmental tracers. The simplest models (e.g. piston flow or exponential) are also applicable for dating based on the decay or accumulation of tracers in groundwater systems. The models are based on the assumption that the transit time distribution function (exit age distribution function) of the tracer particles in the investigated system adequately represents the distribution of flow lines and is described by a simple function. A chosen or fitted function (called the response function) describes the transit time distribution of a tracer which would be observed at the output (discharge area, spring, stream, or pumping wells) in the case of an instantaneous injection at the entrance (recharge area). Due to large space and time scales, response functions are not measurable in groundwater systems, therefore, functions known from other fields of science, mainly from chemical engineering, are usually used. The type of response function and the values of its parameters define the lumped-parameter model of a system. The main parameter is the mean transit time of tracer through the system, which under favourable conditions may represent the mean age of mobile water. The parameters of the model are found by fitting calculated concentrations to the experimental records of concentrations measured at the outlet. The mean transit time of tracer (often called the tracer age), whether equal to the mean age of water or not, serves in adequate combinations with other data for determining other useful parameters, e.g. the recharge rate or the content of water in the system. The transit time distribution and its mean value serve for confirmation or determination of the conceptual model of the system and/or estimation of its potential vulnerability to anthropogenic pollution. In the interpretation of environmental tracer data with the aid of the lumped-parameter models, the influence of diffusion exchange between mobile water and stagnant or quasi-stagnant water is seldom considered, though it leads to large differences between tracer and water ages. Therefore, the article is focused on the transit time distribution functions of the most common lumped-parameter models, particularly those applicable for the interpretation of environmental tracer data in double-porosity aquifers, or aquifers in which aquitard diffusion may play an important role. A case study is recalled for a confined aquifer in which the diffusion exchange with aquitard most probably strongly influenced the transport of environmental tracers. Another case study presented is related to the interpretation of environmental tracer data obtained from lysimeters installed in the unsaturated zone with a fraction of stagnant water.

MOVES sensitivity study

DOT National Transportation Integrated Search

2012-01-01

Purpose: : To determine ranking of important parameters and the overall sensitivity to values of variables in MOVES : To allow a greater understanding of the MOVES modeling process for users : Continued support by FHWA to transportation modeling comm...

Shallow Turbulence in Rivers and Estuaries

DTIC Science & Technology

2012-09-30

objectives are to: 1. Determine spatial patterns of shallow turbulence from in-situ and remote sensing data and investigate the effects and...production through a model parameter study, and determine the optimal model configuration that statistically reproduces the shallow turbulence...more probable cause. According to Nezu et al. (1993), longitudinal vorticity streets would cause alternating upwelling (boils) and down welling

One-Dimensional Simulations for Spall in Metals with Intra- and Inter-grain failure models

NASA Astrophysics Data System (ADS)

Ferri, Brian; Dwivedi, Sunil; McDowell, David

2017-06-01

The objective of the present work is to model spall failure in metals with coupled effect of intra-grain and inter-grain failure mechanisms. The two mechanisms are modeled by a void nucleation, growth, and coalescence (VNGC) model and contact-cohesive model respectively. Both models were implemented in a 1-D code to simulate spall in 6061-T6 aluminum at two impact velocities. The parameters of the VNGC model without inter-grain failure and parameters of the cohesive model without intra-grain failure were first determined to obtain pull-back velocity profiles in agreement with experimental data. With the same impact velocities, the same sets of parameters did not predict the velocity profiles when both mechanisms were simultaneously activated. A sensitivity study was performed to predict spall under combined mechanisms by varying critical stress in the VNGC model and maximum traction in the cohesive model. The study provided possible sets of the two parameters leading to spall. Results will be presented comparing the predicted velocity profile with experimental data using one such set of parameters for the combined intra-grain and inter-grain failures during spall. Work supported by HDTRA1-12-1-0004 gran and by the School of Mechanical Engineering GTA.

Modeling, estimation and identification methods for static shape determination of flexible structures. [for large space structure design

NASA Technical Reports Server (NTRS)

Rodriguez, G.; Scheid, R. E., Jr.

1986-01-01

This paper outlines methods for modeling, identification and estimation for static determination of flexible structures. The shape estimation schemes are based on structural models specified by (possibly interconnected) elliptic partial differential equations. The identification techniques provide approximate knowledge of parameters in elliptic systems. The techniques are based on the method of maximum-likelihood that finds parameter values such that the likelihood functional associated with the system model is maximized. The estimation methods are obtained by means of a function-space approach that seeks to obtain the conditional mean of the state given the data and a white noise characterization of model errors. The solutions are obtained in a batch-processing mode in which all the data is processed simultaneously. After methods for computing the optimal estimates are developed, an analysis of the second-order statistics of the estimates and of the related estimation error is conducted. In addition to outlining the above theoretical results, the paper presents typical flexible structure simulations illustrating performance of the shape determination methods.

Meson properties and phase diagrams in a SU(3) nonlocal PNJL model with lattice-QCD-inspired form factors

NASA Astrophysics Data System (ADS)

Carlomagno, J. P.

2018-05-01

We study the features of a nonlocal SU(3) Polyakov-Nambu-Jona-Lasinio model that includes wave-function renormalization. Model parameters are determined from vacuum phenomenology considering lattice-QCD-inspired nonlocal form factors. Within this framework, we analyze the properties of light scalar and pseudoscalar mesons at finite temperature and chemical potential determining characteristics of deconfinement and chiral restoration transitions.

Development of model for prediction of Leachate Pollution Index (LPI) in absence of leachate parameters.

PubMed

Lothe, Anjali G; Sinha, Alok

2017-05-01

Leachate pollution index (LPI) is an environmental index which quantifies the pollution potential of leachate generated in landfill site. Calculation of Leachate pollution index (LPI) is based on concentration of 18 parameters present in leachate. However, in case of non-availability of all 18 parameters evaluation of actual values of LPI becomes difficult. In this study, a model has been developed to predict the actual values of LPI in case of partial availability of parameters. This model generates eleven equations that helps in determination of upper and lower limit of LPI. The geometric mean of these two values results in LPI value. Application of this model to three landfill site results in LPI value with an error of ±20% for ∑ i n w i ⩾0.6. Copyright © 2016 Elsevier Ltd. All rights reserved.

Probabilistic Inference: Task Dependency and Individual Differences of Probability Weighting Revealed by Hierarchical Bayesian Modeling

PubMed Central

Boos, Moritz; Seer, Caroline; Lange, Florian; Kopp, Bruno

2016-01-01

Cognitive determinants of probabilistic inference were examined using hierarchical Bayesian modeling techniques. A classic urn-ball paradigm served as experimental strategy, involving a factorial two (prior probabilities) by two (likelihoods) design. Five computational models of cognitive processes were compared with the observed behavior. Parameter-free Bayesian posterior probabilities and parameter-free base rate neglect provided inadequate models of probabilistic inference. The introduction of distorted subjective probabilities yielded more robust and generalizable results. A general class of (inverted) S-shaped probability weighting functions had been proposed; however, the possibility of large differences in probability distortions not only across experimental conditions, but also across individuals, seems critical for the model's success. It also seems advantageous to consider individual differences in parameters of probability weighting as being sampled from weakly informative prior distributions of individual parameter values. Thus, the results from hierarchical Bayesian modeling converge with previous results in revealing that probability weighting parameters show considerable task dependency and individual differences. Methodologically, this work exemplifies the usefulness of hierarchical Bayesian modeling techniques for cognitive psychology. Theoretically, human probabilistic inference might be best described as the application of individualized strategic policies for Bayesian belief revision. PMID:27303323

Modeling the soil water retention curves of soil-gravel mixtures with regression method on the Loess Plateau of China.

PubMed

Wang, Huifang; Xiao, Bo; Wang, Mingyu; Shao, Ming'an

2013-01-01

Soil water retention parameters are critical to quantify flow and solute transport in vadose zone, while the presence of rock fragments remarkably increases their variability. Therefore a novel method for determining water retention parameters of soil-gravel mixtures is required. The procedure to generate such a model is based firstly on the determination of the quantitative relationship between the content of rock fragments and the effective saturation of soil-gravel mixtures, and then on the integration of this relationship with former analytical equations of water retention curves (WRCs). In order to find such relationships, laboratory experiments were conducted to determine WRCs of soil-gravel mixtures obtained with a clay loam soil mixed with shale clasts or pebbles in three size groups with various gravel contents. Data showed that the effective saturation of the soil-gravel mixtures with the same kind of gravels within one size group had a linear relation with gravel contents, and had a power relation with the bulk density of samples at any pressure head. Revised formulas for water retention properties of the soil-gravel mixtures are proposed to establish the water retention curved surface models of the power-linear functions and power functions. The analysis of the parameters obtained by regression and validation of the empirical models showed that they were acceptable by using either the measured data of separate gravel size group or those of all the three gravel size groups having a large size range. Furthermore, the regression parameters of the curved surfaces for the soil-gravel mixtures with a large range of gravel content could be determined from the water retention data of the soil-gravel mixtures with two representative gravel contents or bulk densities. Such revised water retention models are potentially applicable in regional or large scale field investigations of significantly heterogeneous media, where various gravel sizes and different gravel contents are present.

