Sensitivity Analysis of Simulation Models
Kleijnen, J.P.C.
2009-01-01
This contribution presents an overview of sensitivity analysis of simulation models, including the estimation of gradients. It covers classic designs and their corresponding (meta)models; namely, resolution-III designs including fractional-factorial two-level designs for first-order polynomial
LBLOCA sensitivity analysis using meta models
International Nuclear Information System (INIS)
Villamizar, M.; Sanchez-Saez, F.; Villanueva, J.F.; Carlos, S.; Sanchez, A.I.; Martorell, S.
2014-01-01
This paper presents an approach to perform the sensitivity analysis of the results of simulation of thermal hydraulic codes within a BEPU approach. Sensitivity analysis is based on the computation of Sobol' indices that makes use of a meta model, It presents also an application to a Large-Break Loss of Coolant Accident, LBLOCA, in the cold leg of a pressurized water reactor, PWR, addressing the results of the BEMUSE program and using the thermal-hydraulic code TRACE. (authors)
Sensitivity Analysis in Sequential Decision Models.
Chen, Qiushi; Ayer, Turgay; Chhatwal, Jagpreet
2017-02-01
Sequential decision problems are frequently encountered in medical decision making, which are commonly solved using Markov decision processes (MDPs). Modeling guidelines recommend conducting sensitivity analyses in decision-analytic models to assess the robustness of the model results against the uncertainty in model parameters. However, standard methods of conducting sensitivity analyses cannot be directly applied to sequential decision problems because this would require evaluating all possible decision sequences, typically in the order of trillions, which is not practically feasible. As a result, most MDP-based modeling studies do not examine confidence in their recommended policies. In this study, we provide an approach to estimate uncertainty and confidence in the results of sequential decision models. First, we provide a probabilistic univariate method to identify the most sensitive parameters in MDPs. Second, we present a probabilistic multivariate approach to estimate the overall confidence in the recommended optimal policy considering joint uncertainty in the model parameters. We provide a graphical representation, which we call a policy acceptability curve, to summarize the confidence in the optimal policy by incorporating stakeholders' willingness to accept the base case policy. For a cost-effectiveness analysis, we provide an approach to construct a cost-effectiveness acceptability frontier, which shows the most cost-effective policy as well as the confidence in that for a given willingness to pay threshold. We demonstrate our approach using a simple MDP case study. We developed a method to conduct sensitivity analysis in sequential decision models, which could increase the credibility of these models among stakeholders.
Sensitivity analysis of a modified energy model
International Nuclear Information System (INIS)
Suganthi, L.; Jagadeesan, T.R.
1997-01-01
Sensitivity analysis is carried out to validate model formulation. A modified model has been developed to predict the future energy requirement of coal, oil and electricity, considering price, income, technological and environmental factors. The impact and sensitivity of the independent variables on the dependent variable are analysed. The error distribution pattern in the modified model as compared to a conventional time series model indicated the absence of clusters. The residual plot of the modified model showed no distinct pattern of variation. The percentage variation of error in the conventional time series model for coal and oil ranges from -20% to +20%, while for electricity it ranges from -80% to +20%. However, in the case of the modified model the percentage variation in error is greatly reduced - for coal it ranges from -0.25% to +0.15%, for oil -0.6% to +0.6% and for electricity it ranges from -10% to +10%. The upper and lower limit consumption levels at 95% confidence is determined. The consumption at varying percentage changes in price and population are analysed. The gap between the modified model predictions at varying percentage changes in price and population over the years from 1990 to 2001 is found to be increasing. This is because of the increasing rate of energy consumption over the years and also the confidence level decreases as the projection is made far into the future. (author)
Sensitivity Analysis of a Physiochemical Interaction Model ...
African Journals Online (AJOL)
In this analysis, we will study the sensitivity analysis due to a variation of the initial condition and experimental time. These results which we have not seen elsewhere are analysed and discussed quantitatively. Keywords: Passivation Rate, Sensitivity Analysis, ODE23, ODE45 J. Appl. Sci. Environ. Manage. June, 2012, Vol.
Sensitivity analysis of Smith's AMRV model
International Nuclear Information System (INIS)
Ho, Chih-Hsiang
1995-01-01
Multiple-expert hazard/risk assessments have considerable precedent, particularly in the Yucca Mountain site characterization studies. In this paper, we present a Bayesian approach to statistical modeling in volcanic hazard assessment for the Yucca Mountain site. Specifically, we show that the expert opinion on the site disruption parameter p is elicited on the prior distribution, π (p), based on geological information that is available. Moreover, π (p) can combine all available geological information motivated by conflicting but realistic arguments (e.g., simulation, cluster analysis, structural control, etc.). The incorporated uncertainties about the probability of repository disruption p, win eventually be averaged out by taking the expectation over π (p). We use the following priors in the analysis: priors chosen for mathematical convenience: Beta (r, s) for (r, s) = (2, 2), (3, 3), (5, 5), (2, 1), (2, 8), (8, 2), and (1, 1); and three priors motivated by expert knowledge. Sensitivity analysis is performed for each prior distribution. Estimated values of hazard based on the priors chosen for mathematical simplicity are uniformly higher than those obtained based on the priors motivated by expert knowledge. And, the model using the prior, Beta (8,2), yields the highest hazard (= 2.97 X 10 -2 ). The minimum hazard is produced by the open-quotes three-expert priorclose quotes (i.e., values of p are equally likely at 10 -3 10 -2 , and 10 -1 ). The estimate of the hazard is 1.39 x which is only about one order of magnitude smaller than the maximum value. The term, open-quotes hazardclose quotes, is defined as the probability of at least one disruption of a repository at the Yucca Mountain site by basaltic volcanism for the next 10,000 years
Sensitivity analysis approaches applied to systems biology models.
Zi, Z
2011-11-01
With the rising application of systems biology, sensitivity analysis methods have been widely applied to study the biological systems, including metabolic networks, signalling pathways and genetic circuits. Sensitivity analysis can provide valuable insights about how robust the biological responses are with respect to the changes of biological parameters and which model inputs are the key factors that affect the model outputs. In addition, sensitivity analysis is valuable for guiding experimental analysis, model reduction and parameter estimation. Local and global sensitivity analysis approaches are the two types of sensitivity analysis that are commonly applied in systems biology. Local sensitivity analysis is a classic method that studies the impact of small perturbations on the model outputs. On the other hand, global sensitivity analysis approaches have been applied to understand how the model outputs are affected by large variations of the model input parameters. In this review, the author introduces the basic concepts of sensitivity analysis approaches applied to systems biology models. Moreover, the author discusses the advantages and disadvantages of different sensitivity analysis methods, how to choose a proper sensitivity analysis approach, the available sensitivity analysis tools for systems biology models and the caveats in the interpretation of sensitivity analysis results.
Sensitivity of SBLOCA analysis to model nodalization
International Nuclear Information System (INIS)
Lee, C.; Ito, T.; Abramson, P.B.
1983-01-01
The recent Semiscale test S-UT-8 indicates the possibility for primary liquid to hang up in the steam generators during a SBLOCA, permitting core uncovery prior to loop-seal clearance. In analysis of Small Break Loss of Coolant Accidents with RELAP5, it is found that resultant transient behavior is quite sensitive to the selection of nodalization for the steam generators. Although global parameters such as integrated mass loss, primary inventory and primary pressure are relatively insensitive to the nodalization, it is found that the predicted distribution of inventory around the primary is significantly affected by nodalization. More detailed nodalization predicts that more of the inventory tends to remain in the steam generators, resulting in less inventory in the reactor vessel and therefore causing earlier and more severe core uncovery
Sensitivity Analysis of a Simplified Fire Dynamic Model
DEFF Research Database (Denmark)
Sørensen, Lars Schiøtt; Nielsen, Anker
2015-01-01
This paper discusses a method for performing a sensitivity analysis of parameters used in a simplified fire model for temperature estimates in the upper smoke layer during a fire. The results from the sensitivity analysis can be used when individual parameters affecting fire safety are assessed...
Global sensitivity analysis of computer models with functional inputs
International Nuclear Information System (INIS)
Iooss, Bertrand; Ribatet, Mathieu
2009-01-01
Global sensitivity analysis is used to quantify the influence of uncertain model inputs on the response variability of a numerical model. The common quantitative methods are appropriate with computer codes having scalar model inputs. This paper aims at illustrating different variance-based sensitivity analysis techniques, based on the so-called Sobol's indices, when some model inputs are functional, such as stochastic processes or random spatial fields. In this work, we focus on large cpu time computer codes which need a preliminary metamodeling step before performing the sensitivity analysis. We propose the use of the joint modeling approach, i.e., modeling simultaneously the mean and the dispersion of the code outputs using two interlinked generalized linear models (GLMs) or generalized additive models (GAMs). The 'mean model' allows to estimate the sensitivity indices of each scalar model inputs, while the 'dispersion model' allows to derive the total sensitivity index of the functional model inputs. The proposed approach is compared to some classical sensitivity analysis methodologies on an analytical function. Lastly, the new methodology is applied to an industrial computer code that simulates the nuclear fuel irradiation.
Global Sensitivity Analysis of Environmental Models: Convergence, Robustness and Validation
Sarrazin, Fanny; Pianosi, Francesca; Khorashadi Zadeh, Farkhondeh; Van Griensven, Ann; Wagener, Thorsten
2015-04-01
Global Sensitivity Analysis aims to characterize the impact that variations in model input factors (e.g. the parameters) have on the model output (e.g. simulated streamflow). In sampling-based Global Sensitivity Analysis, the sample size has to be chosen carefully in order to obtain reliable sensitivity estimates while spending computational resources efficiently. Furthermore, insensitive parameters are typically identified through the definition of a screening threshold: the theoretical value of their sensitivity index is zero but in a sampling-base framework they regularly take non-zero values. There is little guidance available for these two steps in environmental modelling though. The objective of the present study is to support modellers in making appropriate choices, regarding both sample size and screening threshold, so that a robust sensitivity analysis can be implemented. We performed sensitivity analysis for the parameters of three hydrological models with increasing level of complexity (Hymod, HBV and SWAT), and tested three widely used sensitivity analysis methods (Elementary Effect Test or method of Morris, Regional Sensitivity Analysis, and Variance-Based Sensitivity Analysis). We defined criteria based on a bootstrap approach to assess three different types of convergence: the convergence of the value of the sensitivity indices, of the ranking (the ordering among the parameters) and of the screening (the identification of the insensitive parameters). We investigated the screening threshold through the definition of a validation procedure. The results showed that full convergence of the value of the sensitivity indices is not necessarily needed to rank or to screen the model input factors. Furthermore, typical values of the sample sizes that are reported in the literature can be well below the sample sizes that actually ensure convergence of ranking and screening.
Experimental Design for Sensitivity Analysis of Simulation Models
Kleijnen, J.P.C.
2001-01-01
This introductory tutorial gives a survey on the use of statistical designs for what if-or sensitivity analysis in simulation.This analysis uses regression analysis to approximate the input/output transformation that is implied by the simulation model; the resulting regression model is also known as
sensitivity analysis on flexible road pavement life cycle cost model
African Journals Online (AJOL)
user
of sensitivity analysis on a developed flexible pavement life cycle cost model using varying discount rate. The study .... organizations and specific projects needs based. Life-cycle ... developed and completed urban road infrastructure corridor ...
A tool model for predicting atmospheric kinetics with sensitivity analysis
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
A package( a tool model) for program of predicting atmospheric chemical kinetics with sensitivity analysis is presented. The new direct method of calculating the first order sensitivity coefficients using sparse matrix technology to chemical kinetics is included in the tool model, it is only necessary to triangularize the matrix related to the Jacobian matrix of the model equation. The Gear type procedure is used to integrate amodel equation and its coupled auxiliary sensitivity coefficient equations. The FORTRAN subroutines of the model equation, the sensitivity coefficient equations, and their Jacobian analytical expressions are generated automatically from a chemical mechanism. The kinetic representation for the model equation and its sensitivity coefficient equations, and their Jacobian matrix is presented. Various FORTRAN subroutines in packages, such as SLODE, modified MA28, Gear package, with which the program runs in conjunction are recommended.The photo-oxidation of dimethyl disulfide is used for illustration.
Sensitivity Analysis for Urban Drainage Modeling Using Mutual Information
Directory of Open Access Journals (Sweden)
Chuanqi Li
2014-11-01
Full Text Available The intention of this paper is to evaluate the sensitivity of the Storm Water Management Model (SWMM output to its input parameters. A global parameter sensitivity analysis is conducted in order to determine which parameters mostly affect the model simulation results. Two different methods of sensitivity analysis are applied in this study. The first one is the partial rank correlation coefficient (PRCC which measures nonlinear but monotonic relationships between model inputs and outputs. The second one is based on the mutual information which provides a general measure of the strength of the non-monotonic association between two variables. Both methods are based on the Latin Hypercube Sampling (LHS of the parameter space, and thus the same datasets can be used to obtain both measures of sensitivity. The utility of the PRCC and the mutual information analysis methods are illustrated by analyzing a complex SWMM model. The sensitivity analysis revealed that only a few key input variables are contributing significantly to the model outputs; PRCCs and mutual information are calculated and used to determine and rank the importance of these key parameters. This study shows that the partial rank correlation coefficient and mutual information analysis can be considered effective methods for assessing the sensitivity of the SWMM model to the uncertainty in its input parameters.
Automating sensitivity analysis of computer models using computer calculus
International Nuclear Information System (INIS)
Oblow, E.M.; Pin, F.G.
1986-01-01
An automated procedure for performing sensitivity analysis has been developed. The procedure uses a new FORTRAN compiler with computer calculus capabilities to generate the derivatives needed to set up sensitivity equations. The new compiler is called GRESS - Gradient Enhanced Software System. Application of the automated procedure with direct and adjoint sensitivity theory for the analysis of non-linear, iterative systems of equations is discussed. Calculational efficiency consideration and techniques for adjoint sensitivity analysis are emphasized. The new approach is found to preserve the traditional advantages of adjoint theory while removing the tedious human effort previously needed to apply this theoretical methodology. Conclusions are drawn about the applicability of the automated procedure in numerical analysis and large-scale modelling sensitivity studies
Analytic uncertainty and sensitivity analysis of models with input correlations
Zhu, Yueying; Wang, Qiuping A.; Li, Wei; Cai, Xu
2018-03-01
Probabilistic uncertainty analysis is a common means of evaluating mathematical models. In mathematical modeling, the uncertainty in input variables is specified through distribution laws. Its contribution to the uncertainty in model response is usually analyzed by assuming that input variables are independent of each other. However, correlated parameters are often happened in practical applications. In the present paper, an analytic method is built for the uncertainty and sensitivity analysis of models in the presence of input correlations. With the method, it is straightforward to identify the importance of the independence and correlations of input variables in determining the model response. This allows one to decide whether or not the input correlations should be considered in practice. Numerical examples suggest the effectiveness and validation of our analytic method in the analysis of general models. A practical application of the method is also proposed to the uncertainty and sensitivity analysis of a deterministic HIV model.
Sensitivity and uncertainty analysis of the PATHWAY radionuclide transport model
International Nuclear Information System (INIS)
Otis, M.D.
1983-01-01
Procedures were developed for the uncertainty and sensitivity analysis of a dynamic model of radionuclide transport through human food chains. Uncertainty in model predictions was estimated by propagation of parameter uncertainties using a Monte Carlo simulation technique. Sensitivity of model predictions to individual parameters was investigated using the partial correlation coefficient of each parameter with model output. Random values produced for the uncertainty analysis were used in the correlation analysis for sensitivity. These procedures were applied to the PATHWAY model which predicts concentrations of radionuclides in foods grown in Nevada and Utah and exposed to fallout during the period of atmospheric nuclear weapons testing in Nevada. Concentrations and time-integrated concentrations of iodine-131, cesium-136, and cesium-137 in milk and other foods were investigated. 9 figs., 13 tabs
Automating sensitivity analysis of computer models using computer calculus
International Nuclear Information System (INIS)
Oblow, E.M.; Pin, F.G.
1985-01-01
An automated procedure for performing sensitivity analyses has been developed. The procedure uses a new FORTRAN compiler with computer calculus capabilities to generate the derivatives needed to set up sensitivity equations. The new compiler is called GRESS - Gradient Enhanced Software System. Application of the automated procedure with ''direct'' and ''adjoint'' sensitivity theory for the analysis of non-linear, iterative systems of equations is discussed. Calculational efficiency consideration and techniques for adjoint sensitivity analysis are emphasized. The new approach is found to preserve the traditional advantages of adjoint theory while removing the tedious human effort previously needed to apply this theoretical methodology. Conclusions are drawn about the applicability of the automated procedure in numerical analysis and large-scale modelling sensitivity studies. 24 refs., 2 figs
Sensitivity analysis technique for application to deterministic models
International Nuclear Information System (INIS)
Ishigami, T.; Cazzoli, E.; Khatib-Rahbar, M.; Unwin, S.D.
1987-01-01
The characterization of sever accident source terms for light water reactors should include consideration of uncertainties. An important element of any uncertainty analysis is an evaluation of the sensitivity of the output probability distributions reflecting source term uncertainties to assumptions regarding the input probability distributions. Historically, response surface methods (RSMs) were developed to replace physical models using, for example, regression techniques, with simplified models for example, regression techniques, with simplified models for extensive calculations. The purpose of this paper is to present a new method for sensitivity analysis that does not utilize RSM, but instead relies directly on the results obtained from the original computer code calculations. The merits of this approach are demonstrated by application of the proposed method to the suppression pool aerosol removal code (SPARC), and the results are compared with those obtained by sensitivity analysis with (a) the code itself, (b) a regression model, and (c) Iman's method
Automated differentiation of computer models for sensitivity analysis
International Nuclear Information System (INIS)
Worley, B.A.
1990-01-01
Sensitivity analysis of reactor physics computer models is an established discipline after more than twenty years of active development of generalized perturbations theory based on direct and adjoint methods. Many reactor physics models have been enhanced to solve for sensitivities of model results to model data. The calculated sensitivities are usually normalized first derivatives although some codes are capable of solving for higher-order sensitivities. The purpose of this paper is to report on the development and application of the GRESS system for automating the implementation of the direct and adjoint techniques into existing FORTRAN computer codes. The GRESS system was developed at ORNL to eliminate the costly man-power intensive effort required to implement the direct and adjoint techniques into already-existing FORTRAN codes. GRESS has been successfully tested for a number of codes over a wide range of applications and presently operates on VAX machines under both VMS and UNIX operating systems
Automated differentiation of computer models for sensitivity analysis
International Nuclear Information System (INIS)
Worley, B.A.
1991-01-01
Sensitivity analysis of reactor physics computer models is an established discipline after more than twenty years of active development of generalized perturbations theory based on direct and adjoint methods. Many reactor physics models have been enhanced to solve for sensitivities of model results to model data. The calculated sensitivities are usually normalized first derivatives, although some codes are capable of solving for higher-order sensitivities. The purpose of this paper is to report on the development and application of the GRESS system for automating the implementation of the direct and adjoint techniques into existing FORTRAN computer codes. The GRESS system was developed at ORNL to eliminate the costly man-power intensive effort required to implement the direct and adjoint techniques into already-existing FORTRAN codes. GRESS has been successfully tested for a number of codes over a wide range of applications and presently operates on VAX machines under both VMS and UNIX operating systems. (author). 9 refs, 1 tab
Sensitivity Analysis of the Integrated Medical Model for ISS Programs
Goodenow, D. A.; Myers, J. G.; Arellano, J.; Boley, L.; Garcia, Y.; Saile, L.; Walton, M.; Kerstman, E.; Reyes, D.; Young, M.
2016-01-01
Sensitivity analysis estimates the relative contribution of the uncertainty in input values to the uncertainty of model outputs. Partial Rank Correlation Coefficient (PRCC) and Standardized Rank Regression Coefficient (SRRC) are methods of conducting sensitivity analysis on nonlinear simulation models like the Integrated Medical Model (IMM). The PRCC method estimates the sensitivity using partial correlation of the ranks of the generated input values to each generated output value. The partial part is so named because adjustments are made for the linear effects of all the other input values in the calculation of correlation between a particular input and each output. In SRRC, standardized regression-based coefficients measure the sensitivity of each input, adjusted for all the other inputs, on each output. Because the relative ranking of each of the inputs and outputs is used, as opposed to the values themselves, both methods accommodate the nonlinear relationship of the underlying model. As part of the IMM v4.0 validation study, simulations are available that predict 33 person-missions on ISS and 111 person-missions on STS. These simulated data predictions feed the sensitivity analysis procedures. The inputs to the sensitivity procedures include the number occurrences of each of the one hundred IMM medical conditions generated over the simulations and the associated IMM outputs: total quality time lost (QTL), number of evacuations (EVAC), and number of loss of crew lives (LOCL). The IMM team will report the results of using PRCC and SRRC on IMM v4.0 predictions of the ISS and STS missions created as part of the external validation study. Tornado plots will assist in the visualization of the condition-related input sensitivities to each of the main outcomes. The outcomes of this sensitivity analysis will drive review focus by identifying conditions where changes in uncertainty could drive changes in overall model output uncertainty. These efforts are an integral
A global sensitivity analysis approach for morphogenesis models
Boas, Sonja E. M.
2015-11-21
Background Morphogenesis is a developmental process in which cells organize into shapes and patterns. Complex, non-linear and multi-factorial models with images as output are commonly used to study morphogenesis. It is difficult to understand the relation between the uncertainty in the input and the output of such ‘black-box’ models, giving rise to the need for sensitivity analysis tools. In this paper, we introduce a workflow for a global sensitivity analysis approach to study the impact of single parameters and the interactions between them on the output of morphogenesis models. Results To demonstrate the workflow, we used a published, well-studied model of vascular morphogenesis. The parameters of this cellular Potts model (CPM) represent cell properties and behaviors that drive the mechanisms of angiogenic sprouting. The global sensitivity analysis correctly identified the dominant parameters in the model, consistent with previous studies. Additionally, the analysis provided information on the relative impact of single parameters and of interactions between them. This is very relevant because interactions of parameters impede the experimental verification of the predicted effect of single parameters. The parameter interactions, although of low impact, provided also new insights in the mechanisms of in silico sprouting. Finally, the analysis indicated that the model could be reduced by one parameter. Conclusions We propose global sensitivity analysis as an alternative approach to study the mechanisms of morphogenesis. Comparison of the ranking of the impact of the model parameters to knowledge derived from experimental data and from manipulation experiments can help to falsify models and to find the operand mechanisms in morphogenesis. The workflow is applicable to all ‘black-box’ models, including high-throughput in vitro models in which output measures are affected by a set of experimental perturbations.