Modeling the Soil Water Retention Curves of Soil-Gravel Mixtures with Regression Method on the Loess Plateau of China

PubMed Central

Wang, Huifang; Xiao, Bo; Wang, Mingyu; Shao, Ming'an

2013-01-01

Soil water retention parameters are critical to quantify flow and solute transport in vadose zone, while the presence of rock fragments remarkably increases their variability. Therefore a novel method for determining water retention parameters of soil-gravel mixtures is required. The procedure to generate such a model is based firstly on the determination of the quantitative relationship between the content of rock fragments and the effective saturation of soil-gravel mixtures, and then on the integration of this relationship with former analytical equations of water retention curves (WRCs). In order to find such relationships, laboratory experiments were conducted to determine WRCs of soil-gravel mixtures obtained with a clay loam soil mixed with shale clasts or pebbles in three size groups with various gravel contents. Data showed that the effective saturation of the soil-gravel mixtures with the same kind of gravels within one size group had a linear relation with gravel contents, and had a power relation with the bulk density of samples at any pressure head. Revised formulas for water retention properties of the soil-gravel mixtures are proposed to establish the water retention curved surface models of the power-linear functions and power functions. The analysis of the parameters obtained by regression and validation of the empirical models showed that they were acceptable by using either the measured data of separate gravel size group or those of all the three gravel size groups having a large size range. Furthermore, the regression parameters of the curved surfaces for the soil-gravel mixtures with a large range of gravel content could be determined from the water retention data of the soil-gravel mixtures with two representative gravel contents or bulk densities. Such revised water retention models are potentially applicable in regional or large scale field investigations of significantly heterogeneous media, where various gravel sizes and different gravel contents are present. PMID:23555040

Decision Models for Determining the Optimal Life Test Sampling Plans

NASA Astrophysics Data System (ADS)

Nechval, Nicholas A.; Nechval, Konstantin N.; Purgailis, Maris; Berzins, Gundars; Strelchonok, Vladimir F.

2010-11-01

Life test sampling plan is a technique, which consists of sampling, inspection, and decision making in determining the acceptance or rejection of a batch of products by experiments for examining the continuous usage time of the products. In life testing studies, the lifetime is usually assumed to be distributed as either a one-parameter exponential distribution, or a two-parameter Weibull distribution with the assumption that the shape parameter is known. Such oversimplified assumptions can facilitate the follow-up analyses, but may overlook the fact that the lifetime distribution can significantly affect the estimation of the failure rate of a product. Moreover, sampling costs, inspection costs, warranty costs, and rejection costs are all essential, and ought to be considered in choosing an appropriate sampling plan. The choice of an appropriate life test sampling plan is a crucial decision problem because a good plan not only can help producers save testing time, and reduce testing cost; but it also can positively affect the image of the product, and thus attract more consumers to buy it. This paper develops the frequentist (non-Bayesian) decision models for determining the optimal life test sampling plans with an aim of cost minimization by identifying the appropriate number of product failures in a sample that should be used as a threshold in judging the rejection of a batch. The two-parameter exponential and Weibull distributions with two unknown parameters are assumed to be appropriate for modelling the lifetime of a product. A practical numerical application is employed to demonstrate the proposed approach.

Closed-loop control of epileptiform activities in a neural population model using a proportional-derivative controller

NASA Astrophysics Data System (ADS)

Wang, Jun-Song; Wang, Mei-Li; Li, Xiao-Li; Ernst, Niebur

2015-03-01

Epilepsy is believed to be caused by a lack of balance between excitation and inhibitation in the brain. A promising strategy for the control of the disease is closed-loop brain stimulation. How to determine the stimulation control parameters for effective and safe treatment protocols remains, however, an unsolved question. To constrain the complex dynamics of the biological brain, we use a neural population model (NPM). We propose that a proportional-derivative (PD) type closed-loop control can successfully suppress epileptiform activities. First, we determine the stability of root loci, which reveals that the dynamical mechanism underlying epilepsy in the NPM is the loss of homeostatic control caused by the lack of balance between excitation and inhibition. Then, we design a PD type closed-loop controller to stabilize the unstable NPM such that the homeostatic equilibriums are maintained; we show that epileptiform activities are successfully suppressed. A graphical approach is employed to determine the stabilizing region of the PD controller in the parameter space, providing a theoretical guideline for the selection of the PD control parameters. Furthermore, we establish the relationship between the control parameters and the model parameters in the form of stabilizing regions to help understand the mechanism of suppressing epileptiform activities in the NPM. Simulations show that the PD-type closed-loop control strategy can effectively suppress epileptiform activities in the NPM. Project supported by the National Natural Science Foundation of China (Grant Nos. 61473208, 61025019, and 91132722), ONR MURI N000141010278, and NIH grant R01EY016281.

Substitutional impurity in single-layer graphene: The Koster–Slater and Anderson models

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

Davydov, S. Yu., E-mail: sergei-davydov@mail.ru

The Koster–Slater and Anderson models are used to consider substitutional impurities in free-standing single-layer graphene. The density of states of graphene is described using a model (the M model). For the nitrogen and boron impurities, the occupation numbers and the parameter η which defines the fraction of delocalized electrons of the impurity are determined. In this case, experimental data are used for both determination of the model parameters and comparison with the results of theoretical estimations. The general features of the Koster–Slater and Anderson models and the differences between the two models are discussed. Specifically, it is shown that themore » band contributions to the occupation numbers of a nitrogen atom in both models are comparable, whereas the local contributions are substantially different: the local contributions are decisive in the Koster–Slater model and negligible in the Anderson model. The asymptotic behavior of the wave functions of a defect is considered in the Koster–Slater model, and the electron states of impurity dimers are considered in the Anderson model.« less

Acceleration Disturbances onboard of Geodetic Precision Space Laboratories

NASA Astrophysics Data System (ADS)

Peterseim, Nadja; Jakob, Flury; Schlicht, Anja

Bartlomiej Oszczak, b@dgps.pl University of Warmia and Mazury in Olsztyn, Poland, Olsztyn, Poland Olga Maciejczyk, omaciejczyk@gmail.com Poland In this paper there is presented the study on the parameters of the ASG-EUPOS real-time RTK service NAWGEO such as: accuracy, availability, integrity and continuity. Author's model is used for tests. These parameters enable determination of the quality of received information and practical applications of the service. Paper includes also the subject related to the NAWGEO service and algorithms used in determination of mentioned parameters. The results of accuracy and precision analyses and study on availability demonstrated that NAWGEO service enables a user a position determination with a few centimeters accuracy with high probability in any moment of time.

Stepwise calibration procedure for regional coupled hydrological-hydrogeological models

NASA Astrophysics Data System (ADS)

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

2014-05-01

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

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.

Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring.

PubMed

Najah, A; El-Shafie, A; Karim, O A; El-Shafie, Amr H

2014-02-01

We discuss the accuracy and performance of the adaptive neuro-fuzzy inference system (ANFIS) in training and prediction of dissolved oxygen (DO) concentrations. The model was used to analyze historical data generated through continuous monitoring of water quality parameters at several stations on the Johor River to predict DO concentrations. Four water quality parameters were selected for ANFIS modeling, including temperature, pH, nitrate (NO3) concentration, and ammoniacal nitrogen concentration (NH3-NL). Sensitivity analysis was performed to evaluate the effects of the input parameters. The inputs with the greatest effect were those related to oxygen content (NO3) or oxygen demand (NH3-NL). Temperature was the parameter with the least effect, whereas pH provided the lowest contribution to the proposed model. To evaluate the performance of the model, three statistical indices were used: the coefficient of determination (R (2)), the mean absolute prediction error, and the correlation coefficient. The performance of the ANFIS model was compared with an artificial neural network model. The ANFIS model was capable of providing greater accuracy, particularly in the case of extreme events.

Three-dimensional deformable-model-based localization and recognition of road vehicles.

PubMed

Zhang, Zhaoxiang; Tan, Tieniu; Huang, Kaiqi; Wang, Yunhong

2012-01-01

We address the problem of model-based object recognition. Our aim is to localize and recognize road vehicles from monocular images or videos in calibrated traffic scenes. A 3-D deformable vehicle model with 12 shape parameters is set up as prior information, and its pose is determined by three parameters, which are its position on the ground plane and its orientation about the vertical axis under ground-plane constraints. An efficient local gradient-based method is proposed to evaluate the fitness between the projection of the vehicle model and image data, which is combined into a novel evolutionary computing framework to estimate the 12 shape parameters and three pose parameters by iterative evolution. The recovery of pose parameters achieves vehicle localization, whereas the shape parameters are used for vehicle recognition. Numerous experiments are conducted in this paper to demonstrate the performance of our approach. It is shown that the local gradient-based method can evaluate accurately and efficiently the fitness between the projection of the vehicle model and the image data. The evolutionary computing framework is effective for vehicles of different types and poses is robust to all kinds of occlusion.

Verification of dosimetric accuracy on the TrueBeam STx: rounded leaf effect of the high definition MLC.

PubMed

Kielar, Kayla N; Mok, Ed; Hsu, Annie; Wang, Lei; Luxton, Gary

2012-10-01

The dosimetric leaf gap (DLG) in the Varian Eclipse treatment planning system is determined during commissioning and is used to model the effect of the rounded leaf-end of the multileaf collimator (MLC). This parameter attempts to model the physical difference between the radiation and light field and account for inherent leakage between leaf tips. With the increased use of single fraction high dose treatments requiring larger monitor units comes an enhanced concern in the accuracy of leakage calculations, as it accounts for much of the patient dose. This study serves to verify the dosimetric accuracy of the algorithm used to model the rounded leaf effect for the TrueBeam STx, and describes a methodology for determining best-practice parameter values, given the novel capabilities of the linear accelerator such as flattening filter free (FFF) treatments and a high definition MLC (HDMLC). During commissioning, the nominal MLC position was verified and the DLG parameter was determined using MLC-defined field sizes and moving gap tests, as is common in clinical testing. Treatment plans were created, and the DLG was optimized to achieve less than 1% difference between measured and calculated dose. The DLG value found was tested on treatment plans for all energies (6 MV, 10 MV, 15 MV, 6 MV FFF, 10 MV FFF) and modalities (3D conventional, IMRT, conformal arc, VMAT) available on the TrueBeam STx. The DLG parameter found during the initial MLC testing did not match the leaf gap modeling parameter that provided the most accurate dose delivery in clinical treatment plans. Using the physical leaf gap size as the DLG for the HDMLC can lead to 5% differences in measured and calculated doses. Separate optimization of the DLG parameter using end-to-end tests must be performed to ensure dosimetric accuracy in the modeling of the rounded leaf ends for the Eclipse treatment planning system. The difference in leaf gap modeling versus physical leaf gap dimensions is more pronounced in the more recent versions of Eclipse for both the HDMLC and the Millennium MLC. Once properly commissioned and tested using a methodology based on treatment plan verification, Eclipse is able to accurately model radiation dose delivered for SBRT treatments using the TrueBeam STx.