A global sensitivity analysis approach for morphogenesis models.
Boas, Sonja E M; Navarro Jimenez, Maria I; Merks, Roeland M H; Blom, Joke G
2015-11-21
Morphogenesis is a developmental process in which cells organize into shapes and patterns. Complex, non-linear and multi-factorial models with images as output are commonly used to study morphogenesis. It is difficult to understand the relation between the uncertainty in the input and the output of such 'black-box' models, giving rise to the need for sensitivity analysis tools. In this paper, we introduce a workflow for a global sensitivity analysis approach to study the impact of single parameters and the interactions between them on the output of morphogenesis models. To demonstrate the workflow, we used a published, well-studied model of vascular morphogenesis. The parameters of this cellular Potts model (CPM) represent cell properties and behaviors that drive the mechanisms of angiogenic sprouting. The global sensitivity analysis correctly identified the dominant parameters in the model, consistent with previous studies. Additionally, the analysis provided information on the relative impact of single parameters and of interactions between them. This is very relevant because interactions of parameters impede the experimental verification of the predicted effect of single parameters. The parameter interactions, although of low impact, provided also new insights in the mechanisms of in silico sprouting. Finally, the analysis indicated that the model could be reduced by one parameter. We propose global sensitivity analysis as an alternative approach to study the mechanisms of morphogenesis. Comparison of the ranking of the impact of the model parameters to knowledge derived from experimental data and from manipulation experiments can help to falsify models and to find the operand mechanisms in morphogenesis. The workflow is applicable to all 'black-box' models, including high-throughput in vitro models in which output measures are affected by a set of experimental perturbations.
Sensitivity analysis of predictive models with an automated adjoint generator
International Nuclear Information System (INIS)
Pin, F.G.; Oblow, E.M.
1987-01-01
The adjoint method is a well established sensitivity analysis methodology that is particularly efficient in large-scale modeling problems. The coefficients of sensitivity of a given response with respect to every parameter involved in the modeling code can be calculated from the solution of a single adjoint run of the code. Sensitivity coefficients provide a quantitative measure of the importance of the model data in calculating the final results. The major drawback of the adjoint method is the requirement for calculations of very large numbers of partial derivatives to set up the adjoint equations of the model. ADGEN is a software system that has been designed to eliminate this drawback and automatically implement the adjoint formulation in computer codes. The ADGEN system will be described and its use for improving performance assessments and predictive simulations will be discussed. 8 refs., 1 fig
Sensitivity analysis of physiochemical interaction model: which pair ...
African Journals Online (AJOL)
... of two model parameters at a time on the solution trajectory of physiochemical interaction over a time interval. Our aim is to use this powerful mathematical technique to select the important pair of parameters of this physical process which is cost-effective. Keywords: Passivation Rate, Sensitivity Analysis, ODE23, ODE45 ...
Bayesian Sensitivity Analysis of Statistical Models with Missing Data.
Zhu, Hongtu; Ibrahim, Joseph G; Tang, Niansheng
2014-04-01
Methods for handling missing data depend strongly on the mechanism that generated the missing values, such as missing completely at random (MCAR) or missing at random (MAR), as well as other distributional and modeling assumptions at various stages. It is well known that the resulting estimates and tests may be sensitive to these assumptions as well as to outlying observations. In this paper, we introduce various perturbations to modeling assumptions and individual observations, and then develop a formal sensitivity analysis to assess these perturbations in the Bayesian analysis of statistical models with missing data. We develop a geometric framework, called the Bayesian perturbation manifold, to characterize the intrinsic structure of these perturbations. We propose several intrinsic influence measures to perform sensitivity analysis and quantify the effect of various perturbations to statistical models. We use the proposed sensitivity analysis procedure to systematically investigate the tenability of the non-ignorable missing at random (NMAR) assumption. Simulation studies are conducted to evaluate our methods, and a dataset is analyzed to illustrate the use of our diagnostic measures.
Prior Sensitivity Analysis in Default Bayesian Structural Equation Modeling.
van Erp, Sara; Mulder, Joris; Oberski, Daniel L
2017-11-27
Bayesian structural equation modeling (BSEM) has recently gained popularity because it enables researchers to fit complex models and solve some of the issues often encountered in classical maximum likelihood estimation, such as nonconvergence and inadmissible solutions. An important component of any Bayesian analysis is the prior distribution of the unknown model parameters. Often, researchers rely on default priors, which are constructed in an automatic fashion without requiring substantive prior information. However, the prior can have a serious influence on the estimation of the model parameters, which affects the mean squared error, bias, coverage rates, and quantiles of the estimates. In this article, we investigate the performance of three different default priors: noninformative improper priors, vague proper priors, and empirical Bayes priors-with the latter being novel in the BSEM literature. Based on a simulation study, we find that these three default BSEM methods may perform very differently, especially with small samples. A careful prior sensitivity analysis is therefore needed when performing a default BSEM analysis. For this purpose, we provide a practical step-by-step guide for practitioners to conducting a prior sensitivity analysis in default BSEM. Our recommendations are illustrated using a well-known case study from the structural equation modeling literature, and all code for conducting the prior sensitivity analysis is available in the online supplemental materials. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Automated sensitivity analysis: New tools for modeling complex dynamic systems
International Nuclear Information System (INIS)
Pin, F.G.
1987-01-01
Sensitivity analysis is an established methodology used by researchers in almost every field to gain essential insight in design and modeling studies and in performance assessments of complex systems. Conventional sensitivity analysis methodologies, however, have not enjoyed the widespread use they deserve considering the wealth of information they can provide, partly because of their prohibitive cost or the large initial analytical investment they require. Automated systems have recently been developed at ORNL to eliminate these drawbacks. Compilers such as GRESS and EXAP now allow automatic and cost effective calculation of sensitivities in FORTRAN computer codes. In this paper, these and other related tools are described and their impact and applicability in the general areas of modeling, performance assessment and decision making for radioactive waste isolation problems are discussed
... page: //medlineplus.gov/ency/article/003741.htm Sensitivity analysis To use the sharing features on this page, please enable JavaScript. Sensitivity analysis determines the effectiveness of antibiotics against microorganisms (germs) ...
Sensitivity Analysis of Launch Vehicle Debris Risk Model
Gee, Ken; Lawrence, Scott L.
2010-01-01
As part of an analysis of the loss of crew risk associated with an ascent abort system for a manned launch vehicle, a model was developed to predict the impact risk of the debris resulting from an explosion of the launch vehicle on the crew module. The model consisted of a debris catalog describing the number, size and imparted velocity of each piece of debris, a method to compute the trajectories of the debris and a method to calculate the impact risk given the abort trajectory of the crew module. The model provided a point estimate of the strike probability as a function of the debris catalog, the time of abort and the delay time between the abort and destruction of the launch vehicle. A study was conducted to determine the sensitivity of the strike probability to the various model input parameters and to develop a response surface model for use in the sensitivity analysis of the overall ascent abort risk model. The results of the sensitivity analysis and the response surface model are presented in this paper.
A sensitivity analysis of regional and small watershed hydrologic models
Ambaruch, R.; Salomonson, V. V.; Simmons, J. W.
1975-01-01
Continuous simulation models of the hydrologic behavior of watersheds are important tools in several practical applications such as hydroelectric power planning, navigation, and flood control. Several recent studies have addressed the feasibility of using remote earth observations as sources of input data for hydrologic models. The objective of the study reported here was to determine how accurately remotely sensed measurements must be to provide inputs to hydrologic models of watersheds, within the tolerances needed for acceptably accurate synthesis of streamflow by the models. The study objective was achieved by performing a series of sensitivity analyses using continuous simulation models of three watersheds. The sensitivity analysis showed quantitatively how variations in each of 46 model inputs and parameters affect simulation accuracy with respect to five different performance indices.
Parametric Sensitivity Analysis of the WAVEWATCH III Model
Directory of Open Access Journals (Sweden)
Beng-Chun Lee
2009-01-01
Full Text Available The parameters in numerical wave models need to be calibrated be fore a model can be applied to a specific region. In this study, we selected the 8 most important parameters from the source term of the WAVEWATCH III model and subjected them to sensitivity analysis to evaluate the sensitivity of the WAVEWATCH III model to the selected parameters to determine how many of these parameters should be considered for further discussion, and to justify the significance priority of each parameter. After ranking each parameter by sensitivity and assessing their cumulative impact, we adopted the ARS method to search for the optimal values of those parameters to which the WAVEWATCH III model is most sensitive by comparing modeling results with ob served data at two data buoys off the coast of north eastern Taiwan; the goal being to find optimal parameter values for improved modeling of wave development. The procedure adopting optimal parameters in wave simulations did improve the accuracy of the WAVEWATCH III model in comparison to default runs based on field observations at two buoys.
Importance measures in global sensitivity analysis of nonlinear models
International Nuclear Information System (INIS)
Homma, Toshimitsu; Saltelli, Andrea
1996-01-01
The present paper deals with a new method of global sensitivity analysis of nonlinear models. This is based on a measure of importance to calculate the fractional contribution of the input parameters to the variance of the model prediction. Measures of importance in sensitivity analysis have been suggested by several authors, whose work is reviewed in this article. More emphasis is given to the developments of sensitivity indices by the Russian mathematician I.M. Sobol'. Given that Sobol' treatment of the measure of importance is the most general, his formalism is employed throughout this paper where conceptual and computational improvements of the method are presented. The computational novelty of this study is the introduction of the 'total effect' parameter index. This index provides a measure of the total effect of a given parameter, including all the possible synergetic terms between that parameter and all the others. Rank transformation of the data is also introduced in order to increase the reproducibility of the method. These methods are tested on a few analytical and computer models. The main conclusion of this work is the identification of a sensitivity analysis methodology which is both flexible, accurate and informative, and which can be achieved at reasonable computational cost
Therapeutic Implications from Sensitivity Analysis of Tumor Angiogenesis Models
Poleszczuk, Jan; Hahnfeldt, Philip; Enderling, Heiko
2015-01-01
Anti-angiogenic cancer treatments induce tumor starvation and regression by targeting the tumor vasculature that delivers oxygen and nutrients. Mathematical models prove valuable tools to study the proof-of-concept, efficacy and underlying mechanisms of such treatment approaches. The effects of parameter value uncertainties for two models of tumor development under angiogenic signaling and anti-angiogenic treatment are studied. Data fitting is performed to compare predictions of both models and to obtain nominal parameter values for sensitivity analysis. Sensitivity analysis reveals that the success of different cancer treatments depends on tumor size and tumor intrinsic parameters. In particular, we show that tumors with ample vascular support can be successfully targeted with conventional cytotoxic treatments. On the other hand, tumors with curtailed vascular support are not limited by their growth rate and therefore interruption of neovascularization emerges as the most promising treatment target. PMID:25785600
Variance-based sensitivity analysis for wastewater treatment plant modelling.
Cosenza, Alida; Mannina, Giorgio; Vanrolleghem, Peter A; Neumann, Marc B
2014-02-01
Global sensitivity analysis (GSA) is a valuable tool to support the use of mathematical models that characterise technical or natural systems. In the field of wastewater modelling, most of the recent applications of GSA use either regression-based methods, which require close to linear relationships between the model outputs and model factors, or screening methods, which only yield qualitative results. However, due to the characteristics of membrane bioreactors (MBR) (non-linear kinetics, complexity, etc.) there is an interest to adequately quantify the effects of non-linearity and interactions. This can be achieved with variance-based sensitivity analysis methods. In this paper, the Extended Fourier Amplitude Sensitivity Testing (Extended-FAST) method is applied to an integrated activated sludge model (ASM2d) for an MBR system including microbial product formation and physical separation processes. Twenty-one model outputs located throughout the different sections of the bioreactor and 79 model factors are considered. Significant interactions among the model factors are found. Contrary to previous GSA studies for ASM models, we find the relationship between variables and factors to be non-linear and non-additive. By analysing the pattern of the variance decomposition along the plant, the model factors having the highest variance contributions were identified. This study demonstrates the usefulness of variance-based methods in membrane bioreactor modelling where, due to the presence of membranes and different operating conditions than those typically found in conventional activated sludge systems, several highly non-linear effects are present. Further, the obtained results highlight the relevant role played by the modelling approach for MBR taking into account simultaneously biological and physical processes. © 2013.
Global sensitivity analysis for models with spatially dependent outputs
International Nuclear Information System (INIS)
Iooss, B.; Marrel, A.; Jullien, M.; Laurent, B.
2011-01-01
The global sensitivity analysis of a complex numerical model often calls for the estimation of variance-based importance measures, named Sobol' indices. Meta-model-based techniques have been developed in order to replace the CPU time-expensive computer code with an inexpensive mathematical function, which predicts the computer code output. The common meta-model-based sensitivity analysis methods are well suited for computer codes with scalar outputs. However, in the environmental domain, as in many areas of application, the numerical model outputs are often spatial maps, which may also vary with time. In this paper, we introduce an innovative method to obtain a spatial map of Sobol' indices with a minimal number of numerical model computations. It is based upon the functional decomposition of the spatial output onto a wavelet basis and the meta-modeling of the wavelet coefficients by the Gaussian process. An analytical example is presented to clarify the various steps of our methodology. This technique is then applied to a real hydrogeological case: for each model input variable, a spatial map of Sobol' indices is thus obtained. (authors)
Sensitivity analysis practices: Strategies for model-based inference
International Nuclear Information System (INIS)
Saltelli, Andrea; Ratto, Marco; Tarantola, Stefano; Campolongo, Francesca
2006-01-01
Fourteen years after Science's review of sensitivity analysis (SA) methods in 1989 (System analysis at molecular scale, by H. Rabitz) we search Science Online to identify and then review all recent articles having 'sensitivity analysis' as a keyword. In spite of the considerable developments which have taken place in this discipline, of the good practices which have emerged, and of existing guidelines for SA issued on both sides of the Atlantic, we could not find in our review other than very primitive SA tools, based on 'one-factor-at-a-time' (OAT) approaches. In the context of model corroboration or falsification, we demonstrate that this use of OAT methods is illicit and unjustified, unless the model under analysis is proved to be linear. We show that available good practices, such as variance based measures and others, are able to overcome OAT shortcomings and easy to implement. These methods also allow the concept of factors importance to be defined rigorously, thus making the factors importance ranking univocal. We analyse the requirements of SA in the context of modelling, and present best available practices on the basis of an elementary model. We also point the reader to available recipes for a rigorous SA
Sensitivity analysis practices: Strategies for model-based inference
Energy Technology Data Exchange (ETDEWEB)
Saltelli, Andrea [Institute for the Protection and Security of the Citizen (IPSC), European Commission, Joint Research Centre, TP 361, 21020 Ispra (Vatican City State, Holy See,) (Italy)]. E-mail: andrea.saltelli@jrc.it; Ratto, Marco [Institute for the Protection and Security of the Citizen (IPSC), European Commission, Joint Research Centre, TP 361, 21020 Ispra (VA) (Italy); Tarantola, Stefano [Institute for the Protection and Security of the Citizen (IPSC), European Commission, Joint Research Centre, TP 361, 21020 Ispra (VA) (Italy); Campolongo, Francesca [Institute for the Protection and Security of the Citizen (IPSC), European Commission, Joint Research Centre, TP 361, 21020 Ispra (VA) (Italy)
2006-10-15
Fourteen years after Science's review of sensitivity analysis (SA) methods in 1989 (System analysis at molecular scale, by H. Rabitz) we search Science Online to identify and then review all recent articles having 'sensitivity analysis' as a keyword. In spite of the considerable developments which have taken place in this discipline, of the good practices which have emerged, and of existing guidelines for SA issued on both sides of the Atlantic, we could not find in our review other than very primitive SA tools, based on 'one-factor-at-a-time' (OAT) approaches. In the context of model corroboration or falsification, we demonstrate that this use of OAT methods is illicit and unjustified, unless the model under analysis is proved to be linear. We show that available good practices, such as variance based measures and others, are able to overcome OAT shortcomings and easy to implement. These methods also allow the concept of factors importance to be defined rigorously, thus making the factors importance ranking univocal. We analyse the requirements of SA in the context of modelling, and present best available practices on the basis of an elementary model. We also point the reader to available recipes for a rigorous SA.
ADGEN: a system for automated sensitivity analysis of predictive models
International Nuclear Information System (INIS)
Pin, F.G.; Horwedel, J.E.; Oblow, E.M.; Lucius, J.L.
1987-01-01
A system that can automatically enhance computer codes with a sensitivity calculation capability is presented. With this new system, named ADGEN, rapid and cost-effective calculation of sensitivities can be performed in any FORTRAN code for all input data or parameters. The resulting sensitivities can be used in performance assessment studies related to licensing or interactions with the public to systematically and quantitatively prove the relative importance of each of the system parameters in calculating the final performance results. A general procedure calling for the systematic use of sensitivities in assessment studies is presented. The procedure can be used in modeling and model validation studies to avoid over modeling, in site characterization planning to avoid over collection of data, and in performance assessments to determine the uncertainties on the final calculated results. The added capability to formally perform the inverse problem, i.e., to determine the input data or parameters on which to focus to determine the input data or parameters on which to focus additional research or analysis effort in order to improve the uncertainty of the final results, is also discussed. 7 references, 2 figures
ADGEN: a system for automated sensitivity analysis of predictive models
International Nuclear Information System (INIS)
Pin, F.G.; Horwedel, J.E.; Oblow, E.M.; Lucius, J.L.
1986-09-01
A system that can automatically enhance computer codes with a sensitivity calculation capability is presented. With this new system, named ADGEN, rapid and cost-effective calculation of sensitivities can be performed in any FORTRAN code for all input data or parameters. The resulting sensitivities can be used in performance assessment studies related to licensing or interactions with the public to systematically and quantitatively prove the relative importance of each of the system parameters in calculating the final performance results. A general procedure calling for the systematic use of sensitivities in assessment studies is presented. The procedure can be used in modelling and model validation studies to avoid ''over modelling,'' in site characterization planning to avoid ''over collection of data,'' and in performance assessment to determine the uncertainties on the final calculated results. The added capability to formally perform the inverse problem, i.e., to determine the input data or parameters on which to focus additional research or analysis effort in order to improve the uncertainty of the final results, is also discussed
Sensitivity analysis of numerical model of prestressed concrete containment
Energy Technology Data Exchange (ETDEWEB)
Bílý, Petr, E-mail: petr.bily@fsv.cvut.cz; Kohoutková, Alena, E-mail: akohout@fsv.cvut.cz
2015-12-15
Graphical abstract: - Highlights: • FEM model of prestressed concrete containment with steel liner was created. • Sensitivity analysis of changes in geometry and loads was conducted. • Steel liner and temperature effects are the most important factors. • Creep and shrinkage parameters are essential for the long time analysis. • Prestressing schedule is a key factor in the early stages. - Abstract: Safety is always the main consideration in the design of containment of nuclear power plant. However, efficiency of the design process should be also taken into consideration. Despite the advances in computational abilities in recent years, simplified analyses may be found useful for preliminary scoping or trade studies. In the paper, a study on sensitivity of finite element model of prestressed concrete containment to changes in geometry, loads and other factors is presented. Importance of steel liner, reinforcement, prestressing process, temperature changes, nonlinearity of materials as well as density of finite elements mesh is assessed in the main stages of life cycle of the containment. Although the modeling adjustments have not produced any significant changes in computation time, it was found that in some cases simplified modeling process can lead to significant reduction of work time without degradation of the results.
Sensitivity analysis of the terrestrial food chain model FOOD III
International Nuclear Information System (INIS)
Zach, Reto.
1980-10-01
As a first step in constructing a terrestrial food chain model suitable for long-term waste management situations, a numerical sensitivity analysis of FOOD III was carried out to identify important model parameters. The analysis involved 42 radionuclides, four pathways, 14 food types, 93 parameters and three percentages of parameter variation. We also investigated the importance of radionuclides, pathways and food types. The analysis involved a simple contamination model to render results from individual pathways comparable. The analysis showed that radionuclides vary greatly in their dose contribution to each of the four pathways, but relative contributions to each pathway are very similar. Man's and animals' drinking water pathways are much more important than the leaf and root pathways. However, this result depends on the contamination model used. All the pathways contain unimportant food types. Considering the number of parameters involved, FOOD III has too many different food types. Many of the parameters of the leaf and root pathway are important. However, this is true for only a few of the parameters of animals' drinking water pathway, and for neither of the two parameters of mans' drinking water pathway. The radiological decay constant increases the variability of these results. The dose factor is consistently the most important variable, and it explains most of the variability of radionuclide doses within pathways. Consideration of the variability of dose factors is important in contemporary as well as long-term waste management assessment models, if realistic estimates are to be made. (auth)
Uncertainty and sensitivity analysis of environmental transport models
International Nuclear Information System (INIS)
Margulies, T.S.; Lancaster, L.E.