Improving Wind Predictions in the Marine Atmospheric Boundary Layer Through Parameter Estimation in a Single Column Model

DOE PAGES

Lee, Jared A.; Hacker, Joshua P.; Monache, Luca Delle; ...

2016-08-03

A current barrier to greater deployment of offshore wind turbines is the poor quality of numerical weather prediction model wind and turbulence forecasts over open ocean. The bulk of development for atmospheric boundary layer (ABL) parameterization schemes has focused on land, partly due to a scarcity of observations over ocean. The 100-m FINO1 tower in the North Sea is one of the few sources worldwide of atmospheric profile observations from the sea surface to turbine hub height. These observations are crucial to developing a better understanding and modeling of physical processes in the marine ABL. In this paper we usemore » the WRF single column model (SCM), coupled with an ensemble Kalman filter from the Data Assimilation Research Testbed (DART), to create 100-member ensembles at the FINO1 location. The goal of this study is to determine the extent to which model parameter estimation can improve offshore wind forecasts. Combining two datasets that provide lateral forcing for the SCM and two methods for determining z 0, the time-varying sea-surface roughness length, we conduct four WRF-SCM/DART experiments over the October-December 2006 period. The two methods for determining z 0 are the default Fairall-adjusted Charnock formulation in WRF, and using parameter estimation techniques to estimate z 0 in DART. Using DART to estimate z 0 is found to reduce 1-h forecast errors of wind speed over the Charnock-Fairall z 0 ensembles by 4%–22%. Finally, however, parameter estimation of z 0 does not simultaneously reduce turbulent flux forecast errors, indicating limitations of this approach and the need for new marine ABL parameterizations.« less

SU-D-BRC-01: An Automatic Beam Model Commissioning Method for Monte Carlo Simulations in Pencil-Beam Scanning Proton Therapy

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

Qin, N; Shen, C; Tian, Z

Purpose: Monte Carlo (MC) simulation is typically regarded as the most accurate dose calculation method for proton therapy. Yet for real clinical cases, the overall accuracy also depends on that of the MC beam model. Commissioning a beam model to faithfully represent a real beam requires finely tuning a set of model parameters, which could be tedious given the large number of pencil beams to commmission. This abstract reports an automatic beam-model commissioning method for pencil-beam scanning proton therapy via an optimization approach. Methods: We modeled a real pencil beam with energy and spatial spread following Gaussian distributions. Mean energy,more » and energy and spatial spread are model parameters. To commission against a real beam, we first performed MC simulations to calculate dose distributions of a set of ideal (monoenergetic, zero-size) pencil beams. Dose distribution for a real pencil beam is hence linear superposition of doses for those ideal pencil beams with weights in the Gaussian form. We formulated the commissioning task as an optimization problem, such that the calculated central axis depth dose and lateral profiles at several depths match corresponding measurements. An iterative algorithm combining conjugate gradient method and parameter fitting was employed to solve the optimization problem. We validated our method in simulation studies. Results: We calculated dose distributions for three real pencil beams with nominal energies 83, 147 and 199 MeV using realistic beam parameters. These data were regarded as measurements and used for commission. After commissioning, average difference in energy and beam spread between determined values and ground truth were 4.6% and 0.2%. With the commissioned model, we recomputed dose. Mean dose differences from measurements were 0.64%, 0.20% and 0.25%. Conclusion: The developed automatic MC beam-model commissioning method for pencil-beam scanning proton therapy can determine beam model parameters with satisfactory accuracy.« less

Near infrared spectroscopy (NIRS) for on-line determination of quality parameters in intact olives.

PubMed

Salguero-Chaparro, Lourdes; Baeten, Vincent; Fernández-Pierna, Juan A; Peña-Rodríguez, Francisco

2013-08-15

The acidity, moisture and fat content in intact olive fruits were determined on-line using a NIR diode array instrument, operating on a conveyor belt. Four sets of calibrations models were obtained by means of different combinations from samples collected during 2009-2010 and 2010-2011, using full-cross and external validation. Several preprocessing treatments such as derivatives and scatter correction were investigated by using the root mean square error of cross-validation (RMSECV) and prediction (RMSEP), as control parameters. The results obtained showed RMSECV values of 2.54-3.26 for moisture, 2.35-2.71 for fat content and 2.50-3.26 for acidity parameters, depending on the calibration model developed. Calibrations for moisture, fat content and acidity gave residual predictive deviation (RPD) values of 2.76, 2.37 and 1.60, respectively. Although, it is concluded that the on-line NIRS prediction results were acceptable for the three parameters measured in intact olive samples in movement, the models developed must be improved in order to increase their accuracy before final NIRS implementation at mills. Copyright © 2013 Elsevier Ltd. All rights reserved.

Dynamics of large-scale brain activity in normal arousal states and epileptic seizures

NASA Astrophysics Data System (ADS)

Robinson, P. A.; Rennie, C. J.; Rowe, D. L.

2002-04-01

Links between electroencephalograms (EEGs) and underlying aspects of neurophysiology and anatomy are poorly understood. Here a nonlinear continuum model of large-scale brain electrical activity is used to analyze arousal states and their stability and nonlinear dynamics for physiologically realistic parameters. A simple ordered arousal sequence in a reduced parameter space is inferred and found to be consistent with experimentally determined parameters of waking states. Instabilities arise at spectral peaks of the major clinically observed EEG rhythms-mainly slow wave, delta, theta, alpha, and sleep spindle-with each instability zone lying near its most common experimental precursor arousal states in the reduced space. Theta, alpha, and spindle instabilities evolve toward low-dimensional nonlinear limit cycles that correspond closely to EEGs of petit mal seizures for theta instability, and grand mal seizures for the other types. Nonlinear stimulus-induced entrainment and seizures are also seen, EEG spectra and potentials evoked by stimuli are reproduced, and numerous other points of experimental agreement are found. Inverse modeling enables physiological parameters underlying observed EEGs to be determined by a new, noninvasive route. This model thus provides a single, powerful framework for quantitative understanding of a wide variety of brain phenomena.

Uncertainty quantification and propagation in dynamic models using ambient vibration measurements, application to a 10-story building

NASA Astrophysics Data System (ADS)

Behmanesh, Iman; Yousefianmoghadam, Seyedsina; Nozari, Amin; Moaveni, Babak; Stavridis, Andreas

2018-07-01

This paper investigates the application of Hierarchical Bayesian model updating for uncertainty quantification and response prediction of civil structures. In this updating framework, structural parameters of an initial finite element (FE) model (e.g., stiffness or mass) are calibrated by minimizing error functions between the identified modal parameters and the corresponding parameters of the model. These error functions are assumed to have Gaussian probability distributions with unknown parameters to be determined. The estimated parameters of error functions represent the uncertainty of the calibrated model in predicting building's response (modal parameters here). The focus of this paper is to answer whether the quantified model uncertainties using dynamic measurement at building's reference/calibration state can be used to improve the model prediction accuracies at a different structural state, e.g., damaged structure. Also, the effects of prediction error bias on the uncertainty of the predicted values is studied. The test structure considered here is a ten-story concrete building located in Utica, NY. The modal parameters of the building at its reference state are identified from ambient vibration data and used to calibrate parameters of the initial FE model as well as the error functions. Before demolishing the building, six of its exterior walls were removed and ambient vibration measurements were also collected from the structure after the wall removal. These data are not used to calibrate the model; they are only used to assess the predicted results. The model updating framework proposed in this paper is applied to estimate the modal parameters of the building at its reference state as well as two damaged states: moderate damage (removal of four walls) and severe damage (removal of six walls). Good agreement is observed between the model-predicted modal parameters and those identified from vibration tests. Moreover, it is shown that including prediction error bias in the updating process instead of commonly-used zero-mean error function can significantly reduce the prediction uncertainties.

Determining Representative Elementary Volume For Multiple Petrophysical Parameters using a Convex Hull Analysis of Digital Rock Data

NASA Astrophysics Data System (ADS)

Shah, S.; Gray, F.; Yang, J.; Crawshaw, J.; Boek, E.

2016-12-01

Advances in 3D pore-scale imaging and computational methods have allowed an exceptionally detailed quantitative and qualitative analysis of the fluid flow in complex porous media. A fundamental problem in pore-scale imaging and modelling is how to represent and model the range of scales encountered in porous media, starting from the smallest pore spaces. In this study, a novel method is presented for determining the representative elementary volume (REV) of a rock for several parameters simultaneously. We calculate the two main macroscopic petrophysical parameters, porosity and single-phase permeability, using micro CT imaging and Lattice Boltzmann (LB) simulations for 14 different porous media, including sandpacks, sandstones and carbonates. The concept of the `Convex Hull' is then applied to calculate the REV for both parameters simultaneously using a plot of the area of the convex hull as a function of the sub-volume, capturing the different scales of heterogeneity from the pore-scale imaging. The results also show that the area of the convex hull (for well-chosen parameters such as the log of the permeability and the porosity) decays exponentially with sub-sample size suggesting a computationally efficient way to determine the system size needed to calculate the parameters to high accuracy (small convex hull area). Finally we propose using a characteristic length such as the pore size to choose an efficient absolute voxel size for the numerical rock.

EPR, optical and modeling of Mn(2+) doped sarcosinium oxalate monohydrate.