1985-01-01
An uncertainty and sensitivity analysis has been made of the CRAC-2 (Calculations of Reactor Accident Consequences) atmospheric transport and deposition models. Robustness and uncertainty aspects of air and ground deposited material and the relative contribution of input and model parameters were systematically studied. The underlying data structures were investigated using a multiway layout of factors over specified ranges generated via a Latin hypercube sampling scheme. The variables selected in our analysis include: weather bin, dry deposition velocity, rain washout coefficient/rain intensity, duration of release, heat content, sigma-z (vertical) plume dispersion parameter, sigma-y (crosswind) plume dispersion parameter, and mixing height. To determine the contributors to the output variability (versus distance from the site) step-wise regression analyses were performed on transformations of the spatial concentration patterns simulated. 27 references, 2 figures, 3 tables
Sensitivity Analysis of a Riparian Vegetation Growth Model
Directory of Open Access Journals (Sweden)
Michael Nones
2016-11-01
Full Text Available The paper presents a sensitivity analysis of two main parameters used in a mathematic model able to evaluate the effects of changing hydrology on the growth of riparian vegetation along rivers and its effects on the cross-section width. Due to a lack of data in existing literature, in a past study the schematization proposed here was applied only to two large rivers, assuming steady conditions for the vegetational carrying capacity and coupling the vegetal model with a 1D description of the river morphology. In this paper, the limitation set by steady conditions is overcome, imposing the vegetational evolution dependent upon the initial plant population and the growth rate, which represents the potential growth of the overall vegetation along the watercourse. The sensitivity analysis shows that, regardless of the initial population density, the growth rate can be considered the main parameter defining the development of riparian vegetation, but it results site-specific effects, with significant differences for large and small rivers. Despite the numerous simplifications adopted and the small database analyzed, the comparison between measured and computed river widths shows a quite good capability of the model in representing the typical interactions between riparian vegetation and water flow occurring along watercourses. After a thorough calibration, the relatively simple structure of the code permits further developments and applications to a wide range of alluvial rivers.
Relative sensitivity analysis of the predictive properties of sloppy models.
Myasnikova, Ekaterina; Spirov, Alexander
2018-01-25
Commonly among the model parameters characterizing complex biological systems are those that do not significantly influence the quality of the fit to experimental data, so-called "sloppy" parameters. The sloppiness can be mathematically expressed through saturating response functions (Hill's, sigmoid) thereby embodying biological mechanisms responsible for the system robustness to external perturbations. However, if a sloppy model is used for the prediction of the system behavior at the altered input (e.g. knock out mutations, natural expression variability), it may demonstrate the poor predictive power due to the ambiguity in the parameter estimates. We introduce a method of the predictive power evaluation under the parameter estimation uncertainty, Relative Sensitivity Analysis. The prediction problem is addressed in the context of gene circuit models describing the dynamics of segmentation gene expression in Drosophila embryo. Gene regulation in these models is introduced by a saturating sigmoid function of the concentrations of the regulatory gene products. We show how our approach can be applied to characterize the essential difference between the sensitivity properties of robust and non-robust solutions and select among the existing solutions those providing the correct system behavior at any reasonable input. In general, the method allows to uncover the sources of incorrect predictions and proposes the way to overcome the estimation uncertainties.
Control strategies and sensitivity analysis of anthroponotic visceral leishmaniasis model.
Zamir, Muhammad; Zaman, Gul; Alshomrani, Ali Saleh
2017-12-01
This study proposes a mathematical model of Anthroponotic visceral leishmaniasis epidemic with saturated infection rate and recommends different control strategies to manage the spread of this disease in the community. To do this, first, a model formulation is presented to support these strategies, with quantifications of transmission and intervention parameters. To understand the nature of the initial transmission of the disease, the reproduction number [Formula: see text] is obtained by using the next-generation method. On the basis of sensitivity analysis of the reproduction number [Formula: see text], four different control strategies are proposed for managing disease transmission. For quantification of the prevalence period of the disease, a numerical simulation for each strategy is performed and a detailed summary is presented. Disease-free state is obtained with the help of control strategies. The threshold condition for globally asymptotic stability of the disease-free state is found, and it is ascertained that the state is globally stable. On the basis of sensitivity analysis of the reproduction number, it is shown that the disease can be eradicated by using the proposed strategies.
Directory of Open Access Journals (Sweden)
Xiao-meng Song
2013-01-01
Full Text Available Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on (1 a screening method (Morris for qualitative ranking of parameters, and (2 a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model (RSMSobol. First, the Morris screening method was used to qualitatively identify the parameters' sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model (RSM were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.
A Sensitivity Analysis of fMRI Balloon Model
Zayane, Chadia
2015-04-22
Functional magnetic resonance imaging (fMRI) allows the mapping of the brain activation through measurements of the Blood Oxygenation Level Dependent (BOLD) contrast. The characterization of the pathway from the input stimulus to the output BOLD signal requires the selection of an adequate hemodynamic model and the satisfaction of some specific conditions while conducting the experiment and calibrating the model. This paper, focuses on the identifiability of the Balloon hemodynamic model. By identifiability, we mean the ability to estimate accurately the model parameters given the input and the output measurement. Previous studies of the Balloon model have somehow added knowledge either by choosing prior distributions for the parameters, freezing some of them, or looking for the solution as a projection on a natural basis of some vector space. In these studies, the identification was generally assessed using event-related paradigms. This paper justifies the reasons behind the need of adding knowledge, choosing certain paradigms, and completing the few existing identifiability studies through a global sensitivity analysis of the Balloon model in the case of blocked design experiment.
A Sensitivity Analysis of fMRI Balloon Model
Zayane, Chadia; Laleg-Kirati, Taous-Meriem
2015-01-01
Functional magnetic resonance imaging (fMRI) allows the mapping of the brain activation through measurements of the Blood Oxygenation Level Dependent (BOLD) contrast. The characterization of the pathway from the input stimulus to the output BOLD signal requires the selection of an adequate hemodynamic model and the satisfaction of some specific conditions while conducting the experiment and calibrating the model. This paper, focuses on the identifiability of the Balloon hemodynamic model. By identifiability, we mean the ability to estimate accurately the model parameters given the input and the output measurement. Previous studies of the Balloon model have somehow added knowledge either by choosing prior distributions for the parameters, freezing some of them, or looking for the solution as a projection on a natural basis of some vector space. In these studies, the identification was generally assessed using event-related paradigms. This paper justifies the reasons behind the need of adding knowledge, choosing certain paradigms, and completing the few existing identifiability studies through a global sensitivity analysis of the Balloon model in the case of blocked design experiment.
Sensitivity study of CFD turbulent models for natural convection analysis
International Nuclear Information System (INIS)
Yu sun, Park
2007-01-01
The buoyancy driven convective flow fields are steady circulatory flows which were made between surfaces maintained at two fixed temperatures. They are ubiquitous in nature and play an important role in many engineering applications. Application of a natural convection can reduce the costs and efforts remarkably. This paper focuses on the sensitivity study of turbulence analysis using CFD (Computational Fluid Dynamics) for a natural convection in a closed rectangular cavity. Using commercial CFD code, FLUENT and various turbulent models were applied to the turbulent flow. Results from each CFD model will be compared each other in the viewpoints of grid resolution and flow characteristics. It has been showed that: -) obtaining general flow characteristics is possible with relatively coarse grid; -) there is no significant difference between results from finer grid resolutions than grid with y + + is defined as y + = ρ*u*y/μ, u being the wall friction velocity, y being the normal distance from the center of the cell to the wall, ρ and μ being respectively the fluid density and the fluid viscosity; -) the K-ε models show a different flow characteristic from K-ω models or from the Reynolds Stress Model (RSM); and -) the y + parameter is crucial for the selection of the appropriate turbulence model to apply within the simulation
Personalization of models with many model parameters : an efficient sensitivity analysis approach
Donders, W.P.; Huberts, W.; van de Vosse, F.N.; Delhaas, T.
2015-01-01
Uncertainty quantification and global sensitivity analysis are indispensable for patient-specific applications of models that enhance diagnosis or aid decision-making. Variance-based sensitivity analysis methods, which apportion each fraction of the output uncertainty (variance) to the effects of
Sensitivity analysis of alkaline plume modelling: influence of mineralogy
International Nuclear Information System (INIS)
Gaboreau, S.; Claret, F.; Marty, N.; Burnol, A.; Tournassat, C.; Gaucher, E.C.; Munier, I.; Michau, N.; Cochepin, B.
2010-01-01
Document available in extended abstract form only. In the context of a disposal facility for radioactive waste in clayey geological formation, an important modelling effort has been carried out in order to predict the time evolution of interacting cement based (concrete or cement) and clay (argillites and bentonite) materials. The high number of modelling input parameters associated with non negligible uncertainties makes often difficult the interpretation of modelling results. As a consequence, it is necessary to carry out sensitivity analysis on main modelling parameters. In a recent study, Marty et al. (2009) could demonstrate that numerical mesh refinement and consideration of dissolution/precipitation kinetics have a marked effect on (i) the time necessary to numerically clog the initial porosity and (ii) on the final mineral assemblage at the interface. On the contrary, these input parameters have little effect on the extension of the alkaline pH plume. In the present study, we propose to investigate the effects of the considered initial mineralogy on the principal simulation outputs: (1) the extension of the high pH plume, (2) the time to clog the porosity and (3) the alteration front in the clay barrier (extension and nature of mineralogy changes). This was done through sensitivity analysis on both concrete composition and clay mineralogical assemblies since in most published studies, authors considered either only one composition per materials or simplified mineralogy in order to facilitate or to reduce their calculation times. 1D Cartesian reactive transport models were run in order to point out the importance of (1) the crystallinity of concrete phases, (2) the type of clayey materials and (3) the choice of secondary phases that are allowed to precipitate during calculations. Two concrete materials with either nanocrystalline or crystalline phases were simulated in contact with two clayey materials (smectite MX80 or Callovo- Oxfordian argillites). Both
Sensitivity analysis of the nuclear data for MYRRHA reactor modelling
International Nuclear Information System (INIS)
Stankovskiy, Alexey; Van den Eynde, Gert; Cabellos, Oscar; Diez, Carlos J.; Schillebeeckx, Peter; Heyse, Jan
2014-01-01
A global sensitivity analysis of effective neutron multiplication factor k eff to the change of nuclear data library revealed that JEFF-3.2T2 neutron-induced evaluated data library produces closer results to ENDF/B-VII.1 than does JEFF-3.1.2. The analysis of contributions of individual evaluations into k eff sensitivity allowed establishing the priority list of nuclides for which uncertainties on nuclear data must be improved. Detailed sensitivity analysis has been performed for two nuclides from this list, 56 Fe and 238 Pu. The analysis was based on a detailed survey of the evaluations and experimental data. To track the origin of the differences in the evaluations and their impact on k eff , the reaction cross-sections and multiplicities in one evaluation have been substituted by the corresponding data from other evaluations. (authors)
The identification of model effective dimensions using global sensitivity analysis
International Nuclear Information System (INIS)
Kucherenko, Sergei; Feil, Balazs; Shah, Nilay; Mauntz, Wolfgang
2011-01-01
It is shown that the effective dimensions can be estimated at reasonable computational costs using variance based global sensitivity analysis. Namely, the effective dimension in the truncation sense can be found by using the Sobol' sensitivity indices for subsets of variables. The effective dimension in the superposition sense can be estimated by using the first order effects and the total Sobol' sensitivity indices. The classification of some important classes of integrable functions based on their effective dimension is proposed. It is shown that it can be used for the prediction of the QMC efficiency. Results of numerical tests verify the prediction of the developed techniques.
The identification of model effective dimensions using global sensitivity analysis
Energy Technology Data Exchange (ETDEWEB)
Kucherenko, Sergei, E-mail: s.kucherenko@ic.ac.u [CPSE, Imperial College London, South Kensington Campus, London SW7 2AZ (United Kingdom); Feil, Balazs [Department of Process Engineering, University of Pannonia, Veszprem (Hungary); Shah, Nilay [CPSE, Imperial College London, South Kensington Campus, London SW7 2AZ (United Kingdom); Mauntz, Wolfgang [Lehrstuhl fuer Anlagensteuerungstechnik, Fachbereich Chemietechnik, Universitaet Dortmund (Germany)
2011-04-15
It is shown that the effective dimensions can be estimated at reasonable computational costs using variance based global sensitivity analysis. Namely, the effective dimension in the truncation sense can be found by using the Sobol' sensitivity indices for subsets of variables. The effective dimension in the superposition sense can be estimated by using the first order effects and the total Sobol' sensitivity indices. The classification of some important classes of integrable functions based on their effective dimension is proposed. It is shown that it can be used for the prediction of the QMC efficiency. Results of numerical tests verify the prediction of the developed techniques.
Dresch, Jacqueline M; Liu, Xiaozhou; Arnosti, David N; Ay, Ahmet
2010-10-24
Quantitative models of gene expression generate parameter values that can shed light on biological features such as transcription factor activity, cooperativity, and local effects of repressors. An important element in such investigations is sensitivity analysis, which determines how strongly a model's output reacts to variations in parameter values. Parameters of low sensitivity may not be accurately estimated, leading to unwarranted conclusions. Low sensitivity may reflect the nature of the biological data, or it may be a result of the model structure. Here, we focus on the analysis of thermodynamic models, which have been used extensively to analyze gene transcription. Extracted parameter values have been interpreted biologically, but until now little attention has been given to parameter sensitivity in this context. We apply local and global sensitivity analyses to two recent transcriptional models to determine the sensitivity of individual parameters. We show that in one case, values for repressor efficiencies are very sensitive, while values for protein cooperativities are not, and provide insights on why these differential sensitivities stem from both biological effects and the structure of the applied models. In a second case, we demonstrate that parameters that were thought to prove the system's dependence on activator-activator cooperativity are relatively insensitive. We show that there are numerous parameter sets that do not satisfy the relationships proferred as the optimal solutions, indicating that structural differences between the two types of transcriptional enhancers analyzed may not be as simple as altered activator cooperativity. Our results emphasize the need for sensitivity analysis to examine model construction and forms of biological data used for modeling transcriptional processes, in order to determine the significance of estimated parameter values for thermodynamic models. Knowledge of parameter sensitivities can provide the necessary
Analysis of Sea Ice Cover Sensitivity in Global Climate Model
Directory of Open Access Journals (Sweden)
V. P. Parhomenko
2014-01-01
Full Text Available The paper presents joint calculations using a 3D atmospheric general circulation model, an ocean model, and a sea ice evolution model. The purpose of the work is to analyze a seasonal and annual evolution of sea ice, long-term variability of a model ice cover, and its sensitivity to some parameters of model as well to define atmosphere-ice-ocean interaction.Results of 100 years simulations of Arctic basin sea ice evolution are analyzed. There are significant (about 0.5 m inter-annual fluctuations of an ice cover.The ice - atmosphere sensible heat flux reduced by 10% leads to the growth of average sea ice thickness within the limits of 0.05 m – 0.1 m. However in separate spatial points the thickness decreases up to 0.5 m. An analysis of the seasonably changing average ice thickness with decreasing, as compared to the basic variant by 0.05 of clear sea ice albedo and that of snow shows the ice thickness reduction in a range from 0.2 m up to 0.6 m, and the change maximum falls for the summer season of intensive melting. The spatial distribution of ice thickness changes shows, that on the large part of the Arctic Ocean there was a reduction of ice thickness down to 1 m. However, there is also an area of some increase of the ice layer basically in a range up to 0.2 m (Beaufort Sea. The 0.05 decrease of sea ice snow albedo leads to reduction of average ice thickness approximately by 0.2 m, and this value slightly depends on a season. In the following experiment the ocean – ice thermal interaction influence on the ice cover is estimated. It is carried out by increase of a heat flux from ocean to the bottom surface of sea ice by 2 W/sq. m in comparison with base variant. The analysis demonstrates, that the average ice thickness reduces in a range from 0.2 m to 0.35 m. There are small seasonal changes of this value.The numerical experiments results have shown, that an ice cover and its seasonal evolution rather strongly depend on varied parameters
Personalization of models with many model parameters: an efficient sensitivity analysis approach.
Donders, W P; Huberts, W; van de Vosse, F N; Delhaas, T
2015-10-01
Uncertainty quantification and global sensitivity analysis are indispensable for patient-specific applications of models that enhance diagnosis or aid decision-making. Variance-based sensitivity analysis methods, which apportion each fraction of the output uncertainty (variance) to the effects of individual input parameters or their interactions, are considered the gold standard. The variance portions are called the Sobol sensitivity indices and can be estimated by a Monte Carlo (MC) approach (e.g., Saltelli's method [1]) or by employing a metamodel (e.g., the (generalized) polynomial chaos expansion (gPCE) [2, 3]). All these methods require a large number of model evaluations when estimating the Sobol sensitivity indices for models with many parameters [4]. To reduce the computational cost, we introduce a two-step approach. In the first step, a subset of important parameters is identified for each output of interest using the screening method of Morris [5]. In the second step, a quantitative variance-based sensitivity analysis is performed using gPCE. Efficient sampling strategies are introduced to minimize the number of model runs required to obtain the sensitivity indices for models considering multiple outputs. The approach is tested using a model that was developed for predicting post-operative flows after creation of a vascular access for renal failure patients. We compare the sensitivity indices obtained with the novel two-step approach with those obtained from a reference analysis that applies Saltelli's MC method. The two-step approach was found to yield accurate estimates of the sensitivity indices at two orders of magnitude lower computational cost. Copyright © 2015 John Wiley & Sons, Ltd.
Sensitivity Analysis of the Bone Fracture Risk Model
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
Models for patients' recruitment in clinical trials and sensitivity analysis.
Mijoule, Guillaume; Savy, Stéphanie; Savy, Nicolas
2012-07-20
Taking a decision on the feasibility and estimating the duration of patients' recruitment in a clinical trial are very important but very hard questions to answer, mainly because of the huge variability of the system. The more elaborated works on this topic are those of Anisimov and co-authors, where they investigate modelling of the enrolment period by using Gamma-Poisson processes, which allows to develop statistical tools that can help the manager of the clinical trial to answer these questions and thus help him to plan the trial. The main idea is to consider an ongoing study at an intermediate time, denoted t(1). Data collected on [0,t(1)] allow to calibrate the parameters of the model, which are then used to make predictions on what will happen after t(1). This method allows us to estimate the probability of ending the trial on time and give possible corrective actions to the trial manager especially regarding how many centres have to be open to finish on time. In this paper, we investigate a Pareto-Poisson model, which we compare with the Gamma-Poisson one. We will discuss the accuracy of the estimation of the parameters and compare the models on a set of real case data. We make the comparison on various criteria : the expected recruitment duration, the quality of fitting to the data and its sensitivity to parameter errors. We discuss the influence of the centres opening dates on the estimation of the duration. This is a very important question to deal with in the setting of our data set. In fact, these dates are not known. For this discussion, we consider a uniformly distributed approach. Finally, we study the sensitivity of the expected duration of the trial with respect to the parameters of the model : we calculate to what extent an error on the estimation of the parameters generates an error in the prediction of the duration.
Global sensitivity analysis of DRAINMOD-FOREST, an integrated forest ecosystem model
Shiying Tian; Mohamed A. Youssef; Devendra M. Amatya; Eric D. Vance
2014-01-01
Global sensitivity analysis is a useful tool to understand process-based ecosystem models by identifying key parameters and processes controlling model predictions. This study reported a comprehensive global sensitivity analysis for DRAINMOD-FOREST, an integrated model for simulating water, carbon (C), and nitrogen (N) cycles and plant growth in lowland forests. The...
Sensitivity Analysis of Mixed Models for Incomplete Longitudinal Data
Xu, Shu; Blozis, Shelley A.
2011-01-01
Mixed models are used for the analysis of data measured over time to study population-level change and individual differences in change characteristics. Linear and nonlinear functions may be used to describe a longitudinal response, individuals need not be observed at the same time points, and missing data, assumed to be missing at random (MAR),…
Modelling pesticides volatilisation in greenhouses: Sensitivity analysis of a modified PEARL model.
Houbraken, Michael; Doan Ngoc, Kim; van den Berg, Frederik; Spanoghe, Pieter
2017-12-01
The application of the existing PEARL model was extended to include estimations of the concentration of crop protection products in greenhouse (indoor) air due to volatilisation from the plant surface. The model was modified to include the processes of ventilation of the greenhouse air to the outside atmosphere and transformation in the air. A sensitivity analysis of the model was performed by varying selected input parameters on a one-by-one basis and comparing the model outputs with the outputs of the reference scenarios. The sensitivity analysis indicates that - in addition to vapour pressure - the model had the highest ratio of variation for the rate ventilation rate and thickness of the boundary layer on the day of application. On the days after application, competing processes, degradation and uptake in the plant, becomes more important. Copyright © 2017 Elsevier B.V. All rights reserved.
Rohmer, Jeremy
2016-04-01
Predicting the temporal evolution of landslides is typically supported by numerical modelling. Dynamic sensitivity analysis aims at assessing the influence of the landslide properties on the time-dependent predictions (e.g., time series of landslide displacements). Yet two major difficulties arise: 1. Global sensitivity analysis require running the landslide model a high number of times (> 1000), which may become impracticable when the landslide model has a high computation time cost (> several hours); 2. Landslide model outputs are not scalar, but function of time, i.e. they are n-dimensional vectors with n usually ranging from 100 to 1000. In this article, I explore the use of a basis set expansion, such as principal component analysis, to reduce the output dimensionality to a few components, each of them being interpreted as a dominant mode of variation in the overall structure of the temporal evolution. The computationally intensive calculation of the Sobol' indices for each of these components are then achieved through meta-modelling, i.e. by replacing the landslide model by a "costless-to-evaluate" approximation (e.g., a projection pursuit regression model). The methodology combining "basis set expansion - meta-model - Sobol' indices" is then applied to the La Frasse landslide to investigate the dynamic sensitivity analysis of the surface horizontal displacements to the slip surface properties during the pore pressure changes. I show how to extract information on the sensitivity of each main modes of temporal behaviour using a limited number (a few tens) of long running simulations. In particular, I identify the parameters, which trigger the occurrence of a turning point marking a shift between a regime of low values of landslide displacements and one of high values.
Comprehensive mechanisms for combustion chemistry: Experiment, modeling, and sensitivity analysis
Energy Technology Data Exchange (ETDEWEB)
Dryer, F.L.; Yetter, R.A. [Princeton Univ., NJ (United States)
1993-12-01
This research program is an integrated experimental/numerical effort to study pyrolysis and oxidation reactions and mechanisms for small-molecule hydrocarbon structures under conditions representative of combustion environments. The experimental aspects of the work are conducted in large diameter flow reactors, at pressures from one to twenty atmospheres, temperatures from 550 K to 1200 K, and with observed reaction times from 10{sup {minus}2} to 5 seconds. Gas sampling of stable reactant, intermediate, and product species concentrations provides not only substantial definition of the phenomenology of reaction mechanisms, but a significantly constrained set of kinetic information with negligible diffusive coupling. Analytical techniques used for detecting hydrocarbons and carbon oxides include gas chromatography (GC), and gas infrared (NDIR) and FTIR methods are utilized for continuous on-line sample detection of light absorption measurements of OH have also been performed in an atmospheric pressure flow reactor (APFR), and a variable pressure flow (VPFR) reactor is presently being instrumented to perform optical measurements of radicals and highly reactive molecular intermediates. The numerical aspects of the work utilize zero and one-dimensional pre-mixed, detailed kinetic studies, including path, elemental gradient sensitivity, and feature sensitivity analyses. The program emphasizes the use of hierarchical mechanistic construction to understand and develop detailed kinetic mechanisms. Numerical studies are utilized for guiding experimental parameter selections, for interpreting observations, for extending the predictive range of mechanism constructs, and to study the effects of diffusive transport coupling on reaction behavior in flames. Modeling using well defined and validated mechanisms for the CO/H{sub 2}/oxidant systems.
Sensitivity and uncertainty analysis
Cacuci, Dan G; Navon, Ionel Michael
2005-01-01
As computer-assisted modeling and analysis of physical processes have continued to grow and diversify, sensitivity and uncertainty analyses have become indispensable scientific tools. Sensitivity and Uncertainty Analysis. Volume I: Theory focused on the mathematical underpinnings of two important methods for such analyses: the Adjoint Sensitivity Analysis Procedure and the Global Adjoint Sensitivity Analysis Procedure. This volume concentrates on the practical aspects of performing these analyses for large-scale systems. The applications addressed include two-phase flow problems, a radiative c
Supplementary Material for: A global sensitivity analysis approach for morphogenesis models
Boas, Sonja; Navarro, Marí a; Merks, Roeland; Blom, Joke
2015-01-01
) represent cell properties and behaviors that drive the mechanisms of angiogenic sprouting. The global sensitivity analysis correctly identified the dominant parameters in the model, consistent with previous studies. Additionally, the analysis provided
Directory of Open Access Journals (Sweden)
Y. Tang
2007-01-01
Full Text Available This study seeks to identify sensitivity tools that will advance our understanding of lumped hydrologic models for the purposes of model improvement, calibration efficiency and improved measurement schemes. Four sensitivity analysis methods were tested: (1 local analysis using parameter estimation software (PEST, (2 regional sensitivity analysis (RSA, (3 analysis of variance (ANOVA, and (4 Sobol's method. The methods' relative efficiencies and effectiveness have been analyzed and compared. These four sensitivity methods were applied to the lumped Sacramento soil moisture accounting model (SAC-SMA coupled with SNOW-17. Results from this study characterize model sensitivities for two medium sized watersheds within the Juniata River Basin in Pennsylvania, USA. Comparative results for the 4 sensitivity methods are presented for a 3-year time series with 1 h, 6 h, and 24 h time intervals. The results of this study show that model parameter sensitivities are heavily impacted by the choice of analysis method as well as the model time interval. Differences between the two adjacent watersheds also suggest strong influences of local physical characteristics on the sensitivity methods' results. This study also contributes a comprehensive assessment of the repeatability, robustness, efficiency, and ease-of-implementation of the four sensitivity methods. Overall ANOVA and Sobol's method were shown to be superior to RSA and PEST. Relative to one another, ANOVA has reduced computational requirements and Sobol's method yielded more robust sensitivity rankings.
Transient dynamic and modeling parameter sensitivity analysis of 1D solid oxide fuel cell model
International Nuclear Information System (INIS)
Huangfu, Yigeng; Gao, Fei; Abbas-Turki, Abdeljalil; Bouquain, David; Miraoui, Abdellatif
2013-01-01
Highlights: • A multiphysics, 1D, dynamic SOFC model is developed. • The presented model is validated experimentally in eight different operating conditions. • Electrochemical and thermal dynamic transient time expressions are given in explicit forms. • Parameter sensitivity is discussed for different semi-empirical parameters in the model. - Abstract: In this paper, a multiphysics solid oxide fuel cell (SOFC) dynamic model is developed by using a one dimensional (1D) modeling approach. The dynamic effects of double layer capacitance on the electrochemical domain and the dynamic effect of thermal capacity on thermal domain are thoroughly considered. The 1D approach allows the model to predict the non-uniform distributions of current density, gas pressure and temperature in SOFC during its operation. The developed model has been experimentally validated, under different conditions of temperature and gas pressure. Based on the proposed model, the explicit time constant expressions for different dynamic phenomena in SOFC have been given and discussed in detail. A parameters sensitivity study has also been performed and discussed by using statistical Multi Parameter Sensitivity Analysis (MPSA) method, in order to investigate the impact of parameters on the modeling accuracy
Voulgarelis, Dimitrios; Velayudhan, Ajoy; Smith, Frank
2017-01-01
Agent-based models provide a formidable tool for exploring complex and emergent behaviour of biological systems as well as accurate results but with the drawback of needing a lot of computational power and time for subsequent analysis. On the other hand, equation-based models can more easily be used for complex analysis in a much shorter timescale. This paper formulates an ordinary differential equations and stochastic differential equations model to capture the behaviour of an existing agent-based model of tumour cell reprogramming and applies it to optimization of possible treatment as well as dosage sensitivity analysis. For certain values of the parameter space a close match between the equation-based and agent-based models is achieved. The need for division of labour between the two approaches is explored. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
The application of sensitivity analysis to models of large scale physiological systems
Leonard, J. I.
1974-01-01
A survey of the literature of sensitivity analysis as it applies to biological systems is reported as well as a brief development of sensitivity theory. A simple population model and a more complex thermoregulatory model illustrate the investigatory techniques and interpretation of parameter sensitivity analysis. The role of sensitivity analysis in validating and verifying models, and in identifying relative parameter influence in estimating errors in model behavior due to uncertainty in input data is presented. This analysis is valuable to the simulationist and the experimentalist in allocating resources for data collection. A method for reducing highly complex, nonlinear models to simple linear algebraic models that could be useful for making rapid, first order calculations of system behavior is presented.
Energy Technology Data Exchange (ETDEWEB)
Di Maio, Francesco, E-mail: francesco.dimaio@polimi.it [Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milano (Italy); Nicola, Giancarlo [Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milano (Italy); Zio, Enrico [Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milano (Italy); Chair on System Science and Energetic Challenge Fondation EDF, Ecole Centrale Paris and Supelec, Paris (France); Yu, Yu [School of Nuclear Science and Engineering, North China Electric Power University, 102206 Beijing (China)
2015-08-15
Highlights: • Uncertainties of TH codes affect the system failure probability quantification. • We present Finite Mixture Models (FMMs) for sensitivity analysis of TH codes. • FMMs approximate the pdf of the output of a TH code with a limited number of simulations. • The approach is tested on a Passive Containment Cooling System of an AP1000 reactor. • The novel approach overcomes the results of a standard variance decomposition method. - Abstract: For safety analysis of Nuclear Power Plants (NPPs), Best Estimate (BE) Thermal Hydraulic (TH) codes are used to predict system response in normal and accidental conditions. The assessment of the uncertainties of TH codes is a critical issue for system failure probability quantification. In this paper, we consider passive safety systems of advanced NPPs and present a novel approach of Sensitivity Analysis (SA). The approach is based on Finite Mixture Models (FMMs) to approximate the probability density function (i.e., the uncertainty) of the output of the passive safety system TH code with a limited number of simulations. We propose a novel Sensitivity Analysis (SA) method for keeping the computational cost low: an Expectation Maximization (EM) algorithm is used to calculate the saliency of the TH code input variables for identifying those that most affect the system functional failure. The novel approach is compared with a standard variance decomposition method on a case study considering a Passive Containment Cooling System (PCCS) of an Advanced Pressurized reactor AP1000.
A framework for 2-stage global sensitivity analysis of GastroPlus™ compartmental models.
Scherholz, Megerle L; Forder, James; Androulakis, Ioannis P
2018-04-01
Parameter sensitivity and uncertainty analysis for physiologically based pharmacokinetic (PBPK) models are becoming an important consideration for regulatory submissions, requiring further evaluation to establish the need for global sensitivity analysis. To demonstrate the benefits of an extensive analysis, global sensitivity was implemented for the GastroPlus™ model, a well-known commercially available platform, using four example drugs: acetaminophen, risperidone, atenolol, and furosemide. The capabilities of GastroPlus were expanded by developing an integrated framework to automate the GastroPlus graphical user interface with AutoIt and for execution of the sensitivity analysis in MATLAB ® . Global sensitivity analysis was performed in two stages using the Morris method to screen over 50 parameters for significant factors followed by quantitative assessment of variability using Sobol's sensitivity analysis. The 2-staged approach significantly reduced computational cost for the larger model without sacrificing interpretation of model behavior, showing that the sensitivity results were well aligned with the biopharmaceutical classification system. Both methods detected nonlinearities and parameter interactions that would have otherwise been missed by local approaches. Future work includes further exploration of how the input domain influences the calculated global sensitivity measures as well as extending the framework to consider a whole-body PBPK model.
International Nuclear Information System (INIS)
Spiessl, Sabine; Becker, Dirk-Alexander
2017-06-01
Sensitivity analysis is a mathematical means for analysing the sensitivities of a computational model to variations of its input parameters. Thus, it is a tool for managing parameter uncertainties. It is often performed probabilistically as global sensitivity analysis, running the model a large number of times with different parameter value combinations. Going along with the increase of computer capabilities, global sensitivity analysis has been a field of mathematical research for some decades. In the field of final repository modelling, probabilistic analysis is regarded a key element of a modern safety case. An appropriate uncertainty and sensitivity analysis can help identify parameters that need further dedicated research to reduce the overall uncertainty, generally leads to better system understanding and can thus contribute to building confidence in the models. The purpose of the project described here was to systematically investigate different numerical and graphical techniques of sensitivity analysis with typical repository models, which produce a distinctly right-skewed and tailed output distribution and can exhibit a highly nonlinear, non-monotonic or even non-continuous behaviour. For the investigations presented here, three test models were defined that describe generic, but typical repository systems. A number of numerical and graphical sensitivity analysis methods were selected for investigation and, in part, modified or adapted. Different sampling methods were applied to produce various parameter samples of different sizes and many individual runs with the test models were performed. The results were evaluated with the different methods of sensitivity analysis. On this basis the methods were compared and assessed. This report gives an overview of the background and the applied methods. The results obtained for three typical test models are presented and explained; conclusions in view of practical applications are drawn. At the end, a recommendation
Energy Technology Data Exchange (ETDEWEB)
Spiessl, Sabine; Becker, Dirk-Alexander
2017-06-15
Sensitivity analysis is a mathematical means for analysing the sensitivities of a computational model to variations of its input parameters. Thus, it is a tool for managing parameter uncertainties. It is often performed probabilistically as global sensitivity analysis, running the model a large number of times with different parameter value combinations. Going along with the increase of computer capabilities, global sensitivity analysis has been a field of mathematical research for some decades. In the field of final repository modelling, probabilistic analysis is regarded a key element of a modern safety case. An appropriate uncertainty and sensitivity analysis can help identify parameters that need further dedicated research to reduce the overall uncertainty, generally leads to better system understanding and can thus contribute to building confidence in the models. The purpose of the project described here was to systematically investigate different numerical and graphical techniques of sensitivity analysis with typical repository models, which produce a distinctly right-skewed and tailed output distribution and can exhibit a highly nonlinear, non-monotonic or even non-continuous behaviour. For the investigations presented here, three test models were defined that describe generic, but typical repository systems. A number of numerical and graphical sensitivity analysis methods were selected for investigation and, in part, modified or adapted. Different sampling methods were applied to produce various parameter samples of different sizes and many individual runs with the test models were performed. The results were evaluated with the different methods of sensitivity analysis. On this basis the methods were compared and assessed. This report gives an overview of the background and the applied methods. The results obtained for three typical test models are presented and explained; conclusions in view of practical applications are drawn. At the end, a recommendation
Sensitivity analysis of Takagi-Sugeno-Kang rainfall-runoff fuzzy models
Directory of Open Access Journals (Sweden)
A. P. Jacquin
2009-01-01
Full Text Available This paper is concerned with the sensitivity analysis of the model parameters of the Takagi-Sugeno-Kang fuzzy rainfall-runoff models previously developed by the authors. These models are classified in two types of fuzzy models, where the first type is intended to account for the effect of changes in catchment wetness and the second type incorporates seasonality as a source of non-linearity. The sensitivity analysis is performed using two global sensitivity analysis methods, namely Regional Sensitivity Analysis and Sobol's variance decomposition. The data of six catchments from different geographical locations and sizes are used in the sensitivity analysis. The sensitivity of the model parameters is analysed in terms of several measures of goodness of fit, assessing the model performance from different points of view. These measures include the Nash-Sutcliffe criteria, volumetric errors and peak errors. The results show that the sensitivity of the model parameters depends on both the catchment type and the measure used to assess the model performance.
Global sensitivity analysis applied to drying models for one or a population of granules
DEFF Research Database (Denmark)
Mortier, Severine Therese F. C.; Gernaey, Krist; Thomas, De Beer
2014-01-01
The development of mechanistic models for pharmaceutical processes is of increasing importance due to a noticeable shift toward continuous production in the industry. Sensitivity analysis is a powerful tool during the model building process. A global sensitivity analysis (GSA), exploring sensitiv......The development of mechanistic models for pharmaceutical processes is of increasing importance due to a noticeable shift toward continuous production in the industry. Sensitivity analysis is a powerful tool during the model building process. A global sensitivity analysis (GSA), exploring...... sensitivity in a broad parameter space, is performed to detect the most sensitive factors in two models, that is, one for drying of a single granule and one for the drying of a population of granules [using population balance model (PBM)], which was extended by including the gas velocity as extra input...... compared to our earlier work. beta(2) was found to be the most important factor for the single particle model which is useful information when performing model calibration. For the PBM-model, the granule radius and gas temperature were found to be most sensitive. The former indicates that granulator...
A sensitivity analysis of centrifugal compressors' empirical models
International Nuclear Information System (INIS)
Yoon, Sung Ho; Baek, Je Hyun
2001-01-01
The mean-line method using empirical models is the most practical method of predicting off-design performance. To gain insight into the empirical models, the influence of empirical models on the performance prediction results is investigated. We found that, in the two-zone model, the secondary flow mass fraction has a considerable effect at high mass flow-rates on the performance prediction curves. In the TEIS model, the first element changes the slope of the performance curves as well as the stable operating range. The second element makes the performance curves move up and down as it increases or decreases. It is also discovered that the slip factor affects pressure ratio, but it has little effect on efficiency. Finally, this study reveals that the skin friction coefficient has significant effect on both the pressure ratio curve and the efficiency curve. These results show the limitations of the present empirical models, and more reasonable empirical models are reeded
MOVES regional level sensitivity analysis
2012-01-01
The MOVES Regional Level Sensitivity Analysis was conducted to increase understanding of the operations of the MOVES Model in regional emissions analysis and to highlight the following: : the relative sensitivity of selected MOVES Model input paramet...
Structure and sensitivity analysis of individual-based predator–prey models
International Nuclear Information System (INIS)
Imron, Muhammad Ali; Gergs, Andre; Berger, Uta
2012-01-01
The expensive computational cost of sensitivity analyses has hampered the use of these techniques for analysing individual-based models in ecology. A relatively cheap computational cost, referred to as the Morris method, was chosen to assess the relative effects of all parameters on the model’s outputs and to gain insights into predator–prey systems. Structure and results of the sensitivity analysis of the Sumatran tiger model – the Panthera Population Persistence (PPP) and the Notonecta foraging model (NFM) – were compared. Both models are based on a general predation cycle and designed to understand the mechanisms behind the predator–prey interaction being considered. However, the models differ significantly in their complexity and the details of the processes involved. In the sensitivity analysis, parameters that directly contribute to the number of prey items killed were found to be most influential. These were the growth rate of prey and the hunting radius of tigers in the PPP model as well as attack rate parameters and encounter distance of backswimmers in the NFM model. Analysis of distances in both of the models revealed further similarities in the sensitivity of the two individual-based models. The findings highlight the applicability and importance of sensitivity analyses in general, and screening design methods in particular, during early development of ecological individual-based models. Comparison of model structures and sensitivity analyses provides a first step for the derivation of general rules in the design of predator–prey models for both practical conservation and conceptual understanding. - Highlights: ► Structure of predation processes is similar in tiger and backswimmer model. ► The two individual-based models (IBM) differ in space formulations. ► In both models foraging distance is among the sensitive parameters. ► Morris method is applicable for the sensitivity analysis even of complex IBMs.
A global sensitivity analysis approach for morphogenesis models
Boas, Sonja E. M.; Navarro, Marí a; Merks, Roeland M. H.; Blom, Joke G.
2015-01-01
Morphogenesis is a developmental process in which cells organize into shapes and patterns. Complex, non-linear and multi-factorial models with images as output are commonly used to study morphogenesis. It is difficult to understand
Heuristic Sensitivity Analysis for Baker's Yeast Model Parameters
Leão, Celina P.; Soares, Filomena O.
2004-01-01
The baker's yeast, essentially composed by living cells of Saccharomyces cerevisiae, used in the bread making and beer industries as a microorganism, has an important industrial role. The simulation procedure represents then a necessary tool to understand clearly the baker's yeast fermentation process. The use of mathematical models based on mass balance equations requires the knowledge of the reaction kinetics, thermodynamics, and transport and physical properties. Models may be more or less...
GCR Environmental Models I: Sensitivity Analysis for GCR Environments
Slaba, Tony C.; Blattnig, Steve R.
2014-01-01
Accurate galactic cosmic ray (GCR) models are required to assess crew exposure during long-duration missions to the Moon or Mars. Many of these models have been developed and compared to available measurements, with uncertainty estimates usually stated to be less than 15%. However, when the models are evaluated over a common epoch and propagated through to effective dose, relative differences exceeding 50% are observed. This indicates that the metrics used to communicate GCR model uncertainty can be better tied to exposure quantities of interest for shielding applications. This is the first of three papers focused on addressing this need. In this work, the focus is on quantifying the extent to which each GCR ion and energy group, prior to entering any shielding material or body tissue, contributes to effective dose behind shielding. Results can be used to more accurately calibrate model-free parameters and provide a mechanism for refocusing validation efforts on measurements taken over important energy regions. Results can also be used as references to guide future nuclear cross-section measurements and radiobiology experiments. It is found that GCR with Z>2 and boundary energies below 500 MeV/n induce less than 5% of the total effective dose behind shielding. This finding is important given that most of the GCR models are developed and validated against Advanced Composition Explorer/Cosmic Ray Isotope Spectrometer (ACE/CRIS) measurements taken below 500 MeV/n. It is therefore possible for two models to very accurately reproduce the ACE/CRIS data while inducing very different effective dose values behind shielding.
DEFF Research Database (Denmark)
Malaguerra, Flavio; Chambon, Julie Claire Claudia; Bjerg, Poul Løgstrup
2011-01-01
experiments of complete trichloroethene (TCE) degradation in natural sediments. Global sensitivity analysis was performed using the Morris method and Sobol sensitivity indices to identify the most influential model parameters. Results show that the sulfate concentration and fermentation kinetics are the most...
We present a multi-faceted sensitivity analysis of a spatially explicit, individual-based model (IBM) (HexSim) of a threatened species, the Northern Spotted Owl (Strix occidentalis caurina) on a national forest in Washington, USA. Few sensitivity analyses have been conducted on ...
Sensitivity Analysis of an Agent-Based Model of Culture's Consequences for Trade
Burgers, S.L.G.E.; Jonker, C.M.; Hofstede, G.J.; Verwaart, D.
2010-01-01
This paper describes the analysis of an agent-based model’s sensitivity to changes in parameters that describe the agents’ cultural background, relational parameters, and parameters of the decision functions. As agent-based models may be very sensitive to small changes in parameter values, it is of
Comparison of global sensitivity analysis methods – Application to fuel behavior modeling
Energy Technology Data Exchange (ETDEWEB)
Ikonen, Timo, E-mail: timo.ikonen@vtt.fi
2016-02-15
Highlights: • Several global sensitivity analysis methods are compared. • The methods’ applicability to nuclear fuel performance simulations is assessed. • The implications of large input uncertainties and complex models are discussed. • Alternative strategies to perform sensitivity analyses are proposed. - Abstract: Fuel performance codes have two characteristics that make their sensitivity analysis challenging: large uncertainties in input parameters and complex, non-linear and non-additive structure of the models. The complex structure of the code leads to interactions between inputs that show as cross terms in the sensitivity analysis. Due to the large uncertainties of the inputs these interactions are significant, sometimes even dominating the sensitivity analysis. For the same reason, standard linearization techniques do not usually perform well in the analysis of fuel performance codes. More sophisticated methods are typically needed in the analysis. To this end, we compare the performance of several sensitivity analysis methods in the analysis of a steady state FRAPCON simulation. The comparison of importance rankings obtained with the various methods shows that even the simplest methods can be sufficient for the analysis of fuel maximum temperature. However, the analysis of the gap conductance requires more powerful methods that take into account the interactions of the inputs. In some cases, moment-independent methods are needed. We also investigate the computational cost of the various methods and present recommendations as to which methods to use in the analysis.
Sensitivity Analysis in Structural Equation Models: Cases and Their Influence
Pek, Jolynn; MacCallum, Robert C.
2011-01-01
The detection of outliers and influential observations is routine practice in linear regression. Despite ongoing extensions and development of case diagnostics in structural equation models (SEM), their application has received limited attention and understanding in practice. The use of case diagnostics informs analysts of the uncertainty of model…
A global sensitivity analysis approach for morphogenesis models
S.E.M. Boas (Sonja); M.I. Navarro Jimenez (Maria); R.M.H. Merks (Roeland); J.G. Blom (Joke)
2015-01-01
textabstract{\\bf Background} %if any Morphogenesis is a developmental process in which cells organize into shapes and patterns. Complex, non-linear and multi-factorial models with images as output are commonly used to study morphogenesis. It is difficult to understand the relation between the
Sensitivity analysis of an Advanced Gas-cooled Reactor control rod model
International Nuclear Information System (INIS)
Scott, M.; Green, P.L.; O’Driscoll, D.; Worden, K.; Sims, N.D.