PubMed

Kripal, Ram; Singh, Manju

2015-01-25

Electron paramagnetic resonance (EPR) study of Mn(2+) ions doped in sarcosinium oxalate monohydrate (SOM) single crystal is done at liquid nitrogen temperature (LNT). EPR spectrum shows a bunch of five fine structure lines and further they split into six hyperfine components. Only one interstitial site was observed. With the help of EPR spectra the spin Hamiltonian parameters including zero field splitting (ZFS) parameters are evaluated. The optical absorption study at room temperature is also done in the wavelength range 195-1100 nm. From this study cubic crystal field splitting parameter, Dq=730 cm(-1) and Racah inter-electronic repulsion parameters B=792 cm(-1), C=2278 cm(-1) are determined. ZFS parameters D and E are also calculated using crystal field parameters from superposition model and microscopic spin Hamiltonian theory. The calculated ZFS parameter values are in good match with the experimental values obtained by EPR. Copyright © 2014 Elsevier B.V. All rights reserved.

On the correlations between the polyhedron eccentricity parameters and the bond-valence sums for the cations with one lone electron pair.

PubMed

Sidey, Vasyl

2008-08-01

Applicability of the Wang-Liebau polyhedron eccentricity parameter in the bond-valence model [Wang & Liebau (2007). Acta Cryst. B63, 216-228] has been found to be doubtful: the correlations between the values of the polyhedron eccentricity parameters and the bond-valence sums calculated for the cations with one lone electron pair are probably an artifact of the poorly determined bond-valence parameters.

New theory of stellar convection without the mixing-length parameter: new stellar atmosphere model

NASA Astrophysics Data System (ADS)

Pasetto, Stefano; Chiosi, Cesare; Cropper, Mark; Grebel, Eva K.

2018-01-01

Stellar convection is usually described by the mixing-length theory, which makes use of the mixing-length scale factor to express the convective flux, velocity, and temperature gradients of the convective elements and stellar medium. The mixing-length scale is proportional to the local pressure scale height of the star, and the proportionality factor (i.e. mixing-length parameter) is determined by comparing the stellar models to some calibrator, i.e. the Sun. No strong arguments exist to suggest that the mixing-length parameter is the same in all stars and all evolutionary phases and because of this, all stellar models in the literature are hampered by this basic uncertainty. In a recent paper [1] we presented a new theory that does not require the mixing length parameter. Our self-consistent analytical formulation of stellar convection determines all the properties of stellar convection as a function of the physical behavior of the convective elements themselves and the surrounding medium. The new theory of stellar convection is formulated starting from a conventional solution of the Navier-Stokes/Euler equations expressed in a non-inertial reference frame co-moving with the convective elements. The motion of stellar convective cells inside convective-unstable layers is fully determined by a new system of equations for convection in a non-local and time-dependent formalism. The predictions of the new theory are compared with those from the standard mixing-length paradigm with positive results for atmosphere models of the Sun and all the stars in the Hertzsprung-Russell diagram.

Use of differential scanning calorimetry to detect canola oil (Brassica napus L.) adulterated with lard stearin.

PubMed

Marikkar, Jalaldeen Mohammed Nazrim; Rana, Sohel

2014-01-01

A study was conducted to detect and quantify lard stearin (LS) content in canola oil (CaO) using differential scanning calorimetry (DSC). Authentic samples of CaO were obtained from a reliable supplier and the adulterant LS were obtained through a fractional crystallization procedure as reported previously. Pure CaO samples spiked with LS in levels ranging from 5 to 15% (w/w) were analyzed using DSC to obtain their cooling and heating profiles. The results showed that samples contaminated with LS at 5% (w/w) level can be detected using characteristic contaminant peaks appearing in the higher temperature regions (0 to 70°C) of the cooling and heating curves. Pearson correlation analysis of LS content against individual DSC parameters of the adulterant peak namely peak temperature, peak area, peak onset temperature indicated that there were strong correlations between these with the LS content of the CaO admixtures. When these three parameters were engaged as variables in the execution of the stepwise regression procedure, predictive models for determination of LS content in CaO were obtained. The predictive models obtained with single DSC parameter had relatively lower coefficient of determination (R(2) value) and higher standard error than the models obtained using two DSC parameters in combination. This study concluded that the predictive models obtained with peak area and peak onset temperature of the adulteration peak would be more accurate for prediction of LS content in CaO based on the highest coefficient of determination (R(2) value) and smallest standard error.

A feasibility investigation for modeling and optimization of temperature in bone drilling using fuzzy logic and Taguchi optimization methodology.

PubMed

Pandey, Rupesh Kumar; Panda, Sudhansu Sekhar

2014-11-01

Drilling of bone is a common procedure in orthopedic surgery to produce hole for screw insertion to fixate the fracture devices and implants. The increase in temperature during such a procedure increases the chances of thermal invasion of bone which can cause thermal osteonecrosis resulting in the increase of healing time or reduction in the stability and strength of the fixation. Therefore, drilling of bone with minimum temperature is a major challenge for orthopedic fracture treatment. This investigation discusses the use of fuzzy logic and Taguchi methodology for predicting and minimizing the temperature produced during bone drilling. The drilling experiments have been conducted on bovine bone using Taguchi's L25 experimental design. A fuzzy model is developed for predicting the temperature during orthopedic drilling as a function of the drilling process parameters (point angle, helix angle, feed rate and cutting speed). Optimum bone drilling process parameters for minimizing the temperature are determined using Taguchi method. The effect of individual cutting parameters on the temperature produced is evaluated using analysis of variance. The fuzzy model using triangular and trapezoidal membership predicts the temperature within a maximum error of ±7%. Taguchi analysis of the obtained results determined the optimal drilling conditions for minimizing the temperature as A3B5C1.The developed system will simplify the tedious task of modeling and determination of the optimal process parameters to minimize the bone drilling temperature. It will reduce the risk of thermal osteonecrosis and can be very effective for the online condition monitoring of the process. © IMechE 2014.

Determining Spacecraft Reaction Wheel Friction Parameters

NASA Technical Reports Server (NTRS)

Sarani, Siamak

2009-01-01

Software was developed to characterize the drag in each of the Cassini spacecraft's Reaction Wheel Assemblies (RWAs) to determine the RWA friction parameters. This tool measures the drag torque of RWAs for not only the high spin rates (greater than 250 RPM), but also the low spin rates (less than 250 RPM) where there is a lack of an elastohydrodynamic boundary layer in the bearings. RWA rate and drag torque profiles as functions of time are collected via telemetry once every 4 seconds and once every 8 seconds, respectively. Intermediate processing steps single-out the coast-down regions. A nonlinear model for the drag torque as a function of RWA spin rate is incorporated in order to characterize the low spin rate regime. The tool then uses a nonlinear parameter optimization algorithm based on the Nelder-Mead simplex method to determine the viscous coefficient, the Dahl friction, and the two parameters that account for the low spin-rate behavior.

Culture and Social Relationship as Factors of Affecting Communicative Non-verbal Behaviors

NASA Astrophysics Data System (ADS)

Akhter Lipi, Afia; Nakano, Yukiko; Rehm, Mathias

The goal of this paper is to link a bridge between social relationship and cultural variation to predict conversants' non-verbal behaviors. This idea serves as a basis of establishing a parameter based socio-cultural model, which determines non-verbal expressive parameters that specify the shapes of agent's nonverbal behaviors in HAI. As the first step, a comparative corpus analysis is done for two cultures in two specific social relationships. Next, by integrating the cultural and social parameters factors with the empirical data from corpus analysis, we establish a model that predicts posture. The predictions from our model successfully demonstrate that both cultural background and social relationship moderate communicative non-verbal behaviors.

Least-Squares Self-Calibration of Imaging Array Data

NASA Technical Reports Server (NTRS)

Arendt, R. G.; Moseley, S. H.; Fixsen, D. J.

2004-01-01

When arrays are used to collect multiple appropriately-dithered images of the same region of sky, the resulting data set can be calibrated using a least-squares minimization procedure that determines the optimal fit between the data and a model of that data. The model parameters include the desired sky intensities as well as instrument parameters such as pixel-to-pixel gains and offsets. The least-squares solution simultaneously provides the formal error estimates for the model parameters. With a suitable observing strategy, the need for separate calibration observations is reduced or eliminated. We show examples of this calibration technique applied to HST NICMOS observations of the Hubble Deep Fields and simulated SIRTF IRAC observations.

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

Sepke, Scott M.

In this note, the laser focal plane intensity pro le for a beam modeled using the 3D ray trace package in HYDRA is determined. First, the analytical model is developed followed by a practical numerical model for evaluating the resulting computationally intensive normalization factor for all possible input parameters.

Simple Spreadsheet Models For Interpretation Of Fractured Media Tracer Tests

EPA Science Inventory

An analysis of a gas-phase partitioning tracer test conducted through fractured media is discussed within this paper. The analysis employed matching eight simple mathematical models to the experimental data to determine transport parameters. All of the models tested; two porous...

How to determine spiral bevel gear tooth geometry for finite element analysis

NASA Technical Reports Server (NTRS)

Handschuh, Robert F.; Litvin, Faydor L.

1991-01-01

An analytical method was developed to determine gear tooth surface coordinates of face milled spiral bevel gears. The method combines the basic gear design parameters with the kinematical aspects for spiral bevel gear manufacturing. A computer program was developed to calculate the surface coordinates. From this data a 3-D model for finite element analysis can be determined. Development of the modeling method and an example case are presented.

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain.

PubMed

Aerts, Hannelore; Schirner, Michael; Jeurissen, Ben; Van Roost, Dirk; Achten, Eric; Ritter, Petra; Marinazzo, Daniele

2018-01-01

Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, noninvasive neuroimaging techniques such as functional MRI and diffusion-weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex nonlinear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics. As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong-Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.

Optimisation of dispersion parameters of Gaussian plume model for CO₂ dispersion.

PubMed

Liu, Xiong; Godbole, Ajit; Lu, Cheng; Michal, Guillaume; Venton, Philip

2015-11-01

The carbon capture and storage (CCS) and enhanced oil recovery (EOR) projects entail the possibility of accidental release of carbon dioxide (CO2) into the atmosphere. To quantify the spread of CO2 following such release, the 'Gaussian' dispersion model is often used to estimate the resulting CO2 concentration levels in the surroundings. The Gaussian model enables quick estimates of the concentration levels. However, the traditionally recommended values of the 'dispersion parameters' in the Gaussian model may not be directly applicable to CO2 dispersion. This paper presents an optimisation technique to obtain the dispersion parameters in order to achieve a quick estimation of CO2 concentration levels in the atmosphere following CO2 blowouts. The optimised dispersion parameters enable the Gaussian model to produce quick estimates of CO2 concentration levels, precluding the necessity to set up and run much more complicated models. Computational fluid dynamics (CFD) models were employed to produce reference CO2 dispersion profiles in various atmospheric stability classes (ASC), different 'source strengths' and degrees of ground roughness. The performance of the CFD models was validated against the 'Kit Fox' field measurements, involving dispersion over a flat horizontal terrain, both with low and high roughness regions. An optimisation model employing a genetic algorithm (GA) to determine the best dispersion parameters in the Gaussian plume model was set up. Optimum values of the dispersion parameters for different ASCs that can be used in the Gaussian plume model for predicting CO2 dispersion were obtained.

Assessing the importance of self-regulating mechanisms in diamondback moth population dynamics: application of discrete mathematical models.

PubMed

Nedorezov, Lev V; Löhr, Bernhard L; Sadykova, Dinara L

2008-10-07

The applicability of discrete mathematical models for the description of diamondback moth (DBM) (Plutella xylostella L.) population dynamics was investigated. The parameter values for several well-known discrete time models (Skellam, Moran-Ricker, Hassell, Maynard Smith-Slatkin, and discrete logistic models) were estimated for an experimental time series from a highland cabbage-growing area in eastern Kenya. For all sets of parameters, boundaries of confidence domains were determined. Maximum calculated birth rates varied between 1.086 and 1.359 when empirical values were used for parameter estimation. After fitting of the models to the empirical trajectory, all birth rate values resulted considerably higher (1.742-3.526). The carrying capacity was determined between 13.0 and 39.9DBM/plant, after fitting of the models these values declined to 6.48-9.3, all values well within the range encountered empirically. The application of the Durbin-Watson criteria for comparison of theoretical and experimental population trajectories produced negative correlations with all models. A test of residual value groupings for randomness showed that their distribution is non-stochastic. In consequence, we conclude that DBM dynamics cannot be explained as a result of intra-population self-regulative mechanisms only (=by any of the models tested) and that more comprehensive models are required for the explanation of DBM population dynamics.

Delineating parameter unidentifiabilities in complex models

NASA Astrophysics Data System (ADS)

Raman, Dhruva V.; Anderson, James; Papachristodoulou, Antonis

2017-03-01

Scientists use mathematical modeling as a tool for understanding and predicting the properties of complex physical systems. In highly parametrized models there often exist relationships between parameters over which model predictions are identical, or nearly identical. These are known as structural or practical unidentifiabilities, respectively. They are hard to diagnose and make reliable parameter estimation from data impossible. They furthermore imply the existence of an underlying model simplification. We describe a scalable method for detecting unidentifiabilities, as well as the functional relations defining them, for generic models. This allows for model simplification, and appreciation of which parameters (or functions thereof) cannot be estimated from data. Our algorithm can identify features such as redundant mechanisms and fast time-scale subsystems, as well as the regimes in parameter space over which such approximations are valid. We base our algorithm on a quantification of regional parametric sensitivity that we call `multiscale sloppiness'. Traditionally, the link between parametric sensitivity and the conditioning of the parameter estimation problem is made locally, through the Fisher information matrix. This is valid in the regime of infinitesimal measurement uncertainty. We demonstrate the duality between multiscale sloppiness and the geometry of confidence regions surrounding parameter estimates made where measurement uncertainty is non-negligible. Further theoretical relationships are provided linking multiscale sloppiness to the likelihood-ratio test. From this, we show that a local sensitivity analysis (as typically done) is insufficient for determining the reliability of parameter estimation, even with simple (non)linear systems. Our algorithm can provide a tractable alternative. We finally apply our methods to a large-scale, benchmark systems biology model of necrosis factor (NF)-κ B , uncovering unidentifiabilities.

Systematic parameter inference in stochastic mesoscopic modeling

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

Lei, Huan; Yang, Xiu; Li, Zhen

2017-02-01

We propose a method to efficiently determine the optimal coarse-grained force field in mesoscopic stochastic simulations of Newtonian fluid and polymer melt systems modeled by dissipative particle dynamics (DPD) and energy conserving dissipative particle dynamics (eDPD). The response surfaces of various target properties (viscosity, diffusivity, pressure, etc.) with respect to model parameters are constructed based on the generalized polynomial chaos (gPC) expansion using simulation results on sampling points (e.g., individual parameter sets). To alleviate the computational cost to evaluate the target properties, we employ the compressive sensing method to compute the coefficients of the dominant gPC terms given the priormore » knowledge that the coefficients are “sparse”. The proposed method shows comparable accuracy with the standard probabilistic collocation method (PCM) while it imposes a much weaker restriction on the number of the simulation samples especially for systems with high dimensional parametric space. Fully access to the response surfaces within the confidence range enables us to infer the optimal force parameters given the desirable values of target properties at the macroscopic scale. Moreover, it enables us to investigate the intrinsic relationship between the model parameters, identify possible degeneracies in the parameter space, and optimize the model by eliminating model redundancies. The proposed method provides an efficient alternative approach for constructing mesoscopic models by inferring model parameters to recover target properties of the physics systems (e.g., from experimental measurements), where those force field parameters and formulation cannot be derived from the microscopic level in a straight forward way.« less

Computer-aided method for the determination of Hansen solubility parameters. Application to the miscibility of refrigerating lubricant and new refrigerant

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

Remigy, J.C.; Nakache, E.; Brechot, P.D.

This article presents a method which allows one to find the Hansen solubility parameters by means of data processing. In the first part, the authors present the thermodynamical principle of Hansen parameters, and then they explain the model used to find parameters from experimental data. They validate the method by studying the solubility parameters of CFC-12 (dichlorodifluoromethane), HFC-134a (1,1,1,2-tetrafluoroethane), neopentylglycol esters, trimethylolpropane esters, dipentaerythritol esters, and pentaerythritol esters. Then, the variation of Hansen parameters are studied as well as the relation between the miscibility temperature (the temperature at which a blend passes from the miscible state to the immiscible state)more » and the interaction distance. The authors establish the critical interaction distance of HFC-134a which determines the solubility limit and they study its variation with temperature.« less

Sensitivity analysis of helicopter IMC decelerating steep approach and landing performance to navigation system parameters

NASA Technical Reports Server (NTRS)

Karmali, M. S.; Phatak, A. V.

1982-01-01

Results of a study to investigate, by means of a computer simulation, the performance sensitivity of helicopter IMC DSAL operations as a function of navigation system parameters are presented. A mathematical model representing generically a navigation system is formulated. The scenario simulated consists of a straight in helicopter approach to landing along a 6 deg glideslope. The deceleration magnitude chosen is 03g. The navigation model parameters are varied and the statistics of the total system errors (TSE) computed. These statistics are used to determine the critical navigation system parameters that affect the performance of the closed-loop navigation, guidance and control system of a UH-1H helicopter.

Toward Scientific Numerical Modeling

NASA Technical Reports Server (NTRS)

Kleb, Bil

2007-01-01

Ultimately, scientific numerical models need quantified output uncertainties so that modeling can evolve to better match reality. Documenting model input uncertainties and verifying that numerical models are translated into code correctly, however, are necessary first steps toward that goal. Without known input parameter uncertainties, model sensitivities are all one can determine, and without code verification, output uncertainties are simply not reliable. To address these two shortcomings, two proposals are offered: (1) an unobtrusive mechanism to document input parameter uncertainties in situ and (2) an adaptation of the Scientific Method to numerical model development and deployment. Because these two steps require changes in the computational simulation community to bear fruit, they are presented in terms of the Beckhard-Harris-Gleicher change model.

Form drag in rivers due to small-scale natural topographic features: 1. Regular sequences

USGS Publications Warehouse

Kean, J.W.; Smith, J.D.

2006-01-01

Small-scale topographic features are commonly found on the boundaries of natural rivers, streams, and floodplains. A simple method for determining the form drag on these features is presented, and the results of this model are compared to laboratory measurements. The roughness elements are modeled as Gaussian-shaped features defined in terms of three parameters: a protrusion height, H; a streamwise length scale, ??; and a spacing between crests, ??. This shape is shown to be a good approximation to a wide variety of natural topographic bank features. The form drag on an individual roughness element embedded in a series of identical elements is determined using the drag coefficient of the individual element and a reference velocity that includes the effects of roughness elements further upstream. In addition to calculating the drag on each element, the model determines the spatially averaged total stress, skin friction stress, and roughness height of the boundary. The effects of bank roughness on patterns of velocity and boundary shear stress are determined by combining the form drag model with a channel flow model. The combined model shows that drag on small-scale topographic features substantially alters the near-bank flow field. These methods can be used to improve predictions of flow resistance in rivers and to form the basis for fully predictive (no empirically adjusted parameters) channel flow models. They also provide a foundation for calculating the near-bank boundary shear stress fields necessary for determining rates of sediment transport and lateral erosion.