2016-01-01
Highlights: • A model was made of the AGR control rod mechanism. • The aim was to better understand the performance when shutting down the reactor. • The model showed good agreement with test data. • Sensitivity analysis was carried out. • The results demonstrated the robustness of the system. - Abstract: A model has been made of the primary shutdown system of an Advanced Gas-cooled Reactor nuclear power station. The aim of this paper is to explore the use of sensitivity analysis techniques on this model. The two motivations for performing sensitivity analysis are to quantify how much individual uncertain parameters are responsible for the model output uncertainty, and to make predictions about what could happen if one or several parameters were to change. Global sensitivity analysis techniques were used based on Gaussian process emulation; the software package GEM-SA was used to calculate the main effects, the main effect index and the total sensitivity index for each parameter and these were compared to local sensitivity analysis results. The results suggest that the system performance is resistant to adverse changes in several parameters at once.
Sensitivity analysis in the WWTP modelling community – new opportunities and applications
DEFF Research Database (Denmark)
Sin, Gürkan; Ruano, M.V.; Neumann, Marc B.
2010-01-01
design (BSM1 plant layout) using Standardized Regression Coefficients (SRC) and (ii) Applying sensitivity analysis to help fine-tuning a fuzzy controller for a BNPR plant using Morris Screening. The results obtained from each case study are then critically discussed in view of practical applications......A mainstream viewpoint on sensitivity analysis in the wastewater modelling community is that it is a first-order differential analysis of outputs with respect to the parameters – typically obtained by perturbing one parameter at a time with a small factor. An alternative viewpoint on sensitivity...
International Nuclear Information System (INIS)
Storlie, Curtis B.; Swiler, Laura P.; Helton, Jon C.; Sallaberry, Cedric J.
2009-01-01
The analysis of many physical and engineering problems involves running complex computational models (simulation models, computer codes). With problems of this type, it is important to understand the relationships between the input variables (whose values are often imprecisely known) and the output. The goal of sensitivity analysis (SA) is to study this relationship and identify the most significant factors or variables affecting the results of the model. In this presentation, an improvement on existing methods for SA of complex computer models is described for use when the model is too computationally expensive for a standard Monte-Carlo analysis. In these situations, a meta-model or surrogate model can be used to estimate the necessary sensitivity index for each input. A sensitivity index is a measure of the variance in the response that is due to the uncertainty in an input. Most existing approaches to this problem either do not work well with a large number of input variables and/or they ignore the error involved in estimating a sensitivity index. Here, a new approach to sensitivity index estimation using meta-models and bootstrap confidence intervals is described that provides solutions to these drawbacks. Further, an efficient yet effective approach to incorporate this methodology into an actual SA is presented. Several simulated and real examples illustrate the utility of this approach. This framework can be extended to uncertainty analysis as well.
Stability and Sensitive Analysis of a Model with Delay Quorum Sensing
Directory of Open Access Journals (Sweden)
Zhonghua Zhang
2015-01-01
Full Text Available This paper formulates a delay model characterizing the competition between bacteria and immune system. The center manifold reduction method and the normal form theory due to Faria and Magalhaes are used to compute the normal form of the model, and the stability of two nonhyperbolic equilibria is discussed. Sensitivity analysis suggests that the growth rate of bacteria is the most sensitive parameter of the threshold parameter R0 and should be targeted in the controlling strategies.
Siamphukdee, Kanjana; Collins, Frank; Zou, Roger
2013-06-01
Chloride-induced reinforcement corrosion is one of the major causes of premature deterioration in reinforced concrete (RC) structures. Given the high maintenance and replacement costs, accurate modeling of RC deterioration is indispensable for ensuring the optimal allocation of limited economic resources. Since corrosion rate is one of the major factors influencing the rate of deterioration, many predictive models exist. However, because the existing models use very different sets of input parameters, the choice of model for RC deterioration is made difficult. Although the factors affecting corrosion rate are frequently reported in the literature, there is no published quantitative study on the sensitivity of predicted corrosion rate to the various input parameters. This paper presents the results of the sensitivity analysis of the input parameters for nine selected corrosion rate prediction models. Three different methods of analysis are used to determine and compare the sensitivity of corrosion rate to various input parameters: (i) univariate regression analysis, (ii) multivariate regression analysis, and (iii) sensitivity index. The results from the analysis have quantitatively verified that the corrosion rate of steel reinforcement bars in RC structures is highly sensitive to corrosion duration time, concrete resistivity, and concrete chloride content. These important findings establish that future empirical models for predicting corrosion rate of RC should carefully consider and incorporate these input parameters.
Sensitivity analysis of an environmental model: an application of different analysis methods
International Nuclear Information System (INIS)
Campolongo, Francesca; Saltelli, Andrea
1997-01-01
A parametric sensitivity analysis (SA) was conducted on a well known model for the production of a key sulphur bearing compound from algal biota. The model is of interest because of the climatic relevance of the gas (dimethylsulphide, DMS), an initiator of cloud particles. A screening test at low sample size is applied first (Morris method) followed by a computationally intensive variance based measure. Standardised regression coefficients are also computed. The various SA measures are compared with each other, and the use of bootstrap is suggested to extract empirical confidence bounds on the SA estimators. For some of the input factors, investigators guess about the parameters relevance was confirmed; for some others, the results shed new light on the system mechanism and on the data parametrisation
Sensitivity Analysis of Fatigue Crack Growth Model for API Steels in Gaseous Hydrogen.
Amaro, Robert L; Rustagi, Neha; Drexler, Elizabeth S; Slifka, Andrew J
2014-01-01
A model to predict fatigue crack growth of API pipeline steels in high pressure gaseous hydrogen has been developed and is presented elsewhere. The model currently has several parameters that must be calibrated for each pipeline steel of interest. This work provides a sensitivity analysis of the model parameters in order to provide (a) insight to the underlying mathematical and mechanistic aspects of the model, and (b) guidance for model calibration of other API steels.
Parameter sensitivity and uncertainty analysis for a storm surge and wave model
Directory of Open Access Journals (Sweden)
L. A. Bastidas
2016-09-01
Full Text Available Development and simulation of synthetic hurricane tracks is a common methodology used to estimate hurricane hazards in the absence of empirical coastal surge and wave observations. Such methods typically rely on numerical models to translate stochastically generated hurricane wind and pressure forcing into coastal surge and wave estimates. The model output uncertainty associated with selection of appropriate model parameters must therefore be addressed. The computational overburden of probabilistic surge hazard estimates is exacerbated by the high dimensionality of numerical surge and wave models. We present a model parameter sensitivity analysis of the Delft3D model for the simulation of hazards posed by Hurricane Bob (1991 utilizing three theoretical wind distributions (NWS23, modified Rankine, and Holland. The sensitive model parameters (of 11 total considered include wind drag, the depth-induced breaking γB, and the bottom roughness. Several parameters show no sensitivity (threshold depth, eddy viscosity, wave triad parameters, and depth-induced breaking αB and can therefore be excluded to reduce the computational overburden of probabilistic surge hazard estimates. The sensitive model parameters also demonstrate a large number of interactions between parameters and a nonlinear model response. While model outputs showed sensitivity to several parameters, the ability of these parameters to act as tuning parameters for calibration is somewhat limited as proper model calibration is strongly reliant on accurate wind and pressure forcing data. A comparison of the model performance with forcings from the different wind models is also presented.
Time-Dependent Global Sensitivity Analysis for Long-Term Degeneracy Model Using Polynomial Chaos
Directory of Open Access Journals (Sweden)
Jianbin Guo
2014-07-01
Full Text Available Global sensitivity is used to quantify the influence of uncertain model inputs on the output variability of static models in general. However, very few approaches can be applied for the sensitivity analysis of long-term degeneracy models, as far as time-dependent reliability is concerned. The reason is that the static sensitivity may not reflect the completed sensitivity during the entire life circle. This paper presents time-dependent global sensitivity analysis for long-term degeneracy models based on polynomial chaos expansion (PCE. Sobol’ indices are employed as the time-dependent global sensitivity since they provide accurate information on the selected uncertain inputs. In order to compute Sobol’ indices more efficiently, this paper proposes a moving least squares (MLS method to obtain the time-dependent PCE coefficients with acceptable simulation effort. Then Sobol’ indices can be calculated analytically as a postprocessing of the time-dependent PCE coefficients with almost no additional cost. A test case is used to show how to conduct the proposed method, then this approach is applied to an engineering case, and the time-dependent global sensitivity is obtained for the long-term degeneracy mechanism model.
Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models
International Nuclear Information System (INIS)
Lamboni, Matieyendou; Monod, Herve; Makowski, David
2011-01-01
Many dynamic models are used for risk assessment and decision support in ecology and crop science. Such models generate time-dependent model predictions, with time either discretised or continuous. Their global sensitivity analysis is usually applied separately on each time output, but Campbell et al. (2006 ) advocated global sensitivity analyses on the expansion of the dynamics in a well-chosen functional basis. This paper focuses on the particular case when principal components analysis is combined with analysis of variance. In addition to the indices associated with the principal components, generalised sensitivity indices are proposed to synthesize the influence of each parameter on the whole time series output. Index definitions are given when the uncertainty on the input factors is either discrete or continuous and when the dynamic model is either discrete or functional. A general estimation algorithm is proposed, based on classical methods of global sensitivity analysis. The method is applied to a dynamic wheat crop model with 13 uncertain parameters. Three methods of global sensitivity analysis are compared: the Sobol'-Saltelli method, the extended FAST method, and the fractional factorial design of resolution 6.
Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models
Energy Technology Data Exchange (ETDEWEB)
Lamboni, Matieyendou [INRA, Unite MIA (UR341), F78352 Jouy en Josas Cedex (France); Monod, Herve, E-mail: herve.monod@jouy.inra.f [INRA, Unite MIA (UR341), F78352 Jouy en Josas Cedex (France); Makowski, David [INRA, UMR Agronomie INRA/AgroParisTech (UMR 211), BP 01, F78850 Thiverval-Grignon (France)
2011-04-15
Many dynamic models are used for risk assessment and decision support in ecology and crop science. Such models generate time-dependent model predictions, with time either discretised or continuous. Their global sensitivity analysis is usually applied separately on each time output, but Campbell et al. (2006) advocated global sensitivity analyses on the expansion of the dynamics in a well-chosen functional basis. This paper focuses on the particular case when principal components analysis is combined with analysis of variance. In addition to the indices associated with the principal components, generalised sensitivity indices are proposed to synthesize the influence of each parameter on the whole time series output. Index definitions are given when the uncertainty on the input factors is either discrete or continuous and when the dynamic model is either discrete or functional. A general estimation algorithm is proposed, based on classical methods of global sensitivity analysis. The method is applied to a dynamic wheat crop model with 13 uncertain parameters. Three methods of global sensitivity analysis are compared: the Sobol'-Saltelli method, the extended FAST method, and the fractional factorial design of resolution 6.
Sensitivity analysis on the model to the DO and BODc of the Almendares river
International Nuclear Information System (INIS)
Dominguez, J.; Borroto, J.; Hernandez, A.
2004-01-01
In the present work, the sensitivity analysis of the model was done, to compare and evaluate the influence of the kinetic coefficients and other parameters, on the DO and BODc. The effect of the BODc and the DO which the river arrives to the studied zone, the influence of the BDO of the discharges and the flow rate, on the DO was modeled. The sensitivity analysis is the base for developing a calibration optimization procedure of the Streeter Phelps model, in order to make easier the process and to increase the precision of predictions. In the other hand, it will contribute to the definition of the strategies to improve river water quality
Sensitivity analysis of infectious disease models: methods, advances and their application
Wu, Jianyong; Dhingra, Radhika; Gambhir, Manoj; Remais, Justin V.
2013-01-01
Sensitivity analysis (SA) can aid in identifying influential model parameters and optimizing model structure, yet infectious disease modelling has yet to adopt advanced SA techniques that are capable of providing considerable insights over traditional methods. We investigate five global SA methods—scatter plots, the Morris and Sobol’ methods, Latin hypercube sampling-partial rank correlation coefficient and the sensitivity heat map method—and detail their relative merits and pitfalls when applied to a microparasite (cholera) and macroparasite (schistosomaisis) transmission model. The methods investigated yielded similar results with respect to identifying influential parameters, but offered specific insights that vary by method. The classical methods differed in their ability to provide information on the quantitative relationship between parameters and model output, particularly over time. The heat map approach provides information about the group sensitivity of all model state variables, and the parameter sensitivity spectrum obtained using this method reveals the sensitivity of all state variables to each parameter over the course of the simulation period, especially valuable for expressing the dynamic sensitivity of a microparasite epidemic model to its parameters. A summary comparison is presented to aid infectious disease modellers in selecting appropriate methods, with the goal of improving model performance and design. PMID:23864497
A sensitivity analysis for a thermomechanical model of the Antarctic ice sheet and ice shelves
Baratelli, F.; Castellani, G.; Vassena, C.; Giudici, M.
2012-04-01
The outcomes of an ice sheet model depend on a number of parameters and physical quantities which are often estimated with large uncertainty, because of lack of sufficient experimental measurements in such remote environments. Therefore, the efforts to improve the accuracy of the predictions of ice sheet models by including more physical processes and interactions with atmosphere, hydrosphere and lithosphere can be affected by the inaccuracy of the fundamental input data. A sensitivity analysis can help to understand which are the input data that most affect the different predictions of the model. In this context, a finite difference thermomechanical ice sheet model based on the Shallow-Ice Approximation (SIA) and on the Shallow-Shelf Approximation (SSA) has been developed and applied for the simulation of the evolution of the Antarctic ice sheet and ice shelves for the last 200 000 years. The sensitivity analysis of the model outcomes (e.g., the volume of the ice sheet and of the ice shelves, the basal melt rate of the ice sheet, the mean velocity of the Ross and Ronne-Filchner ice shelves, the wet area at the base of the ice sheet) with respect to the model parameters (e.g., the basal sliding coefficient, the geothermal heat flux, the present-day surface accumulation and temperature, the mean ice shelves viscosity, the melt rate at the base of the ice shelves) has been performed by computing three synthetic numerical indices: two local sensitivity indices and a global sensitivity index. Local sensitivity indices imply a linearization of the model and neglect both non-linear and joint effects of the parameters. The global variance-based sensitivity index, instead, takes into account the complete variability of the input parameters but is usually conducted with a Monte Carlo approach which is computationally very demanding for non-linear complex models. Therefore, the global sensitivity index has been computed using a development of the model outputs in a
DEFF Research Database (Denmark)
Price, Jason Anthony; Nordblad, Mathias; Woodley, John
2014-01-01
This paper demonstrates the added benefits of using uncertainty and sensitivity analysis in the kinetics of enzymatic biodiesel production. For this study, a kinetic model by Fedosov and co-workers is used. For the uncertainty analysis the Monte Carlo procedure was used to statistically quantify...
Garikapati, Hasini; Verhoosel, Clemens V.; van Brummelen, Harald; Diez, Pedro; Papadrakakis, M.; Papadopoulos, V.; Stefanou, G.; Plevris, V.
2016-01-01
Hydraulic fracturing is a process that is surrounded by uncertainty, as available data on e.g. rock formations is scant and available models are still rudimentary. In this contribution sensitivity analysis is carried out as first step in studying the uncertainties in the model. This is done to
For several decades, optimization and sensitivity/uncertainty analysis of environmental models has been the subject of extensive research. Although much progress has been made and sophisticated methods developed, the growing complexity of environmental models to represent real-world systems makes it...
DEFF Research Database (Denmark)
Meng, Lexuan; Dragicevic, Tomislav; Vasquez, Juan Carlos
2015-01-01
of dynamic study. The aim of this paper is to model the complete DC microgrid system in z-domain and perform sensitivity analysis for the complete system. A generalized modeling method is proposed and the system dynamics under different control parameters, communication topologies and communication speed...
Monte Carlo sensitivity analysis of an Eulerian large-scale air pollution model
International Nuclear Information System (INIS)
Dimov, I.; Georgieva, R.; Ostromsky, Tz.
2012-01-01
Variance-based approaches for global sensitivity analysis have been applied and analyzed to study the sensitivity of air pollutant concentrations according to variations of rates of chemical reactions. The Unified Danish Eulerian Model has been used as a mathematical model simulating a remote transport of air pollutants. Various Monte Carlo algorithms for numerical integration have been applied to compute Sobol's global sensitivity indices. A newly developed Monte Carlo algorithm based on Sobol's quasi-random points MCA-MSS has been applied for numerical integration. It has been compared with some existing approaches, namely Sobol's ΛΠ τ sequences, an adaptive Monte Carlo algorithm, the plain Monte Carlo algorithm, as well as, eFAST and Sobol's sensitivity approaches both implemented in SIMLAB software. The analysis and numerical results show advantages of MCA-MSS for relatively small sensitivity indices in terms of accuracy and efficiency. Practical guidelines on the estimation of Sobol's global sensitivity indices in the presence of computational difficulties have been provided. - Highlights: ► Variance-based global sensitivity analysis is performed for the air pollution model UNI-DEM. ► The main effect of input parameters dominates over higher-order interactions. ► Ozone concentrations are influenced mostly by variability of three chemical reactions rates. ► The newly developed MCA-MSS for multidimensional integration is compared with other approaches. ► More precise approaches like MCA-MSS should be applied when the needed accuracy has not been achieved.
A New Computationally Frugal Method For Sensitivity Analysis Of Environmental Models
Rakovec, O.; Hill, M. C.; Clark, M. P.; Weerts, A.; Teuling, R.; Borgonovo, E.; Uijlenhoet, R.
2013-12-01
Effective and efficient parameter sensitivity analysis methods are crucial to understand the behaviour of complex environmental models and use of models in risk assessment. This paper proposes a new computationally frugal method for analyzing parameter sensitivity: the Distributed Evaluation of Local Sensitivity Analysis (DELSA). The DELSA method can be considered a hybrid of local and global methods, and focuses explicitly on multiscale evaluation of parameter sensitivity across the parameter space. Results of the DELSA method are compared with the popular global, variance-based Sobol' method and the delta method. We assess the parameter sensitivity of both (1) a simple non-linear reservoir model with only two parameters, and (2) five different "bucket-style" hydrologic models applied to a medium-sized catchment (200 km2) in the Belgian Ardennes. Results show that in both the synthetic and real-world examples, the global Sobol' method and the DELSA method provide similar sensitivities, with the DELSA method providing more detailed insight at much lower computational cost. The ability to understand how sensitivity measures vary through parameter space with modest computational requirements provides exciting new opportunities.
Xi, Qing; Li, Zhao-Fu; Luo, Chuan
2014-05-01
Sensitivity analysis of hydrology and water quality parameters has a great significance for integrated model's construction and application. Based on AnnAGNPS model's mechanism, terrain, hydrology and meteorology, field management, soil and other four major categories of 31 parameters were selected for the sensitivity analysis in Zhongtian river watershed which is a typical small watershed of hilly region in the Taihu Lake, and then used the perturbation method to evaluate the sensitivity of the parameters to the model's simulation results. The results showed that: in the 11 terrain parameters, LS was sensitive to all the model results, RMN, RS and RVC were generally sensitive and less sensitive to the output of sediment but insensitive to the remaining results. For hydrometeorological parameters, CN was more sensitive to runoff and sediment and relatively sensitive for the rest results. In field management, fertilizer and vegetation parameters, CCC, CRM and RR were less sensitive to sediment and particulate pollutants, the six fertilizer parameters (FR, FD, FID, FOD, FIP, FOP) were particularly sensitive for nitrogen and phosphorus nutrients. For soil parameters, K is quite sensitive to all the results except the runoff, the four parameters of the soil's nitrogen and phosphorus ratio (SONR, SINR, SOPR, SIPR) were less sensitive to the corresponding results. The simulation and verification results of runoff in Zhongtian watershed show a good accuracy with the deviation less than 10% during 2005- 2010. Research results have a direct reference value on AnnAGNPS model's parameter selection and calibration adjustment. The runoff simulation results of the study area also proved that the sensitivity analysis was practicable to the parameter's adjustment and showed the adaptability to the hydrology simulation in the Taihu Lake basin's hilly region and provide reference for the model's promotion in China.
Abusam, A.A.A.; Keesman, K.J.; Straten, van G.; Spanjers, H.; Meinema, K.
2001-01-01
This paper demonstrates the application of the factorial sensitivity analysis methodology in studying the influence of variations in stoichiometric, kinetic and operating parameters on the performance indices of an oxidation ditch simulation model (benchmark). Factorial sensitivity analysis
Sensitivity analysis of machine-learning models of hydrologic time series
O'Reilly, A. M.
2017-12-01
Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.
A sensitivity analysis of the WIPP disposal room model: Phase 1
Energy Technology Data Exchange (ETDEWEB)
Labreche, D.A.; Beikmann, M.A. [RE/SPEC, Inc., Albuquerque, NM (United States); Osnes, J.D. [RE/SPEC, Inc., Rapid City, SD (United States); Butcher, B.M. [Sandia National Labs., Albuquerque, NM (United States)
1995-07-01
The WIPP Disposal Room Model (DRM) is a numerical model with three major components constitutive models of TRU waste, crushed salt backfill, and intact halite -- and several secondary components, including air gap elements, slidelines, and assumptions on symmetry and geometry. A sensitivity analysis of the Disposal Room Model was initiated on two of the three major components (waste and backfill models) and on several secondary components as a group. The immediate goal of this component sensitivity analysis (Phase I) was to sort (rank) model parameters in terms of their relative importance to model response so that a Monte Carlo analysis on a reduced set of DRM parameters could be performed under Phase II. The goal of the Phase II analysis will be to develop a probabilistic definition of a disposal room porosity surface (porosity, gas volume, time) that could be used in WIPP Performance Assessment analyses. This report documents a literature survey which quantifies the relative importance of the secondary room components to room closure, a differential analysis of the creep consolidation model and definition of a follow-up Monte Carlo analysis of the model, and an analysis and refitting of the waste component data on which a volumetric plasticity model of TRU drum waste is based. A summary, evaluation of progress, and recommendations for future work conclude the report.
Robles, A; Ruano, M V; Ribes, J; Seco, A; Ferrer, J
2014-04-01
The results of a global sensitivity analysis of a filtration model for submerged anaerobic MBRs (AnMBRs) are assessed in this paper. This study aimed to (1) identify the less- (or non-) influential factors of the model in order to facilitate model calibration and (2) validate the modelling approach (i.e. to determine the need for each of the proposed factors to be included in the model). The sensitivity analysis was conducted using a revised version of the Morris screening method. The dynamic simulations were conducted using long-term data obtained from an AnMBR plant fitted with industrial-scale hollow-fibre membranes. Of the 14 factors in the model, six were identified as influential, i.e. those calibrated using off-line protocols. A dynamic calibration (based on optimisation algorithms) of these influential factors was conducted. The resulting estimated model factors accurately predicted membrane performance. Copyright © 2014 Elsevier Ltd. All rights reserved.
Gul, R; Bernhard, S
2015-11-01
In computational cardiovascular models, parameters are one of major sources of uncertainty, which make the models unreliable and less predictive. In order to achieve predictive models that allow the investigation of the cardiovascular diseases, sensitivity analysis (SA) can be used to quantify and reduce the uncertainty in outputs (pressure and flow) caused by input (electrical and structural) model parameters. In the current study, three variance based global sensitivity analysis (GSA) methods; Sobol, FAST and a sparse grid stochastic collocation technique based on the Smolyak algorithm were applied on a lumped parameter model of carotid bifurcation. Sensitivity analysis was carried out to identify and rank most sensitive parameters as well as to fix less sensitive parameters at their nominal values (factor fixing). In this context, network location and temporal dependent sensitivities were also discussed to identify optimal measurement locations in carotid bifurcation and optimal temporal regions for each parameter in the pressure and flow waves, respectively. Results show that, for both pressure and flow, flow resistance (R), diameter (d) and length of the vessel (l) are sensitive within right common carotid (RCC), right internal carotid (RIC) and right external carotid (REC) arteries, while compliance of the vessels (C) and blood inertia (L) are sensitive only at RCC. Moreover, Young's modulus (E) and wall thickness (h) exhibit less sensitivities on pressure and flow at all locations of carotid bifurcation. Results of network location and temporal variabilities revealed that most of sensitivity was found in common time regions i.e. early systole, peak systole and end systole. Copyright © 2015 Elsevier Inc. All rights reserved.
Bayesian sensitivity analysis of a 1D vascular model with Gaussian process emulators.
Melis, Alessandro; Clayton, Richard H; Marzo, Alberto
2017-12-01
One-dimensional models of the cardiovascular system can capture the physics of pulse waves but involve many parameters. Since these may vary among individuals, patient-specific models are difficult to construct. Sensitivity analysis can be used to rank model parameters by their effect on outputs and to quantify how uncertainty in parameters influences output uncertainty. This type of analysis is often conducted with a Monte Carlo method, where large numbers of model runs are used to assess input-output relations. The aim of this study was to demonstrate the computational efficiency of variance-based sensitivity analysis of 1D vascular models using Gaussian process emulators, compared to a standard Monte Carlo approach. The methodology was tested on four vascular networks of increasing complexity to analyse its scalability. The computational time needed to perform the sensitivity analysis with an emulator was reduced by the 99.96% compared to a Monte Carlo approach. Despite the reduced computational time, sensitivity indices obtained using the two approaches were comparable. The scalability study showed that the number of mechanistic simulations needed to train a Gaussian process for sensitivity analysis was of the order O(d), rather than O(d×103) needed for Monte Carlo analysis (where d is the number of parameters in the model). The efficiency of this approach, combined with capacity to estimate the impact of uncertain parameters on model outputs, will enable development of patient-specific models of the vascular system, and has the potential to produce results with clinical relevance. © 2017 The Authors International Journal for Numerical Methods in Biomedical Engineering Published by John Wiley & Sons Ltd.
Supplementary Material for: A global sensitivity analysis approach for morphogenesis models
Boas, Sonja
2015-01-01
Abstract Background Morphogenesis is a developmental process in which cells organize into shapes and patterns. Complex, non-linear and multi-factorial models with images as output are commonly used to study morphogenesis. It is difficult to understand the relation between the uncertainty in the input and the output of such ‘black-box’ models, giving rise to the need for sensitivity analysis tools. In this paper, we introduce a workflow for a global sensitivity analysis approach to study the impact of single parameters and the interactions between them on the output of morphogenesis models. Results To demonstrate the workflow, we used a published, well-studied model of vascular morphogenesis. The parameters of this cellular Potts model (CPM) represent cell properties and behaviors that drive the mechanisms of angiogenic sprouting. The global sensitivity analysis correctly identified the dominant parameters in the model, consistent with previous studies. Additionally, the analysis provided information on the relative impact of single parameters and of interactions between them. This is very relevant because interactions of parameters impede the experimental verification of the predicted effect of single parameters. The parameter interactions, although of low impact, provided also new insights in the mechanisms of in silico sprouting. Finally, the analysis indicated that the model could be reduced by one parameter. Conclusions We propose global sensitivity analysis as an alternative approach to study the mechanisms of morphogenesis. Comparison of the ranking of the impact of the model parameters to knowledge derived from experimental data and from manipulation experiments can help to falsify models and to find the operand mechanisms in morphogenesis. The workflow is applicable to all ‘black-box’ models, including high-throughput in vitro models in which output measures are affected by a set of experimental perturbations.
Sensitivity Analysis of Biome-Bgc Model for Dry Tropical Forests of Vindhyan Highlands, India
Kumar, M.; Raghubanshi, A. S.
2011-08-01
A process-based model BIOME-BGC was run for sensitivity analysis to see the effect of ecophysiological parameters on net primary production (NPP) of dry tropical forest of India. The sensitivity test reveals that the forest NPP was highly sensitive to the following ecophysiological parameters: Canopy light extinction coefficient (k), Canopy average specific leaf area (SLA), New stem C : New leaf C (SC:LC), Maximum stomatal conductance (gs,max), C:N of fine roots (C:Nfr), All-sided to projected leaf area ratio and Canopy water interception coefficient (Wint). Therefore, these parameters need more precision and attention during estimation and observation in the field studies.
SENSITIVITY ANALYSIS OF BIOME-BGC MODEL FOR DRY TROPICAL FORESTS OF VINDHYAN HIGHLANDS, INDIA
M. Kumar; A. S. Raghubanshi
2012-01-01
A process-based model BIOME-BGC was run for sensitivity analysis to see the effect of ecophysiological parameters on net primary production (NPP) of dry tropical forest of India. The sensitivity test reveals that the forest NPP was highly sensitive to the following ecophysiological parameters: Canopy light extinction coefficient (k), Canopy average specific leaf area (SLA), New stem C : New leaf C (SC:LC), Maximum stomatal conductance (gs,max), C:N of fine roots (C:Nfr), All-sided to...
Wang, Daosheng; Cao, Anzhou; Zhang, Jicai; Fan, Daidu; Liu, Yongzhi; Zhang, Yue
2018-06-01
Based on the theory of inverse problems, a three-dimensional sigma-coordinate cohesive sediment transport model with the adjoint data assimilation is developed. In this model, the physical processes of cohesive sediment transport, including deposition, erosion and advection-diffusion, are parameterized by corresponding model parameters. These parameters are usually poorly known and have traditionally been assigned empirically. By assimilating observations into the model, the model parameters can be estimated using the adjoint method; meanwhile, the data misfit between model results and observations can be decreased. The model developed in this work contains numerous parameters; therefore, it is necessary to investigate the parameter sensitivity of the model, which is assessed by calculating a relative sensitivity function and the gradient of the cost function with respect to each parameter. The results of parameter sensitivity analysis indicate that the model is sensitive to the initial conditions, inflow open boundary conditions, suspended sediment settling velocity and resuspension rate, while the model is insensitive to horizontal and vertical diffusivity coefficients. A detailed explanation of the pattern of sensitivity analysis is also given. In ideal twin experiments, constant parameters are estimated by assimilating 'pseudo' observations. The results show that the sensitive parameters are estimated more easily than the insensitive parameters. The conclusions of this work can provide guidance for the practical applications of this model to simulate sediment transport in the study area.
A reactive transport model for mercury fate in contaminated soil--sensitivity analysis.
Leterme, Bertrand; Jacques, Diederik
2015-11-01
We present a sensitivity analysis of a reactive transport model of mercury (Hg) fate in contaminated soil systems. The one-dimensional model, presented in Leterme et al. (2014), couples water flow in variably saturated conditions with Hg physico-chemical reactions. The sensitivity of Hg leaching and volatilisation to parameter uncertainty is examined using the elementary effect method. A test case is built using a hypothetical 1-m depth sandy soil and a 50-year time series of daily precipitation and evapotranspiration. Hg anthropogenic contamination is simulated in the topsoil by separately considering three different sources: cinnabar, non-aqueous phase liquid and aqueous mercuric chloride. The model sensitivity to a set of 13 input parameters is assessed, using three different model outputs (volatilized Hg, leached Hg, Hg still present in the contaminated soil horizon). Results show that dissolved organic matter (DOM) concentration in soil solution and the binding constant to DOM thiol groups are critical parameters, as well as parameters related to Hg sorption to humic and fulvic acids in solid organic matter. Initial Hg concentration is also identified as a sensitive parameter. The sensitivity analysis also brings out non-monotonic model behaviour for certain parameters.
Parametric sensitivity analysis of an agro-economic model of management of irrigation water
El Ouadi, Ihssan; Ouazar, Driss; El Menyari, Younesse
2015-04-01
The current work aims to build an analysis and decision support tool for policy options concerning the optimal allocation of water resources, while allowing a better reflection on the issue of valuation of water by the agricultural sector in particular. Thus, a model disaggregated by farm type was developed for the rural town of Ait Ben Yacoub located in the east Morocco. This model integrates economic, agronomic and hydraulic data and simulates agricultural gross margin across in this area taking into consideration changes in public policy and climatic conditions, taking into account the competition for collective resources. To identify the model input parameters that influence over the results of the model, a parametric sensitivity analysis is performed by the "One-Factor-At-A-Time" approach within the "Screening Designs" method. Preliminary results of this analysis show that among the 10 parameters analyzed, 6 parameters affect significantly the objective function of the model, it is in order of influence: i) Coefficient of crop yield response to water, ii) Average daily gain in weight of livestock, iii) Exchange of livestock reproduction, iv) maximum yield of crops, v) Supply of irrigation water and vi) precipitation. These 6 parameters register sensitivity indexes ranging between 0.22 and 1.28. Those results show high uncertainties on these parameters that can dramatically skew the results of the model or the need to pay particular attention to their estimates. Keywords: water, agriculture, modeling, optimal allocation, parametric sensitivity analysis, Screening Designs, One-Factor-At-A-Time, agricultural policy, climate change.
Martinez, Gonzalo; Pachepsky, Yakov A; Whelan, Gene; Yakirevich, Alexander M; Guber, Andrey; Gish, Timothy J
2014-02-01
Microbial quality of surface waters attracts attention due to food- and waterborne disease outbreaks. Fecal indicator organisms (FIOs) are commonly used for the microbial pollution level evaluation. Models predicting the fate and transport of FIOs are required to design and evaluate best management practices that reduce the microbial pollution in ecosystems and water sources and thus help to predict the risk of food and waterborne diseases. In this study we performed a sensitivity analysis for the KINEROS/STWIR model developed to predict the FIOs transport out of manured fields to other fields and water bodies in order to identify input variables that control the transport uncertainty. The distributions of model input parameters were set to encompass values found from three-year experiments at the USDA-ARS OPE3 experimental site in Beltsville and publicly available information. Sobol' indices and complementary regression trees were used to perform the global sensitivity analysis of the model and to explore the interactions between model input parameters on the proportion of FIO removed from fields. Regression trees provided a useful visualization of the differences in sensitivity of the model output in different parts of the input variable domain. Environmental controls such as soil saturation, rainfall duration and rainfall intensity had the largest influence in the model behavior, whereas soil and manure properties ranked lower. The field length had only moderate effect on the model output sensitivity to the model inputs. Among the manure-related properties the parameter determining the shape of the FIO release kinetic curve had the largest influence on the removal of FIOs from the fields. That underscored the need to better characterize the FIO release kinetics. Since the most sensitive model inputs are available in soil and weather databases or can be obtained using soil water models, results indicate the opportunity of obtaining large-scale estimates of FIO
DEFF Research Database (Denmark)
Sayar, N.A.; Chen, B.H.; Lye, G.J.
2009-01-01
In this paper we have used a proposed mathematical model, describing the carbon-carbon bond format ion reaction between beta-hydroxypyruvate and glycolaldehyde to synthesise L-erythrulose, catalysed by the enzyme transketolase, for the analysis of the sensitivity of the process to its kinetic...
Deterministic methods for sensitivity and uncertainty analysis in large-scale computer models
International Nuclear Information System (INIS)
Worley, B.A.; Oblow, E.M.; Pin, F.G.; Maerker, R.E.; Horwedel, J.E.; Wright, R.Q.; Lucius, J.L.
1987-01-01
The fields of sensitivity and uncertainty analysis are dominated by statistical techniques when large-scale modeling codes are being analyzed. This paper reports on the development and availability of two systems, GRESS and ADGEN, that make use of computer calculus compilers to automate the implementation of deterministic sensitivity analysis capability into existing computer models. This automation removes the traditional limitation of deterministic sensitivity methods. The paper describes a deterministic uncertainty analysis method (DUA) that uses derivative information as a basis to propagate parameter probability distributions to obtain result probability distributions. The paper demonstrates the deterministic approach to sensitivity and uncertainty analysis as applied to a sample problem that models the flow of water through a borehole. The sample problem is used as a basis to compare the cumulative distribution function of the flow rate as calculated by the standard statistical methods and the DUA method. The DUA method gives a more accurate result based upon only two model executions compared to fifty executions in the statistical case
DEFF Research Database (Denmark)
Flores-Alsina, Xavier; Rodriguez-Roda, Ignasi; Sin, Gürkan
2009-01-01
The objective of this paper is to perform an uncertainty and sensitivity analysis of the predictions of the Benchmark Simulation Model (BSM) No. 1, when comparing four activated sludge control strategies. The Monte Carlo simulation technique is used to evaluate the uncertainty in the BSM1 predict...
Directory of Open Access Journals (Sweden)
J. Li
2013-08-01
Full Text Available Proper specification of model parameters is critical to the performance of land surface models (LSMs. Due to high dimensionality and parameter interaction, estimating parameters of an LSM is a challenging task. Sensitivity analysis (SA is a tool that can screen out the most influential parameters on model outputs. In this study, we conducted parameter screening for six output fluxes for the Common Land Model: sensible heat, latent heat, upward longwave radiation, net radiation, soil temperature and soil moisture. A total of 40 adjustable parameters were considered. Five qualitative SA methods, including local, sum-of-trees, multivariate adaptive regression splines, delta test and Morris methods, were compared. The proper sampling design and sufficient sample size necessary to effectively screen out the sensitive parameters were examined. We found that there are 2–8 sensitive parameters, depending on the output type, and about 400 samples are adequate to reliably identify the most sensitive parameters. We also employed a revised Sobol' sensitivity method to quantify the importance of all parameters. The total effects of the parameters were used to assess the contribution of each parameter to the total variances of the model outputs. The results confirmed that global SA methods can generally identify the most sensitive parameters effectively, while local SA methods result in type I errors (i.e., sensitive parameters labeled as insensitive or type II errors (i.e., insensitive parameters labeled as sensitive. Finally, we evaluated and confirmed the screening results for their consistency with the physical interpretation of the model parameters.
About the use of rank transformation in sensitivity analysis of model output
International Nuclear Information System (INIS)
Saltelli, Andrea; Sobol', Ilya M
1995-01-01
Rank transformations are frequently employed in numerical experiments involving a computational model, especially in the context of sensitivity and uncertainty analyses. Response surface replacement and parameter screening are tasks which may benefit from a rank transformation. Ranks can cope with nonlinear (albeit monotonic) input-output distributions, allowing the use of linear regression techniques. Rank transformed statistics are more robust, and provide a useful solution in the presence of long tailed input and output distributions. As is known to practitioners, care must be employed when interpreting the results of such analyses, as any conclusion drawn using ranks does not translate easily to the original model. In the present note an heuristic approach is taken, to explore, by way of practical examples, the effect of a rank transformation on the outcome of a sensitivity analysis. An attempt is made to identify trends, and to correlate these effects to a model taxonomy. Employing sensitivity indices, whereby the total variance of the model output is decomposed into a sum of terms of increasing dimensionality, we show that the main effect of the rank transformation is to increase the relative weight of the first order terms (the 'main effects'), at the expense of the 'interactions' and 'higher order interactions'. As a result the influence of those parameters which influence the output mostly by way of interactions may be overlooked in an analysis based on the ranks. This difficulty increases with the dimensionality of the problem, and may lead to the failure of a rank based sensitivity analysis. We suggest that the models can be ranked, with respect to the complexity of their input-output relationship, by mean of an 'Association' index I y . I y may complement the usual model coefficient of determination R y 2 as a measure of model complexity for the purpose of uncertainty and sensitivity analysis
DEFF Research Database (Denmark)
Sin, Gürkan; Gernaey, Krist; Neumann, Marc B.
2011-01-01
This study demonstrates the usefulness of global sensitivity analysis in wastewater treatment plant (WWTP) design to prioritize sources of uncertainty and quantify their impact on performance criteria. The study, which is performed with the Benchmark Simulation Model no. 1 plant design, complements...... insight into devising useful ways for reducing uncertainties in the plant performance. This information can help engineers design robust WWTP plants....... a previous paper on input uncertainty characterisation and propagation (Sin et al., 2009). A sampling-based sensitivity analysis is conducted to compute standardized regression coefficients. It was found that this method is able to decompose satisfactorily the variance of plant performance criteria (with R2...
Feng, Jinchao; Lansford, Joshua; Mironenko, Alexander; Pourkargar, Davood Babaei; Vlachos, Dionisios G.; Katsoulakis, Markos A.
2018-03-01
We propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adsorption model on a close packed platinum surface, whose parameters, estimated from quantum-scale computations, are correlated and are limited in size (small data). The proposed mathematical methodology employs gradient-based methods to compute sensitivity indices. We observe that ranking influential parameters depends critically on whether or not correlations between parameters are taken into account. The impact of uncertainty in the correlation and the necessity of the proposed non-parametric perspective are demonstrated.
Directory of Open Access Journals (Sweden)
Jinchao Feng
2018-03-01
Full Text Available We propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adsorption model on a close packed platinum surface, whose parameters, estimated from quantum-scale computations, are correlated and are limited in size (small data. The proposed mathematical methodology employs gradient-based methods to compute sensitivity indices. We observe that ranking influential parameters depends critically on whether or not correlations between parameters are taken into account. The impact of uncertainty in the correlation and the necessity of the proposed non-parametric perspective are demonstrated.
Deterministic sensitivity and uncertainty analysis for large-scale computer models
International Nuclear Information System (INIS)
Worley, B.A.; Pin, F.G.; Oblow, E.M.; Maerker, R.E.; Horwedel, J.E.; Wright, R.Q.
1988-01-01
This paper presents a comprehensive approach to sensitivity and uncertainty analysis of large-scale computer models that is analytic (deterministic) in principle and that is firmly based on the model equations. The theory and application of two systems based upon computer calculus, GRESS and ADGEN, are discussed relative to their role in calculating model derivatives and sensitivities without a prohibitive initial manpower investment. Storage and computational requirements for these two systems are compared for a gradient-enhanced version of the PRESTO-II computer model. A Deterministic Uncertainty Analysis (DUA) method that retains the characteristics of analytically computing result uncertainties based upon parameter probability distributions is then introduced and results from recent studies are shown. 29 refs., 4 figs., 1 tab
Parameter sensitivity analysis of a 1-D cold region lake model for land-surface schemes
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.
Demonstration uncertainty/sensitivity analysis using the health and economic consequence model CRAC2
International Nuclear Information System (INIS)
Alpert, D.J.; Iman, R.L.; Johnson, J.D.; Helton, J.C.
1985-01-01
This paper summarizes a demonstration uncertainty/sensitivity analysis performed on the reactor accident consequence model CRAC2. The study was performed with uncertainty/sensitivity analysis techniques compiled as part of the MELCOR program. The principal objectives of the study were: 1) to demonstrate the use of the uncertainty/sensitivity analysis techniques on a health and economic consequence model, 2) to test the computer models which implement the techniques, 3) to identify possible difficulties in performing such an analysis, and 4) to explore alternative means of analyzing, displaying, and describing the results. Demonstration of the applicability of the techniques was the motivation for performing this study; thus, the results should not be taken as a definitive uncertainty analysis of health and economic consequences. Nevertheless, significant insights on health and economic consequence analysis can be drawn from the results of this type of study. Latin hypercube sampling (LHS), a modified Monte Carlo technique, was used in this study. LHS generates a multivariate input structure in which all the variables of interest are varied simultaneously and desired correlations between variables are preserved. LHS has been shown to produce estimates of output distribution functions that are comparable with results of larger random samples
International Nuclear Information System (INIS)
Rout, S.; Mishra, D.G.; Ravi, P.M.; Tripathi, R.M.