Parameter Estimation and Model Selection in Computational Biology

PubMed Central

Lillacci, Gabriele; Khammash, Mustafa

2010-01-01

A central challenge in computational modeling of biological systems is the determination of the model parameters. Typically, only a fraction of the parameters (such as kinetic rate constants) are experimentally measured, while the rest are often fitted. The fitting process is usually based on experimental time course measurements of observables, which are used to assign parameter values that minimize some measure of the error between these measurements and the corresponding model prediction. The measurements, which can come from immunoblotting assays, fluorescent markers, etc., tend to be very noisy and taken at a limited number of time points. In this work we present a new approach to the problem of parameter selection of biological models. We show how one can use a dynamic recursive estimator, known as extended Kalman filter, to arrive at estimates of the model parameters. The proposed method follows. First, we use a variation of the Kalman filter that is particularly well suited to biological applications to obtain a first guess for the unknown parameters. Secondly, we employ an a posteriori identifiability test to check the reliability of the estimates. Finally, we solve an optimization problem to refine the first guess in case it should not be accurate enough. The final estimates are guaranteed to be statistically consistent with the measurements. Furthermore, we show how the same tools can be used to discriminate among alternate models of the same biological process. We demonstrate these ideas by applying our methods to two examples, namely a model of the heat shock response in E. coli, and a model of a synthetic gene regulation system. The methods presented are quite general and may be applied to a wide class of biological systems where noisy measurements are used for parameter estimation or model selection. PMID:20221262

PeTTSy: a computational tool for perturbation analysis of complex systems biology models.

PubMed

Domijan, Mirela; Brown, Paul E; Shulgin, Boris V; Rand, David A

2016-03-10

Over the last decade sensitivity analysis techniques have been shown to be very useful to analyse complex and high dimensional Systems Biology models. However, many of the currently available toolboxes have either used parameter sampling, been focused on a restricted set of model observables of interest, studied optimisation of a objective function, or have not dealt with multiple simultaneous model parameter changes where the changes can be permanent or temporary. Here we introduce our new, freely downloadable toolbox, PeTTSy (Perturbation Theory Toolbox for Systems). PeTTSy is a package for MATLAB which implements a wide array of techniques for the perturbation theory and sensitivity analysis of large and complex ordinary differential equation (ODE) based models. PeTTSy is a comprehensive modelling framework that introduces a number of new approaches and that fully addresses analysis of oscillatory systems. It examines sensitivity analysis of the models to perturbations of parameters, where the perturbation timing, strength, length and overall shape can be controlled by the user. This can be done in a system-global setting, namely, the user can determine how many parameters to perturb, by how much and for how long. PeTTSy also offers the user the ability to explore the effect of the parameter perturbations on many different types of outputs: period, phase (timing of peak) and model solutions. PeTTSy can be employed on a wide range of mathematical models including free-running and forced oscillators and signalling systems. To enable experimental optimisation using the Fisher Information Matrix it efficiently allows one to combine multiple variants of a model (i.e. a model with multiple experimental conditions) in order to determine the value of new experiments. It is especially useful in the analysis of large and complex models involving many variables and parameters. PeTTSy is a comprehensive tool for analysing large and complex models of regulatory and signalling systems. It allows for simulation and analysis of models under a variety of environmental conditions and for experimental optimisation of complex combined experiments. With its unique set of tools it makes a valuable addition to the current library of sensitivity analysis toolboxes. We believe that this software will be of great use to the wider biological, systems biology and modelling communities.

Formulation, General Features and Global Calibration of a Bioenergetically-Constrained Fishery Model

PubMed Central

Bianchi, Daniele; Galbraith, Eric D.

2017-01-01

Human exploitation of marine resources is profoundly altering marine ecosystems, while climate change is expected to further impact commercially-harvested fish and other species. Although the global fishery is a highly complex system with many unpredictable aspects, the bioenergetic limits on fish production and the response of fishing effort to profit are both relatively tractable, and are sure to play important roles. Here we describe a generalized, coupled biological-economic model of the global marine fishery that represents both of these aspects in a unified framework, the BiOeconomic mArine Trophic Size-spectrum (BOATS) model. BOATS predicts fish production according to size spectra as a function of net primary production and temperature, and dynamically determines harvest spectra from the biomass density and interactive, prognostic fishing effort. Within this framework, the equilibrium fish biomass is determined by the economic forcings of catchability, ex-vessel price and cost per unit effort, while the peak harvest depends on the ecosystem parameters. Comparison of a large ensemble of idealized simulations with observational databases, focusing on historical biomass and peak harvests, allows us to narrow the range of several uncertain ecosystem parameters, rule out most parameter combinations, and select an optimal ensemble of model variants. Compared to the prior distributions, model variants with lower values of the mortality rate, trophic efficiency, and allometric constant agree better with observations. For most acceptable parameter combinations, natural mortality rates are more strongly affected by temperature than growth rates, suggesting different sensitivities of these processes to climate change. These results highlight the utility of adopting large-scale, aggregated data constraints to reduce model parameter uncertainties and to better predict the response of fisheries to human behaviour and climate change. PMID:28103280

Formulation, General Features and Global Calibration of a Bioenergetically-Constrained Fishery Model.

PubMed

Carozza, David A; Bianchi, Daniele; Galbraith, Eric D

2017-01-01

Human exploitation of marine resources is profoundly altering marine ecosystems, while climate change is expected to further impact commercially-harvested fish and other species. Although the global fishery is a highly complex system with many unpredictable aspects, the bioenergetic limits on fish production and the response of fishing effort to profit are both relatively tractable, and are sure to play important roles. Here we describe a generalized, coupled biological-economic model of the global marine fishery that represents both of these aspects in a unified framework, the BiOeconomic mArine Trophic Size-spectrum (BOATS) model. BOATS predicts fish production according to size spectra as a function of net primary production and temperature, and dynamically determines harvest spectra from the biomass density and interactive, prognostic fishing effort. Within this framework, the equilibrium fish biomass is determined by the economic forcings of catchability, ex-vessel price and cost per unit effort, while the peak harvest depends on the ecosystem parameters. Comparison of a large ensemble of idealized simulations with observational databases, focusing on historical biomass and peak harvests, allows us to narrow the range of several uncertain ecosystem parameters, rule out most parameter combinations, and select an optimal ensemble of model variants. Compared to the prior distributions, model variants with lower values of the mortality rate, trophic efficiency, and allometric constant agree better with observations. For most acceptable parameter combinations, natural mortality rates are more strongly affected by temperature than growth rates, suggesting different sensitivities of these processes to climate change. These results highlight the utility of adopting large-scale, aggregated data constraints to reduce model parameter uncertainties and to better predict the response of fisheries to human behaviour and climate change.

Means and Method for Measurement of Drilling Fluid Properties

NASA Astrophysics Data System (ADS)

Lysyannikov, A.; Kondrashov, P.; Pavlova, P.

2016-06-01

The paper addresses the problem on creation of a new design of the device for determining rheological parameters of drilling fluids and the basic requirements which it must meet. The key quantitative parameters that define the developed device are provided. The algorithm of determining the coefficient of the yield point from the rheological Shvedov- Bingam model at a relative speed of rotation of glasses from the investigated drilling fluid of 300 and 600 rpm is presented.

Time-varying parameter models for catchments with land use change: the importance of model structure

NASA Astrophysics Data System (ADS)

Pathiraja, Sahani; Anghileri, Daniela; Burlando, Paolo; Sharma, Ashish; Marshall, Lucy; Moradkhani, Hamid

2018-05-01

Rapid population and economic growth in Southeast Asia has been accompanied by extensive land use change with consequent impacts on catchment hydrology. Modeling methodologies capable of handling changing land use conditions are therefore becoming ever more important and are receiving increasing attention from hydrologists. A recently developed data-assimilation-based framework that allows model parameters to vary through time in response to signals of change in observations is considered for a medium-sized catchment (2880 km2) in northern Vietnam experiencing substantial but gradual land cover change. We investigate the efficacy of the method as well as the importance of the chosen model structure in ensuring the success of a time-varying parameter method. The method was used with two lumped daily conceptual models (HBV and HyMOD) that gave good-quality streamflow predictions during pre-change conditions. Although both time-varying parameter models gave improved streamflow predictions under changed conditions compared to the time-invariant parameter model, persistent biases for low flows were apparent in the HyMOD case. It was found that HyMOD was not suited to representing the modified baseflow conditions, resulting in extreme and unrealistic time-varying parameter estimates. This work shows that the chosen model can be critical for ensuring the time-varying parameter framework successfully models streamflow under changing land cover conditions. It can also be used to determine whether land cover changes (and not just meteorological factors) contribute to the observed hydrologic changes in retrospective studies where the lack of a paired control catchment precludes such an assessment.

Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model

NASA Astrophysics Data System (ADS)

Urrego-Blanco, Jorge R.; Urban, Nathan M.; Hunke, Elizabeth C.; Turner, Adrian K.; Jeffery, Nicole

2016-04-01

Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual model parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance-based approach in which Sobol' sequences are used to efficiently sample the full 39-dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. It is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model.

Surveillance system and method having an operating mode partitioned fault classification model

NASA Technical Reports Server (NTRS)

Bickford, Randall L. (Inventor)

2005-01-01

A system and method which partitions a parameter estimation model, a fault detection model, and a fault classification model for a process surveillance scheme into two or more coordinated submodels together providing improved diagnostic decision making for at least one determined operating mode of an asset.

A model for explaining fusion suppression using classical trajectory method

NASA Astrophysics Data System (ADS)

Phookan, C. K.; Kalita, K.

2015-01-01

We adopt a semi-classical approach for explanation of projectile breakup and above barrier fusion suppression for the reactions 6Li+152Sm and 6Li+144Sm. The cut-off impact parameter for fusion is determined by employing quantum mechanical ideas. Within this cut-off impact parameter for fusion, the fraction of projectiles undergoing breakup is determined using the method of classical trajectory in two-dimensions. For obtaining the initial conditions of the equations of motion, a simplified model of the 6Li nucleus has been proposed. We introduce a simple formula for explanation of fusion suppression. We find excellent agreement between the experimental and calculated fusion cross section. A slight modification of the above formula for fusion suppression is also proposed for a three-dimensional model.

Observational constraints on extended Chaplygin gas cosmologies

NASA Astrophysics Data System (ADS)

Paul, B. C.; Thakur, P.; Saha, A.

2017-08-01

We investigate cosmological models with extended Chaplygin gas (ECG) as a candidate for dark energy and determine the equation of state parameters using observed data namely, observed Hubble data, baryon acoustic oscillation data and cosmic microwave background shift data. Cosmological models are investigated considering cosmic fluid which is an extension of Chaplygin gas, however, it reduces to modified Chaplygin gas (MCG) and also to generalized Chaplygin gas (GCG) in special cases. It is found that in the case of MCG and GCG, the best-fit values of all the parameters are positive. The distance modulus agrees quite well with the experimental Union2 data. The speed of sound obtained in the model is small, necessary for structure formation. We also determine the observational constraints on the constants of the ECG equation.

Extracting survival parameters from isothermal, isobaric, and "iso-concentration" inactivation experiments by the "3 end points method".

PubMed

Corradini, M G; Normand, M D; Newcomer, C; Schaffner, D W; Peleg, M

2009-01-01

Theoretically, if an organism's resistance can be characterized by 3 survival parameters, they can be found by solving 3 simultaneous equations that relate the final survival ratio to the lethal agent's intensity. (For 2 resistance parameters, 2 equations will suffice.) In practice, the inevitable experimental scatter would distort the results of such a calculation or render the method unworkable. Averaging the results obtained with more than 3 final survival ratio triplet combinations, determined in four or more treatments, can remove this impediment. This can be confirmed by the ability of a kinetic inactivation model derived from the averaged parameters to predict survival patterns under conditions not employed in their determination, as demonstrated with published isothermal survival data of Clostridium botulinum spores, isobaric data of Escherichia coli under HPP, and Pseudomonas exposed to hydrogen peroxide. Both the method and the underlying assumption that the inactivation followed a Weibull-Log logistic (WeLL) kinetics were confirmed in this way, indicating that when an appropriate survival model is available, it is possible to predict the entire inactivation curves from several experimental final survival ratios alone. Where applicable, the method could simplify the experimental procedure and lower the cost of microbial resistance determinations. In principle, the methodology can be extended to deteriorative chemical reactions if they too can be characterized by 2 or 3 kinetic parameters.

Probabilistic Mesomechanical Fatigue Model

NASA Technical Reports Server (NTRS)

Tryon, Robert G.

1997-01-01

A probabilistic mesomechanical fatigue life model is proposed to link the microstructural material heterogeneities to the statistical scatter in the macrostructural response. The macrostructure is modeled as an ensemble of microelements. Cracks nucleation within the microelements and grow from the microelements to final fracture. Variations of the microelement properties are defined using statistical parameters. A micromechanical slip band decohesion model is used to determine the crack nucleation life and size. A crack tip opening displacement model is used to determine the small crack growth life and size. Paris law is used to determine the long crack growth life. The models are combined in a Monte Carlo simulation to determine the statistical distribution of total fatigue life for the macrostructure. The modeled response is compared to trends in experimental observations from the literature.

Parameter Estimation of Actuators for Benchmark Active Control Technology (BACT) Wind Tunnel Model with Analysis of Wear and Aerodynamic Loading Effects

NASA Technical Reports Server (NTRS)

Waszak, Martin R.; Fung, Jimmy

1998-01-01

This report describes the development of transfer function models for the trailing-edge and upper and lower spoiler actuators of the Benchmark Active Control Technology (BACT) wind tunnel model for application to control system analysis and design. A simple nonlinear least-squares parameter estimation approach is applied to determine transfer function parameters from frequency response data. Unconstrained quasi-Newton minimization of weighted frequency response error was employed to estimate the transfer function parameters. An analysis of the behavior of the actuators over time to assess the effects of wear and aerodynamic load by using the transfer function models is also presented. The frequency responses indicate consistent actuator behavior throughout the wind tunnel test and only slight degradation in effectiveness due to aerodynamic hinge loading. The resulting actuator models have been used in design, analysis, and simulation of controllers for the BACT to successfully suppress flutter over a wide range of conditions.

Orbit Estimation of Non-Cooperative Maneuvering Spacecraft

DTIC Science & Technology

2015-06-01

only take on values that generate real sigma points; therefore, λ > −n. The additional weighting scheme is outlined in the following equations κ = α2...orbit shapes resulted in a similar model weighting. Additional cases of this orbit type also resulted in heavily weighting smaller η value models. It is...determined using both the symmetric and additional parameters UTs. The best values for the weighting parameters are then compared for each test case

Determination of Indicators of Ecological Change

DTIC Science & Technology

2004-09-01

simultaneously characterized parameters for more than one forest (e.g., Huber and Iroume, 2001; Tobón Marin et al., 2000). As parameters (e.g...necessary to apply the revised model for use in five forest biomes , 2) use the model to predict precipitation interception and compare the measured and...larger interception losses than many other forest biomes . The within plot sampling coefficient of variation, ranging from a study average of 0.11 in

Estimation of Suspended and Dissolved Matter Concentration In Sea Water On Shelves By Satellite Remote Sensing

NASA Astrophysics Data System (ADS)

Pelevin, V.; Rostovtseva, V.

Falling of rivers into the seas or surging in shallow aquatoria cause the violation of the balance between living and dead matter occurring in the open ocean ( Pelevin and Rostovtseva, 2001). That means in littoral arias the one-parameter model of sea waters optical properties developed for the open ocean (Pelevin and Rostovtseva, 1997) is not valid. We suggest to use the three-parameters model of light scattering and absorbing prop- erties of sea water for the most arias on shelves. The three parameters are: the coeffi- cient of light absorption by coloured matter at 500 nm (coloured matter includes both chlorophyll pigments and "yellow substance"), the coefficient of light absorption by suspended matter and the coefficient of light backscattering by suspended matter. For some specific shelf arias with coloured suspended matter we suggest to add the fourth parameter taking into account the spectral dependence of backscattering by suspended matter. The method of such type arias determination is also given. The algorithm of solution of the inverse problem of these parameters estimation using optical remote sensing data obtained from satellites is developed. It consists of two steps: the rough determination of the parameters values by some spectral characteris- tics and then the minimization of real and model spectra discrepancy. The suggested algorithm was used for spectral distribution of upward radiation mea- sured in the Black, Marmora and Baltic Seas. Comparison of the obtained results with some data of direct measurements carried out in these aquatoria proved the validity of the model for these shelf waters and showed the efficiency of the suggested approach. V.N.Pelevin and V.V.Rostovtseva , 1997, Estimation of lightscattering and lightabsorb- ing admixture concentration in open ocean waters of different types.- Atmospheric and Oceanic Optics, 10(9), 989-995. V.N.Pelevin and V.V.Rostovtseva, 2001, Modelling of optic- biological parameters of open ocean waters. - OCEANOLOGIA, 43(4).

Reliable results from stochastic simulation models

Treesearch

Donald L., Jr. Gochenour; Leonard R. Johnson

1973-01-01

Development of a computer simulation model is usually done without fully considering how long the model should run (e.g. computer time) before the results are reliable. However construction of confidence intervals (CI) about critical output parameters from the simulation model makes it possible to determine the point where model results are reliable. If the results are...

Probability Distribution Estimated From the Minimum, Maximum, and Most Likely Values: Applied to Turbine Inlet Temperature Uncertainty

NASA Technical Reports Server (NTRS)

Holland, Frederic A., Jr.

2004-01-01

Modern engineering design practices are tending more toward the treatment of design parameters as random variables as opposed to fixed, or deterministic, values. The probabilistic design approach attempts to account for the uncertainty in design parameters by representing them as a distribution of values rather than as a single value. The motivations for this effort include preventing excessive overdesign as well as assessing and assuring reliability, both of which are important for aerospace applications. However, the determination of the probability distribution is a fundamental problem in reliability analysis. A random variable is often defined by the parameters of the theoretical distribution function that gives the best fit to experimental data. In many cases the distribution must be assumed from very limited information or data. Often the types of information that are available or reasonably estimated are the minimum, maximum, and most likely values of the design parameter. For these situations the beta distribution model is very convenient because the parameters that define the distribution can be easily determined from these three pieces of information. Widely used in the field of operations research, the beta model is very flexible and is also useful for estimating the mean and standard deviation of a random variable given only the aforementioned three values. However, an assumption is required to determine the four parameters of the beta distribution from only these three pieces of information (some of the more common distributions, like the normal, lognormal, gamma, and Weibull distributions, have two or three parameters). The conventional method assumes that the standard deviation is a certain fraction of the range. The beta parameters are then determined by solving a set of equations simultaneously. A new method developed in-house at the NASA Glenn Research Center assumes a value for one of the beta shape parameters based on an analogy with the normal distribution (ref.1). This new approach allows for a very simple and direct algebraic solution without restricting the standard deviation. The beta parameters obtained by the new method are comparable to the conventional method (and identical when the distribution is symmetrical). However, the proposed method generally produces a less peaked distribution with a slightly larger standard deviation (up to 7 percent) than the conventional method in cases where the distribution is asymmetric or skewed. The beta distribution model has now been implemented into the Fast Probability Integration (FPI) module used in the NESSUS computer code for probabilistic analyses of structures (ref. 2).

Maximum likelihood-based analysis of single-molecule photon arrival trajectories.