2016-01-01
Tritium is one of the radionuclides likely to get released to the environment from Pressurized Heavy Water Reactors. Environmental models are extensively used to quantify the complex environmental transport processes of radionuclides and also to assess the impact to the environment. Model parameters exerting the significant influence on model results are identified through a sensitivity analysis (SA). SA is the study of how the variation (uncertainty) in the output of a mathematical model can be apportioned, qualitatively or quantitatively, to different sources of variation in the input parameters. This study was designed to identify the sensitive model parameters of specific activity model (TRS 1616, IAEA) for environmental transfer of 3 H following release to air and then to vegetation and animal products. Model includes parameters such as air to soil transfer factor (CRs), Tissue Free Water 3 H to Organically Bound 3 H ratio (Rp), Relative humidity (RH), WCP (fractional water content) and WEQp (water equivalent factor) any change in these parameters leads to change in 3 H level in vegetation and animal products consequently change in dose due to ingestion. All these parameters are function of climate and/or plant which change with time, space and species. Estimation of these parameters at every time is a time consuming and also required sophisticated instrumentation. Therefore it is necessary to identify the sensitive parameters and freeze the values of least sensitive parameters at constant values for more accurate estimation of 3 H dose in short time for routine assessment
Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models
Rakovec, O.; Hill, M. C.; Clark, M. P.; Weerts, A. H.; Teuling, A. J.; Uijlenhoet, R.
2014-01-01
This paper presents a hybrid local-global sensitivity analysis method termed the Distributed Evaluation of Local Sensitivity Analysis (DELSA), which is used here to identify important and unimportant parameters and evaluate how model parameter importance changes as parameter values change. DELSA uses derivative-based "local" methods to obtain the distribution of parameter sensitivity across the parameter space, which promotes consideration of sensitivity analysis results in the context of simulated dynamics. This work presents DELSA, discusses how it relates to existing methods, and uses two hydrologic test cases to compare its performance with the popular global, variance-based Sobol' method. The first test case is a simple nonlinear reservoir model with two parameters. The second test case involves five alternative "bucket-style" hydrologic models with up to 14 parameters applied to a medium-sized catchment (200 km2) in the Belgian Ardennes. Results show that in both examples, Sobol' and DELSA identify similar important and unimportant parameters, with DELSA enabling more detailed insight at much lower computational cost. For example, in the real-world problem the time delay in runoff is the most important parameter in all models, but DELSA shows that for about 20% of parameter sets it is not important at all and alternative mechanisms and parameters dominate. Moreover, the time delay was identified as important in regions producing poor model fits, whereas other parameters were identified as more important in regions of the parameter space producing better model fits. The ability to understand how parameter importance varies through parameter space is critical to inform decisions about, for example, additional data collection and model development. The ability to perform such analyses with modest computational requirements provides exciting opportunities to evaluate complicated models as well as many alternative models.
International Nuclear Information System (INIS)
Li, W. B.; Hoeschen, C.
2010-01-01
Mathematical models for kinetics of radiopharmaceuticals in humans were developed and are used to estimate the radiation absorbed dose for patients in nuclear medicine by the International Commission on Radiological Protection and the Medical Internal Radiation Dose (MIRD) Committee. However, due to the fact that the residence times used were derived from different subjects, partially even with different ethnic backgrounds, a large variation in the model parameters propagates to a high uncertainty of the dose estimation. In this work, a method was developed for analysing the uncertainty and sensitivity of biokinetic models that are used to calculate the residence times. The biokinetic model of 18 F-FDG (FDG) developed by the MIRD Committee was analysed by this developed method. The sources of uncertainty of all model parameters were evaluated based on the experiments. The Latin hypercube sampling technique was used to sample the parameters for model input. Kinetic modelling of FDG in humans was performed. Sensitivity of model parameters was indicated by combining the model input and output, using regression and partial correlation analysis. The transfer rate parameter of plasma to other tissue fast is the parameter with the greatest influence on the residence time of plasma. Optimisation of biokinetic data acquisition in the clinical practice by exploitation of the sensitivity of model parameters obtained in this study is discussed. (authors)
A Sensitivity Analysis Method to Study the Behavior of Complex Process-based Models
Brugnach, M.; Neilson, R.; Bolte, J.
2001-12-01
The use of process-based models as a tool for scientific inquiry is becoming increasingly relevant in ecosystem studies. Process-based models are artificial constructs that simulate the system by mechanistically mimicking the functioning of its component processes. Structurally, a process-based model can be characterized, in terms of its processes and the relationships established among them. Each process comprises a set of functional relationships among several model components (e.g., state variables, parameters and input data). While not encoded explicitly, the dynamics of the model emerge from this set of components and interactions organized in terms of processes. It is the task of the modeler to guarantee that the dynamics generated are appropriate and semantically equivalent to the phenomena being modeled. Despite the availability of techniques to characterize and understand model behavior, they do not suffice to completely and easily understand how a complex process-based model operates. For example, sensitivity analysis studies model behavior by determining the rate of change in model output as parameters or input data are varied. One of the problems with this approach is that it considers the model as a "black box", and it focuses on explaining model behavior by analyzing the relationship input-output. Since, these models have a high degree of non-linearity, understanding how the input affects an output can be an extremely difficult task. Operationally, the application of this technique may constitute a challenging task because complex process-based models are generally characterized by a large parameter space. In order to overcome some of these difficulties, we propose a method of sensitivity analysis to be applicable to complex process-based models. This method focuses sensitivity analysis at the process level, and it aims to determine how sensitive the model output is to variations in the processes. Once the processes that exert the major influence in
Deterministic sensitivity and uncertainty analysis for large-scale computer models
International Nuclear Information System (INIS)
Worley, B.A.; Pin, F.G.; Oblow, E.M.; Maerker, R.E.; Horwedel, J.E.; Wright, R.Q.
1988-01-01
The fields of sensitivity and uncertainty analysis have traditionally been dominated by statistical techniques when large-scale modeling codes are being analyzed. These methods are able to estimate sensitivities, generate response surfaces, and estimate response probability distributions given the input parameter probability distributions. Because the statistical methods are computationally costly, they are usually applied only to problems with relatively small parameter sets. Deterministic methods, on the other hand, are very efficient and can handle large data sets, but generally require simpler models because of the considerable programming effort required for their implementation. The first part of this paper reports on the development and availability of two systems, GRESS and ADGEN, that make use of computer calculus compilers to automate the implementation of deterministic sensitivity analysis capability into existing computer models. This automation removes the traditional limitation of deterministic sensitivity methods. This second part of the paper describes a deterministic uncertainty analysis method (DUA) that uses derivative information as a basis to propagate parameter probability distributions to obtain result probability distributions. This paper is applicable to low-level radioactive waste disposal system performance assessment
International Nuclear Information System (INIS)
Wu, Qiong-Li; Cournède, Paul-Henry; Mathieu, Amélie
2012-01-01
Global sensitivity analysis has a key role to play in the design and parameterisation of functional–structural plant growth models which combine the description of plant structural development (organogenesis and geometry) and functional growth (biomass accumulation and allocation). We are particularly interested in this study in Sobol's method which decomposes the variance of the output of interest into terms due to individual parameters but also to interactions between parameters. Such information is crucial for systems with potentially high levels of non-linearity and interactions between processes, like plant growth. However, the computation of Sobol's indices relies on Monte Carlo sampling and re-sampling, whose costs can be very high, especially when model evaluation is also expensive, as for tree models. In this paper, we thus propose a new method to compute Sobol's indices inspired by Homma–Saltelli, which improves slightly their use of model evaluations, and then derive for this generic type of computational methods an estimator of the error estimation of sensitivity indices with respect to the sampling size. It allows the detailed control of the balance between accuracy and computing time. Numerical tests on a simple non-linear model are convincing and the method is finally applied to a functional–structural model of tree growth, GreenLab, whose particularity is the strong level of interaction between plant functioning and organogenesis. - Highlights: ► We study global sensitivity analysis in the context of functional–structural plant modelling. ► A new estimator based on Homma–Saltelli method is proposed to compute Sobol indices, based on a more balanced re-sampling strategy. ► The estimation accuracy of sensitivity indices for a class of Sobol's estimators can be controlled by error analysis. ► The proposed algorithm is implemented efficiently to compute Sobol indices for a complex tree growth model.
Sensitivity analysis using contribution to sample variance plot: Application to a water hammer model
International Nuclear Information System (INIS)
Tarantola, S.; Kopustinskas, V.; Bolado-Lavin, R.; Kaliatka, A.; Ušpuras, E.; Vaišnoras, M.
2012-01-01
This paper presents “contribution to sample variance plot”, a natural extension of the “contribution to the sample mean plot”, which is a graphical tool for global sensitivity analysis originally proposed by Sinclair. These graphical tools have a great potential to display graphically sensitivity information given a generic input sample and its related model realizations. The contribution to the sample variance can be obtained at no extra computational cost, i.e. from the same points used for deriving the contribution to the sample mean and/or scatter-plots. The proposed approach effectively instructs the analyst on how to achieve a targeted reduction of the variance, by operating on the extremes of the input parameters' ranges. The approach is tested against a known benchmark for sensitivity studies, the Ishigami test function, and a numerical model simulating the behaviour of a water hammer effect in a piping system.
Micropollutants throughout an integrated urban drainage model: Sensitivity and uncertainty analysis
Mannina, Giorgio; Cosenza, Alida; Viviani, Gaspare
2017-11-01
The paper presents the sensitivity and uncertainty analysis of an integrated urban drainage model which includes micropollutants. Specifically, a bespoke integrated model developed in previous studies has been modified in order to include the micropollutant assessment (namely, sulfamethoxazole - SMX). The model takes into account also the interactions between the three components of the system: sewer system (SS), wastewater treatment plant (WWTP) and receiving water body (RWB). The analysis has been applied to an experimental catchment nearby Palermo (Italy): the Nocella catchment. Overall, five scenarios, each characterized by different uncertainty combinations of sub-systems (i.e., SS, WWTP and RWB), have been considered applying, for the sensitivity analysis, the Extended-FAST method in order to select the key factors affecting the RWB quality and to design a reliable/useful experimental campaign. Results have demonstrated that sensitivity analysis is a powerful tool for increasing operator confidence in the modelling results. The approach adopted here can be used for blocking some non-identifiable factors, thus wisely modifying the structure of the model and reducing the related uncertainty. The model factors related to the SS have been found to be the most relevant factors affecting the SMX modeling in the RWB when all model factors (scenario 1) or model factors of SS (scenarios 2 and 3) are varied. If the only factors related to the WWTP are changed (scenarios 4 and 5), the SMX concentration in the RWB is mainly influenced (till to 95% influence of the total variance for SSMX,max) by the aerobic sorption coefficient. A progressive uncertainty reduction from the upstream to downstream was found for the soluble fraction of SMX in the RWB.
Adjoint sensitivity analysis of dynamic reliability models based on Markov chains - I: Theory
International Nuclear Information System (INIS)
Cacuci, D. G.; Cacuci, D. G.; Ionescu-Bujor, M.
2008-01-01
The development of the adjoint sensitivity analysis procedure (ASAP) for generic dynamic reliability models based on Markov chains is presented, together with applications of this procedure to the analysis of several systems of increasing complexity. The general theory is presented in Part I of this work and is accompanied by a paradigm application to the dynamic reliability analysis of a simple binary component, namely a pump functioning on an 'up/down' cycle until it fails irreparably. This paradigm example admits a closed form analytical solution, which permits a clear illustration of the main characteristics of the ASAP for Markov chains. In particular, it is shown that the ASAP for Markov chains presents outstanding computational advantages over other procedures currently in use for sensitivity and uncertainty analysis of the dynamic reliability of large-scale systems. This conclusion is further underscored by the large-scale applications presented in Part II. (authors)
Adjoint sensitivity analysis of dynamic reliability models based on Markov chains - I: Theory
Energy Technology Data Exchange (ETDEWEB)
Cacuci, D. G. [Commiss Energy Atom, Direct Energy Nucl, Saclay, (France); Cacuci, D. G. [Univ Karlsruhe, Inst Nucl Technol and Reactor Safety, D-76021 Karlsruhe, (Germany); Ionescu-Bujor, M. [Forschungszentrum Karlsruhe, Fus Program, D-76021 Karlsruhe, (Germany)
2008-07-01
The development of the adjoint sensitivity analysis procedure (ASAP) for generic dynamic reliability models based on Markov chains is presented, together with applications of this procedure to the analysis of several systems of increasing complexity. The general theory is presented in Part I of this work and is accompanied by a paradigm application to the dynamic reliability analysis of a simple binary component, namely a pump functioning on an 'up/down' cycle until it fails irreparably. This paradigm example admits a closed form analytical solution, which permits a clear illustration of the main characteristics of the ASAP for Markov chains. In particular, it is shown that the ASAP for Markov chains presents outstanding computational advantages over other procedures currently in use for sensitivity and uncertainty analysis of the dynamic reliability of large-scale systems. This conclusion is further underscored by the large-scale applications presented in Part II. (authors)
Kleijnen, J.P.C.
1995-01-01
This tutorial discusses what-if analysis and optimization of System Dynamics models. These problems are solved, using the statistical techniques of regression analysis and design of experiments (DOE). These issues are illustrated by applying the statistical techniques to a System Dynamics model for
International Nuclear Information System (INIS)
Lamoureux, Benjamin; Mechbal, Nazih; Massé, Jean-Rémi
2014-01-01
To increase the dependability of complex systems, one solution is to assess their state of health continuously through the monitoring of variables sensitive to potential degradation modes. When computed in an operating environment, these variables, known as health indicators, are subject to many uncertainties. Hence, the stochastic nature of health assessment combined with the lack of data in design stages makes it difficult to evaluate the efficiency of a health indicator before the system enters into service. This paper introduces a method for early validation of health indicators during the design stages of a system development process. This method uses physics-based modeling and uncertainties propagation to create simulated stochastic data. However, because of the large number of parameters defining the model and its computation duration, the necessary runtime for uncertainties propagation is prohibitive. Thus, kriging is used to obtain low computation time estimations of the model outputs. Moreover, sensitivity analysis techniques are performed upstream to determine the hierarchization of the model parameters and to reduce the dimension of the input space. The validation is based on three types of numerical key performance indicators corresponding to the detection, identification and prognostic processes. After having introduced and formalized the framework of uncertain systems modeling and the different performance metrics, the issues of sensitivity analysis and surrogate modeling are addressed. The method is subsequently applied to the validation of a set of health indicators for the monitoring of an aircraft engine’s pumping unit
Sensitivity analysis of the noble gas transport and fate model: CASCADR9
International Nuclear Information System (INIS)
Lindstrom, F.T.; Cawlfield, D.E.; Barker, L.E.
1994-03-01
CASCADR9 is a desert alluvial soil site-specific noble gas transport and fate model. Input parameters for CASCADR9 are: man-made source term, background concentration of radionuclides, radon half-life, soil porosity, period of barometric pressure wave, amplitude of barometric pressure wave, and effective eddy diffusivity. Using average flux, total flow, and radon concentration at the 40 day mark as output parameters, a sensitivity analysis for CASCADR9 is carried out, under a variety of scenarios. For each scenario, the parameter to which output parameters are most sensitive are identified
Toward a more robust variance-based global sensitivity analysis of model outputs
Energy Technology Data Exchange (ETDEWEB)
Tong, C
2007-10-15
Global sensitivity analysis (GSA) measures the variation of a model output as a function of the variations of the model inputs given their ranges. In this paper we consider variance-based GSA methods that do not rely on certain assumptions about the model structure such as linearity or monotonicity. These variance-based methods decompose the output variance into terms of increasing dimensionality called 'sensitivity indices', first introduced by Sobol' [25]. Sobol' developed a method of estimating these sensitivity indices using Monte Carlo simulations. McKay [13] proposed an efficient method using replicated Latin hypercube sampling to compute the 'correlation ratios' or 'main effects', which have been shown to be equivalent to Sobol's first-order sensitivity indices. Practical issues with using these variance estimators are how to choose adequate sample sizes and how to assess the accuracy of the results. This paper proposes a modified McKay main effect method featuring an adaptive procedure for accuracy assessment and improvement. We also extend our adaptive technique to the computation of second-order sensitivity indices. Details of the proposed adaptive procedure as wells as numerical results are included in this paper.
International Nuclear Information System (INIS)
Na, Jang-Hwan; Jeon, Ho-Jun; Hwang, Seok-Won
2015-01-01
In this paper, we focus on risk insights of Westinghouse typed reactors. We identified that Reactor Coolant Pump (RCP) seal integrity is the most important contributor to Core Damage Frequency (CDF). As we reflected the latest technical report; WCAP-15603(Rev. 1-A), 'WOG2000 RCP Seal Leakage Model for Westinghouse PWRs' instead of the old version, RCP seal integrity became more important to Westinghouse typed reactors. After Fukushima accidents, Korea Hydro and Nuclear Power (KHNP) decided to develop Low Power and Shutdown (LPSD) Probabilistic Safety Assessment (PSA) models and upgrade full power PSA models of all operating Nuclear Power Plants (NPPs). As for upgrading full power PSA models, we have tried to standardize the methodology of CCF (Common Cause Failure) and HRA (Human Reliability Analysis), which are the most influential factors to risk measures of NPPs. Also, we have reviewed and reflected the latest operating experiences, reliability data sources and technical methods to improve the quality of PSA models. KHNP has operating various types of reactors; Optimized Pressurized Reactor (OPR) 1000, CANDU, Framatome and Westinghouse. So, one of the most challengeable missions is to keep the balance of risk contributors of all types of reactors. This paper presents the method of new RCP seal leakage model and the sensitivity analysis results from applying the detailed method to PSA models of Westinghouse typed reference reactors. To perform the sensitivity analysis on LOCCW of the reference Westinghouse typed reactors, we reviewed WOG2000 RCP seal leakage model and developed the detailed event tree of LOCCW considering all scenarios of RCP seal failures. Also, we performed HRA based on the T/H analysis by using the leakage rates for each scenario. We could recognize that HRA was the sensitive contributor to CDF, and the RCP seal failure scenario of 182gpm leakage rate was estimated as the most important scenario
Energy Technology Data Exchange (ETDEWEB)
Na, Jang-Hwan; Jeon, Ho-Jun; Hwang, Seok-Won [KHNP Central Research Institute, Daejeon (Korea, Republic of)
2015-10-15
In this paper, we focus on risk insights of Westinghouse typed reactors. We identified that Reactor Coolant Pump (RCP) seal integrity is the most important contributor to Core Damage Frequency (CDF). As we reflected the latest technical report; WCAP-15603(Rev. 1-A), 'WOG2000 RCP Seal Leakage Model for Westinghouse PWRs' instead of the old version, RCP seal integrity became more important to Westinghouse typed reactors. After Fukushima accidents, Korea Hydro and Nuclear Power (KHNP) decided to develop Low Power and Shutdown (LPSD) Probabilistic Safety Assessment (PSA) models and upgrade full power PSA models of all operating Nuclear Power Plants (NPPs). As for upgrading full power PSA models, we have tried to standardize the methodology of CCF (Common Cause Failure) and HRA (Human Reliability Analysis), which are the most influential factors to risk measures of NPPs. Also, we have reviewed and reflected the latest operating experiences, reliability data sources and technical methods to improve the quality of PSA models. KHNP has operating various types of reactors; Optimized Pressurized Reactor (OPR) 1000, CANDU, Framatome and Westinghouse. So, one of the most challengeable missions is to keep the balance of risk contributors of all types of reactors. This paper presents the method of new RCP seal leakage model and the sensitivity analysis results from applying the detailed method to PSA models of Westinghouse typed reference reactors. To perform the sensitivity analysis on LOCCW of the reference Westinghouse typed reactors, we reviewed WOG2000 RCP seal leakage model and developed the detailed event tree of LOCCW considering all scenarios of RCP seal failures. Also, we performed HRA based on the T/H analysis by using the leakage rates for each scenario. We could recognize that HRA was the sensitive contributor to CDF, and the RCP seal failure scenario of 182gpm leakage rate was estimated as the most important scenario.
Tang, Niansheng; Chow, Sy-Miin; Ibrahim, Joseph G; Zhu, Hongtu
2017-12-01
Many psychological concepts are unobserved and usually represented as latent factors apprehended through multiple observed indicators. When multiple-subject multivariate time series data are available, dynamic factor analysis models with random effects offer one way of modeling patterns of within- and between-person variations by combining factor analysis and time series analysis at the factor level. Using the Dirichlet process (DP) as a nonparametric prior for individual-specific time series parameters further allows the distributional forms of these parameters to deviate from commonly imposed (e.g., normal or other symmetric) functional forms, arising as a result of these parameters' restricted ranges. Given the complexity of such models, a thorough sensitivity analysis is critical but computationally prohibitive. We propose a Bayesian local influence method that allows for simultaneous sensitivity analysis of multiple modeling components within a single fitting of the model of choice. Five illustrations and an empirical example are provided to demonstrate the utility of the proposed approach in facilitating the detection of outlying cases and common sources of misspecification in dynamic factor analysis models, as well as identification of modeling components that are sensitive to changes in the DP prior specification.
Energy Technology Data Exchange (ETDEWEB)
Sobolik, S.R.; Ho, C.K.; Dunn, E. [Sandia National Labs., Albuquerque, NM (United States); Robey, T.H. [Spectra Research Inst., Albuquerque, NM (United States); Cruz, W.T. [Univ. del Turabo, Gurabo (Puerto Rico)
1996-07-01
The Yucca Mountain Site Characterization Project is studying Yucca Mountain in southwestern Nevada as a potential site for a high-level nuclear waste repository. Site characterization includes surface- based and underground testing. Analyses have been performed to support the design of an Exploratory Studies Facility (ESF) and the design of the tests performed as part of the characterization process, in order to ascertain that they have minimal impact on the natural ability of the site to isolate waste. The information in this report pertains to sensitivity studies evaluating previous hydrological performance assessment analyses to variation in the material properties, conceptual models, and ventilation models, and the implications of this sensitivity on previous recommendations supporting ESF design. This document contains information that has been used in preparing recommendations for Appendix I of the Exploratory Studies Facility Design Requirements document.
International Nuclear Information System (INIS)
Sobolik, S.R.; Ho, C.K.; Dunn, E.; Robey, T.H.; Cruz, W.T.