PubMed

Hajdziona, Marta; Molski, Andrzej

2011-02-07

In this work we explore the statistical properties of the maximum likelihood-based analysis of one-color photon arrival trajectories. This approach does not involve binning and, therefore, all of the information contained in an observed photon strajectory is used. We study the accuracy and precision of parameter estimates and the efficiency of the Akaike information criterion and the Bayesian information criterion (BIC) in selecting the true kinetic model. We focus on the low excitation regime where photon trajectories can be modeled as realizations of Markov modulated Poisson processes. The number of observed photons is the key parameter in determining model selection and parameter estimation. For example, the BIC can select the true three-state model from competing two-, three-, and four-state kinetic models even for relatively short trajectories made up of 2 × 10(3) photons. When the intensity levels are well-separated and 10(4) photons are observed, the two-state model parameters can be estimated with about 10% precision and those for a three-state model with about 20% precision.

Development of quantitative radioactive methodologies on paper to determine important lateral-flow immunoassay parameters.

PubMed

Mosley, Garrett L; Nguyen, Phuong; Wu, Benjamin M; Kamei, Daniel T

2016-08-07

The lateral-flow immunoassay (LFA) is a well-established diagnostic technology that has recently seen significant advancements due in part to the rapidly expanding fields of paper diagnostics and paper-fluidics. As LFA-based diagnostics become more complex, it becomes increasingly important to quantitatively determine important parameters during the design and evaluation process. However, current experimental methods for determining these parameters have certain limitations when applied to LFA systems. In this work, we describe our novel methods of combining paper and radioactive measurements to determine nanoprobe molarity, the number of antibodies per nanoprobe, and the forward and reverse rate constants for nanoprobe binding to immobilized target on the LFA test line. Using a model LFA system that detects for the presence of the protein transferrin (Tf), we demonstrate the application of our methods, which involve quantitative experimentation and mathematical modeling. We also compare the results of our rate constant experiments with traditional experiments to demonstrate how our methods more appropriately capture the influence of the LFA environment on the binding interaction. Our novel experimental approaches can therefore more efficiently guide the research process for LFA design, leading to more rapid advancement of the field of paper-based diagnostics.

Extended behavioural modelling of FET and lattice-mismatched HEMT devices

NASA Astrophysics Data System (ADS)

Khawam, Yahya; Albasha, Lutfi

2017-07-01

This study presents an improved large signal model that can be used for high electron mobility transistors (HEMTs) and field effect transistors using measurement-based behavioural modelling techniques. The steps for accurate large and small signal modelling for transistor are also discussed. The proposed DC model is based on the Fager model since it compensates between the number of model's parameters and accuracy. The objective is to increase the accuracy of the drain-source current model with respect to any change in gate or drain voltages. Also, the objective is to extend the improved DC model to account for soft breakdown and kink effect found in some variants of HEMT devices. A hybrid Newton's-Genetic algorithm is used in order to determine the unknown parameters in the developed model. In addition to accurate modelling of a transistor's DC characteristics, the complete large signal model is modelled using multi-bias s-parameter measurements. The way that the complete model is performed is by using a hybrid multi-objective optimisation technique (Non-dominated Sorting Genetic Algorithm II) and local minimum search (multivariable Newton's method) for parasitic elements extraction. Finally, the results of DC modelling and multi-bias s-parameters modelling are presented, and three-device modelling recommendations are discussed.

Identifying parameter regions for multistationarity

PubMed Central

Conradi, Carsten; Mincheva, Maya; Wiuf, Carsten

2017-01-01

Mathematical modelling has become an established tool for studying the dynamics of biological systems. Current applications range from building models that reproduce quantitative data to identifying systems with predefined qualitative features, such as switching behaviour, bistability or oscillations. Mathematically, the latter question amounts to identifying parameter values associated with a given qualitative feature. We introduce a procedure to partition the parameter space of a parameterized system of ordinary differential equations into regions for which the system has a unique or multiple equilibria. The procedure is based on the computation of the Brouwer degree, and it creates a multivariate polynomial with parameter depending coefficients. The signs of the coefficients determine parameter regions with and without multistationarity. A particular strength of the procedure is the avoidance of numerical analysis and parameter sampling. The procedure consists of a number of steps. Each of these steps might be addressed algorithmically using various computer programs and available software, or manually. We demonstrate our procedure on several models of gene transcription and cell signalling, and show that in many cases we obtain a complete partitioning of the parameter space with respect to multistationarity. PMID:28972969

Atomic Radius and Charge Parameter Uncertainty in Biomolecular Solvation Energy Calculations

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

Yang, Xiu; Lei, Huan; Gao, Peiyuan

Atomic radii and charges are two major parameters used in implicit solvent electrostatics and energy calculations. The optimization problem for charges and radii is under-determined, leading to uncertainty in the values of these parameters and in the results of solvation energy calculations using these parameters. This paper presents a method for quantifying this uncertainty in solvation energies using surrogate models based on generalized polynomial chaos (gPC) expansions. There are relatively few atom types used to specify radii parameters in implicit solvation calculations; therefore, surrogate models for these low-dimensional spaces could be constructed using least-squares fitting. However, there are many moremore » types of atomic charges; therefore, construction of surrogate models for the charge parameter space required compressed sensing combined with an iterative rotation method to enhance problem sparsity. We present results for the uncertainty in small molecule solvation energies based on these approaches. Additionally, we explore the correlation between uncertainties due to radii and charges which motivates the need for future work in uncertainty quantification methods for high-dimensional parameter spaces.« less

Assessing the relative importance of parameter and forcing uncertainty and their interactions in conceptual hydrological model simulations

NASA Astrophysics Data System (ADS)

Mockler, E. M.; Chun, K. P.; Sapriza-Azuri, G.; Bruen, M.; Wheater, H. S.

2016-11-01

Predictions of river flow dynamics provide vital information for many aspects of water management including water resource planning, climate adaptation, and flood and drought assessments. Many of the subjective choices that modellers make including model and criteria selection can have a significant impact on the magnitude and distribution of the output uncertainty. Hydrological modellers are tasked with understanding and minimising the uncertainty surrounding streamflow predictions before communicating the overall uncertainty to decision makers. Parameter uncertainty in conceptual rainfall-runoff models has been widely investigated, and model structural uncertainty and forcing data have been receiving increasing attention. This study aimed to assess uncertainties in streamflow predictions due to forcing data and the identification of behavioural parameter sets in 31 Irish catchments. By combining stochastic rainfall ensembles and multiple parameter sets for three conceptual rainfall-runoff models, an analysis of variance model was used to decompose the total uncertainty in streamflow simulations into contributions from (i) forcing data, (ii) identification of model parameters and (iii) interactions between the two. The analysis illustrates that, for our subjective choices, hydrological model selection had a greater contribution to overall uncertainty, while performance criteria selection influenced the relative intra-annual uncertainties in streamflow predictions. Uncertainties in streamflow predictions due to the method of determining parameters were relatively lower for wetter catchments, and more evenly distributed throughout the year when the Nash-Sutcliffe Efficiency of logarithmic values of flow (lnNSE) was the evaluation criterion.

R programming for parameters estimation of geographically weighted ordinal logistic regression (GWOLR) model based on Newton Raphson

NASA Astrophysics Data System (ADS)

Zuhdi, Shaifudin; Saputro, Dewi Retno Sari

2017-03-01

GWOLR model used for represent relationship between dependent variable has categories and scale of category is ordinal with independent variable influenced the geographical location of the observation site. Parameters estimation of GWOLR model use maximum likelihood provide system of nonlinear equations and hard to be found the result in analytic resolution. By finishing it, it means determine the maximum completion, this thing associated with optimizing problem. The completion nonlinear system of equations optimize use numerical approximation, which one is Newton Raphson method. The purpose of this research is to make iteration algorithm Newton Raphson and program using R software to estimate GWOLR model. Based on the research obtained that program in R can be used to estimate the parameters of GWOLR model by forming a syntax program with command "while".

Spectroscopic properties of a two-dimensional time-dependent Cepheid model. II. Determination of stellar parameters and abundances

NASA Astrophysics Data System (ADS)

Vasilyev, V.; Ludwig, H.-G.; Freytag, B.; Lemasle, B.; Marconi, M.

2018-03-01

Context. Standard spectroscopic analyses of variable stars are based on hydrostatic 1D model atmospheres. This quasi-static approach has not been theoretically validated. Aim. We aim at investigating the validity of the quasi-static approximation for Cepheid variables. We focus on the spectroscopic determination of the effective temperature Teff, surface gravity log g, microturbulent velocity ξt, and a generic metal abundance log A, here taken as iron. Methods: We calculated a grid of 1D hydrostatic plane-parallel models covering the ranges in effective temperature and gravity that are encountered during the evolution of a 2D time-dependent envelope model of a Cepheid computed with the radiation-hydrodynamics code CO5BOLD. We performed 1D spectral syntheses for artificial iron lines in local thermodynamic equilibrium by varying the microturbulent velocity and abundance. We fit the resulting equivalent widths to corresponding values obtained from our dynamical model for 150 instances in time, covering six pulsational cycles. In addition, we considered 99 instances during the initial non-pulsating stage of the temporal evolution of the 2D model. In the most general case, we treated Teff, log g, ξt, and log A as free parameters, and in two more limited cases, we fixed Teff and log g by independent constraints. We argue analytically that our approach of fitting equivalent widths is closely related to current standard procedures focusing on line-by-line abundances. Results: For the four-parametric case, the stellar parameters are typically underestimated and exhibit a bias in the iron abundance of ≈-0.2 dex. To avoid biases of this type, it is favorable to restrict the spectroscopic analysis to photometric phases ϕph ≈ 0.3…0.65 using additional information to fix the effective temperature and surface gravity. Conclusions: Hydrostatic 1D model atmospheres can provide unbiased estimates of stellar parameters and abundances of Cepheid variables for particular phases of their pulsations. We identified convective inhomogeneities as the main driver behind potential biases. To obtain a complete view on the effects when determining stellar parameters with 1D models, multidimensional Cepheid atmosphere models are necessary for variables of longer period than investigated here.