1996-07-01
The Yucca Mountain Site Characterization Project is studying Yucca Mountain in southwestern Nevada as a potential site for a high-level nuclear waste repository. Site characterization includes surface- based and underground testing. Analyses have been performed to support the design of an Exploratory Studies Facility (ESF) and the design of the tests performed as part of the characterization process, in order to ascertain that they have minimal impact on the natural ability of the site to isolate waste. The information in this report pertains to sensitivity studies evaluating previous hydrological performance assessment analyses to variation in the material properties, conceptual models, and ventilation models, and the implications of this sensitivity on previous recommendations supporting ESF design. This document contains information that has been used in preparing recommendations for Appendix I of the Exploratory Studies Facility Design Requirements document
Trame, MN; Lesko, LJ
2015-01-01
A systems pharmacology model typically integrates pharmacokinetic, biochemical network, and systems biology concepts into a unifying approach. It typically consists of a large number of parameters and reaction species that are interlinked based upon the underlying (patho)physiology and the mechanism of drug action. The more complex these models are, the greater the challenge of reliably identifying and estimating respective model parameters. Global sensitivity analysis provides an innovative tool that can meet this challenge. CPT Pharmacometrics Syst. Pharmacol. (2015) 4, 69–79; doi:10.1002/psp4.6; published online 25 February 2015 PMID:27548289
Parameter estimation and sensitivity analysis for a mathematical model with time delays of leukemia
Cândea, Doina; Halanay, Andrei; Rǎdulescu, Rodica; Tǎlmaci, Rodica
2017-01-01
We consider a system of nonlinear delay differential equations that describes the interaction between three competing cell populations: healthy, leukemic and anti-leukemia T cells involved in Chronic Myeloid Leukemia (CML) under treatment with Imatinib. The aim of this work is to establish which model parameters are the most important in the success or failure of leukemia remission under treatment using a sensitivity analysis of the model parameters. For the most significant parameters of the model which affect the evolution of CML disease during Imatinib treatment we try to estimate the realistic values using some experimental data. For these parameters, steady states are calculated and their stability is analyzed and biologically interpreted.
A Fuel-Sensitive Reduced-Order Model (ROM) for Piston Engine Scaling Analysis
2017-09-29
of high Reynolds number nonreacting and reacting JP-8 sprays in a constant pressure flow vessel with a detailed chemistry approach . J Energy Resour...for rapid grid generation applied to in-cylinder diesel engine simulations. Society of Automotive Engineers ; 2007 Apr. SAE Technical Paper No.: 2007...ARL-TR-8172 ● Sep 2017 US Army Research Laboratory A Fuel-Sensitive Reduced-Order Model (ROM) for Piston Engine Scaling Analysis
Sensitivity analysis of model output - a step towards robust safety indicators?
International Nuclear Information System (INIS)
Broed, R.; Pereira, A.; Moberg, L.
2004-01-01
The protection of the environment from ionising radiation challenges the radioecological community with the issue of harmonising disparate safety indicators. These indicators should preferably cover the whole spectrum of model predictions on chemo-toxic and radiation impact of contaminants. In question is not only the protection of man and biota but also of abiotic systems. In many cases modelling will constitute the basis for an evaluation of potential impact. It is recognised that uncertainty and sensitivity analysis of model output will play an important role in the 'construction' of safety indicators that are robust, reliable and easy to explain to all groups of stakeholders including the general public. However, environmental models of transport of radionuclides have some extreme characteristics. They are, a) complex, b) non-linear, c) include a huge number of input parameters, d) input parameters are associated with large or very large uncertainties, e) parameters are often correlated to each other, f) uncertainties other than parameter-driven may be present in the modelling system, g) space variability and time-dependence of parameters are present, h) model predictions may cover geological time scales. Consequently, uncertainty and sensitivity analysis are non-trivial tasks, challenging the decision-maker when it comes to the interpretation of safety indicators or the application of regulatory criteria. In this work we use the IAEA model ISAM, to make a set of Monte Carlo calculations. The ISAM model includes several nuclides and decay chains, many compartments and variable parameters covering the range of nuclide migration pathways from the near field to the biosphere. The goal of our calculations is to make a global sensitivity analysis. After extracting the non-influential parameters, the M.C. calculations are repeated with those parameters frozen. Reducing the number of parameters to a few ones will simplify the interpretation of the results and the use
Sensitivity analysis of complex models: Coping with dynamic and static inputs
International Nuclear Information System (INIS)
Anstett-Collin, F.; Goffart, J.; Mara, T.; Denis-Vidal, L.
2015-01-01
In this paper, we address the issue of conducting a sensitivity analysis of complex models with both static and dynamic uncertain inputs. While several approaches have been proposed to compute the sensitivity indices of the static inputs (i.e. parameters), the one of the dynamic inputs (i.e. stochastic fields) have been rarely addressed. For this purpose, we first treat each dynamic as a Gaussian process. Then, the truncated Karhunen–Loève expansion of each dynamic input is performed. Such an expansion allows to generate independent Gaussian processes from a finite number of independent random variables. Given that a dynamic input is represented by a finite number of random variables, its variance-based sensitivity index is defined by the sensitivity index of this group of variables. Besides, an efficient sampling-based strategy is described to estimate the first-order indices of all the input factors by only using two input samples. The approach is applied to a building energy model, in order to assess the impact of the uncertainties of the material properties (static inputs) and the weather data (dynamic inputs) on the energy performance of a real low energy consumption house. - Highlights: • Sensitivity analysis of models with uncertain static and dynamic inputs is performed. • Karhunen–Loève (KL) decomposition of the spatio/temporal inputs is performed. • The influence of the dynamic inputs is studied through the modes of the KL expansion. • The proposed approach is applied to a building energy model. • Impact of weather data and material properties on performance of real house is given
Sensitivity analysis on a dose-calculation model for the terrestrial food-chain pathway
International Nuclear Information System (INIS)
Abdel-Aal, M.M.
1994-01-01
Parameter uncertainty and sensitivity were applied to the U.S. Regulatory Commission's (NRC) Regulatory Guide 1.109 (1977) models for calculating the ingestion dose via a terrestrial food-chain pathway in order to assess the transport of chronically released, low-level effluents from light-water reactors. In the analysis, we used the generation of latin hypercube samples (LHS) and employed a constrained sampling scheme. The generation of these samples is based on information supplied to the LHS program for variables or parameters. The actually sampled values are used to form vectors of variables that are commonly used as inputs to computer models for the purpose of sensitivity and uncertainty analysis. Regulatory models consider the concentrations of radionuclides that are deposited on plant tissues or lead to root uptake of nuclides initially deposited on soil. We also consider concentrations in milk and beef as a consequence of grazing on contaminated pasture or ingestion of contaminated feed by dairy and beef cattle. The radionuclides Sr-90 and Cs-137 were selected for evaluation. The most sensitive input parameters for the model were the ground-dispersion parameter, release rates of radionuclides, and soil-to-plant transfer coefficients of radionuclides. (Author)
Wang, Zhihui; Deisboeck, Thomas S.; Cristini, Vittorio
2014-01-01
There are two challenges that researchers face when performing global sensitivity analysis (GSA) on multiscale in silico cancer models. The first is increased computational intensity, since a multiscale cancer model generally takes longer to run than does a scale-specific model. The second problem is the lack of a best GSA method that fits all types of models, which implies that multiple methods and their sequence need to be taken into account. In this article, we therefore propose a sampling-based GSA workflow consisting of three phases – pre-analysis, analysis, and post-analysis – by integrating Monte Carlo and resampling methods with the repeated use of analysis of variance (ANOVA); we then exemplify this workflow using a two-dimensional multiscale lung cancer model. By accounting for all parameter rankings produced by multiple GSA methods, a summarized ranking is created at the end of the workflow based on the weighted mean of the rankings for each input parameter. For the cancer model investigated here, this analysis reveals that ERK, a downstream molecule of the EGFR signaling pathway, has the most important impact on regulating both the tumor volume and expansion rate in the algorithm used. PMID:25257020
Sensitivity analysis of Repast computational ecology models with R/Repast.
Prestes García, Antonio; Rodríguez-Patón, Alfonso
2016-12-01
Computational ecology is an emerging interdisciplinary discipline founded mainly on modeling and simulation methods for studying ecological systems. Among the existing modeling formalisms, the individual-based modeling is particularly well suited for capturing the complex temporal and spatial dynamics as well as the nonlinearities arising in ecosystems, communities, or populations due to individual variability. In addition, being a bottom-up approach, it is useful for providing new insights on the local mechanisms which are generating some observed global dynamics. Of course, no conclusions about model results could be taken seriously if they are based on a single model execution and they are not analyzed carefully. Therefore, a sound methodology should always be used for underpinning the interpretation of model results. The sensitivity analysis is a methodology for quantitatively assessing the effect of input uncertainty in the simulation output which should be incorporated compulsorily to every work based on in-silico experimental setup. In this article, we present R/Repast a GNU R package for running and analyzing Repast Simphony models accompanied by two worked examples on how to perform global sensitivity analysis and how to interpret the results.
International Nuclear Information System (INIS)
Sig Drellack, Lance Prothro
2007-01-01
The Underground Test Area (UGTA) Project of the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office is in the process of assessing and developing regulatory decision options based on modeling predictions of contaminant transport from underground testing of nuclear weapons at the Nevada Test Site (NTS). The UGTA Project is attempting to develop an effective modeling strategy that addresses and quantifies multiple components of uncertainty including natural variability, parameter uncertainty, conceptual/model uncertainty, and decision uncertainty in translating model results into regulatory requirements. The modeling task presents multiple unique challenges to the hydrological sciences as a result of the complex fractured and faulted hydrostratigraphy, the distributed locations of sources, the suite of reactive and non-reactive radionuclides, and uncertainty in conceptual models. Characterization of the hydrogeologic system is difficult and expensive because of deep groundwater in the arid desert setting and the large spatial setting of the NTS. Therefore, conceptual model uncertainty is partially addressed through the development of multiple alternative conceptual models of the hydrostratigraphic framework and multiple alternative models of recharge and discharge. Uncertainty in boundary conditions is assessed through development of alternative groundwater fluxes through multiple simulations using the regional groundwater flow model. Calibration of alternative models to heads and measured or inferred fluxes has not proven to provide clear measures of model quality. Therefore, model screening by comparison to independently-derived natural geochemical mixing targets through cluster analysis has also been invoked to evaluate differences between alternative conceptual models. Advancing multiple alternative flow models, sensitivity of transport predictions to parameter uncertainty is assessed through Monte Carlo simulations. The
Application of uncertainty and sensitivity analysis to the air quality SHERPA modelling tool
Pisoni, E.; Albrecht, D.; Mara, T. A.; Rosati, R.; Tarantola, S.; Thunis, P.
2018-06-01
Air quality has significantly improved in Europe over the past few decades. Nonetheless we still find high concentrations in measurements mainly in specific regions or cities. This dimensional shift, from EU-wide to hot-spot exceedances, calls for a novel approach to regional air quality management (to complement EU-wide existing policies). The SHERPA (Screening for High Emission Reduction Potentials on Air quality) modelling tool was developed in this context. It provides an additional tool to be used in support to regional/local decision makers responsible for the design of air quality plans. It is therefore important to evaluate the quality of the SHERPA model, and its behavior in the face of various kinds of uncertainty. Uncertainty and sensitivity analysis techniques can be used for this purpose. They both reveal the links between assumptions and forecasts, help in-model simplification and may highlight unexpected relationships between inputs and outputs. Thus, a policy steered SHERPA module - predicting air quality improvement linked to emission reduction scenarios - was evaluated by means of (1) uncertainty analysis (UA) to quantify uncertainty in the model output, and (2) by sensitivity analysis (SA) to identify the most influential input sources of this uncertainty. The results of this study provide relevant information about the key variables driving the SHERPA output uncertainty, and advise policy-makers and modellers where to place their efforts for an improved decision-making process.
DEFF Research Database (Denmark)
Sin, Gürkan; Gernaey, Krist; Eliasson Lantz, Anna
2009-01-01
The uncertainty and sensitivity analysis are evaluated for their usefulness as part of the model-building within Process Analytical Technology applications. A mechanistic model describing a batch cultivation of Streptomyces coelicolor for antibiotic production was used as case study. The input...... compared to the large uncertainty observed in the antibiotic and off-gas CO2 predictions. The output uncertainty was observed to be lower during the exponential growth phase, while higher in the stationary and death phases - meaning the model describes some periods better than others. To understand which...... promising for helping to build reliable mechanistic models and to interpret the model outputs properly. These tools make part of good modeling practice, which can contribute to successful PAT applications for increased process understanding, operation and control purposes. © 2009 American Institute...
Directory of Open Access Journals (Sweden)
W. Castaings
2009-04-01
Full Text Available Variational methods are widely used for the analysis and control of computationally intensive spatially distributed systems. In particular, the adjoint state method enables a very efficient calculation of the derivatives of an objective function (response function to be analysed or cost function to be optimised with respect to model inputs.
In this contribution, it is shown that the potential of variational methods for distributed catchment scale hydrology should be considered. A distributed flash flood model, coupling kinematic wave overland flow and Green Ampt infiltration, is applied to a small catchment of the Thoré basin and used as a relatively simple (synthetic observations but didactic application case.
It is shown that forward and adjoint sensitivity analysis provide a local but extensive insight on the relation between the assigned model parameters and the simulated hydrological response. Spatially distributed parameter sensitivities can be obtained for a very modest calculation effort (~6 times the computing time of a single model run and the singular value decomposition (SVD of the Jacobian matrix provides an interesting perspective for the analysis of the rainfall-runoff relation.
For the estimation of model parameters, adjoint-based derivatives were found exceedingly efficient in driving a bound-constrained quasi-Newton algorithm. The reference parameter set is retrieved independently from the optimization initial condition when the very common dimension reduction strategy (i.e. scalar multipliers is adopted.
Furthermore, the sensitivity analysis results suggest that most of the variability in this high-dimensional parameter space can be captured with a few orthogonal directions. A parametrization based on the SVD leading singular vectors was found very promising but should be combined with another regularization strategy in order to prevent overfitting.
Sensitivity analysis of an individual-based model for simulation of influenza epidemics.
Directory of Open Access Journals (Sweden)
Elaine O Nsoesie
Full Text Available Individual-based epidemiology models are increasingly used in the study of influenza epidemics. Several studies on influenza dynamics and evaluation of intervention measures have used the same incubation and infectious period distribution parameters based on the natural history of influenza. A sensitivity analysis evaluating the influence of slight changes to these parameters (in addition to the transmissibility would be useful for future studies and real-time modeling during an influenza pandemic.In this study, we examined individual and joint effects of parameters and ranked parameters based on their influence on the dynamics of simulated epidemics. We also compared the sensitivity of the model across synthetic social networks for Montgomery County in Virginia and New York City (and surrounding metropolitan regions with demographic and rural-urban differences. In addition, we studied the effects of changing the mean infectious period on age-specific epidemics. The research was performed from a public health standpoint using three relevant measures: time to peak, peak infected proportion and total attack rate. We also used statistical methods in the design and analysis of the experiments. The results showed that: (i minute changes in the transmissibility and mean infectious period significantly influenced the attack rate; (ii the mean of the incubation period distribution appeared to be sufficient for determining its effects on the dynamics of epidemics; (iii the infectious period distribution had the strongest influence on the structure of the epidemic curves; (iv the sensitivity of the individual-based model was consistent across social networks investigated in this study and (v age-specific epidemics were sensitive to changes in the mean infectious period irrespective of the susceptibility of the other age groups. These findings suggest that small changes in some of the disease model parameters can significantly influence the uncertainty
Sensitivity analysis using the FRAPCON-1/EM: development of a calculation model for licensing
International Nuclear Information System (INIS)
Chapot, J.L.C.
1985-01-01
The FRAPCON-1/EM is version of the FRAPCON-1 code which analyses fuel rods performance under normal operation conditions. This version yields conservative results and is used by the NRC in its licensing activities. A sensitivity analysis was made, to determine the combination of models from the FRAPCON-1/EM which yields the most conservative results for a typical Angra-1 reactor fuel rod. The present analysis showed that this code can be used as a calculation tool for the licensing of the Angra-1 reload. (F.E.) [pt
Numerical model analysis of thermal performance for a dye-sensitized solar cell module
International Nuclear Information System (INIS)
Chen, Shuanghong; Huang, Yang; Weng, Jian; Fan, Xiaqin; Mo, Lie; Pan, Bin; Dai, Songyuan
2013-01-01
Temperature is one of the major factors that influence a dye-sensitized solar cell's (DSC's) photovoltaic efficiency. Temperature control is very important when solar cell modules are designed. In the present paper, a numerical model of a DSC module is built for the simulation of the solar cell's temperature. In this model, energy balance and three methods of heat transfer (conduction, convection, and radiation) are taken into account, and the simulation results are consistent with the experimental results. The influence of wind speeds and interfacial thermal resistance on the temperature inside the DSC modules is discussed in detail based on theoretical analysis. (paper)
Sensor selection of helicopter transmission systems based on physical model and sensitivity analysis
Directory of Open Access Journals (Sweden)
Lyu Kehong
2014-06-01
Full Text Available In the helicopter transmission systems, it is important to monitor and track the tooth damage evolution using lots of sensors and detection methods. This paper develops a novel approach for sensor selection based on physical model and sensitivity analysis. Firstly, a physical model of tooth damage and mesh stiffness is built. Secondly, some effective condition indicators (CIs are presented, and the optimal CIs set is selected by comparing their test statistics according to Mann–Kendall test. Afterwards, the selected CIs are used to generate a health indicator (HI through sen slop estimator. Then, the sensors are selected according to the monotonic relevance and sensitivity to the damage levels. Finally, the proposed method is verified by the simulation and experimental data. The results show that the approach can provide a guide for health monitoring of helicopter transmission systems, and it is effective to reduce the test cost and improve the system’s reliability.
Parameter sensitivity and uncertainty of the forest carbon flux model FORUG : a Monte Carlo analysis
Energy Technology Data Exchange (ETDEWEB)
Verbeeck, H.; Samson, R.; Lemeur, R. [Ghent Univ., Ghent (Belgium). Laboratory of Plant Ecology; Verdonck, F. [Ghent Univ., Ghent (Belgium). Dept. of Applied Mathematics, Biometrics and Process Control
2006-06-15
The FORUG model is a multi-layer process-based model that simulates carbon dioxide (CO{sub 2}) and water exchange between forest stands and the atmosphere. The main model outputs are net ecosystem exchange (NEE), total ecosystem respiration (TER), gross primary production (GPP) and evapotranspiration. This study used a sensitivity analysis to identify the parameters contributing to NEE uncertainty in the FORUG model. The aim was to determine if it is necessary to estimate the uncertainty of all parameters of a model to determine overall output uncertainty. Data used in the study were the meteorological and flux data of beech trees in Hesse. The Monte Carlo method was used to rank sensitivity and uncertainty parameters in combination with a multiple linear regression. Simulations were run in which parameters were assigned probability distributions and the effect of variance in the parameters on the output distribution was assessed. The uncertainty of the output for NEE was estimated. Based on the arbitrary uncertainty of 10 key parameters, a standard deviation of 0.88 Mg C per year per NEE was found, which was equal to 24 per cent of the mean value of NEE. The sensitivity analysis showed that the overall output uncertainty of the FORUG model could be determined by accounting for only a few key parameters, which were identified as corresponding to critical parameters in the literature. It was concluded that the 10 most important parameters determined more than 90 per cent of the output uncertainty. High ranking parameters included soil respiration; photosynthesis; and crown architecture. It was concluded that the Monte Carlo technique is a useful tool for ranking the uncertainty of parameters of process-based forest flux models. 48 refs., 2 tabs., 2 figs.
Hydraulic head interpolation using ANFIS—model selection and sensitivity analysis
Kurtulus, Bedri; Flipo, Nicolas
2012-01-01
The aim of this study is to investigate the efficiency of ANFIS (adaptive neuro fuzzy inference system) for interpolating hydraulic head in a 40-km 2 agricultural watershed of the Seine basin (France). Inputs of ANFIS are Cartesian coordinates and the elevation of the ground. Hydraulic head was measured at 73 locations during a snapshot campaign on September 2009, which characterizes low-water-flow regime in the aquifer unit. The dataset was then split into three subsets using a square-based selection method: a calibration one (55%), a training one (27%), and a test one (18%). First, a method is proposed to select the best ANFIS model, which corresponds to a sensitivity analysis of ANFIS to the type and number of membership functions (MF). Triangular, Gaussian, general bell, and spline-based MF are used with 2, 3, 4, and 5 MF per input node. Performance criteria on the test subset are used to select the 5 best ANFIS models among 16. Then each is used to interpolate the hydraulic head distribution on a (50×50)-m grid, which is compared to the soil elevation. The cells where the hydraulic head is higher than the soil elevation are counted as "error cells." The ANFIS model that exhibits the less "error cells" is selected as the best ANFIS model. The best model selection reveals that ANFIS models are very sensitive to the type and number of MF. Finally, a sensibility analysis of the best ANFIS model with four triangular MF is performed on the interpolation grid, which shows that ANFIS remains stable to error propagation with a higher sensitivity to soil elevation.
Lim, J. T.; Gold, H. J.; Wilkerson, G. G.; Raper, C. D. Jr; Raper CD, J. r. (Principal Investigator)
1989-01-01
We describe the application of a strategy for conducting a sensitivity analysis for a complex dynamic model. The procedure involves preliminary screening of parameter sensitivities by numerical estimation of linear sensitivity coefficients, followed by generation of a response surface based on Monte Carlo simulation. Application is to a physiological model of the vegetative growth of soybean plants. The analysis provides insights as to the relative importance of certain physiological processes in controlling plant growth. Advantages and disadvantages of the strategy are discussed.
Global sensitivity analysis in the identification of cohesive models using full-field kinematic data
Alfano, Marco; Lubineau, Gilles; Paulino, Glá ucio Hermogenes
2015-01-01
Failure of adhesive bonded structures often occurs concurrent with the formation of a non-negligible fracture process zone in front of a macroscopic crack. For this reason, the analysis of damage and fracture is effectively carried out using the cohesive zone model (CZM). The crucial aspect of the CZM approach is the precise determination of the traction-separation relation. Yet it is usually determined empirically, by using calibration procedures combining experimental data, such as load-displacement or crack length data, with finite element simulation of fracture. Thanks to the recent progress in image processing, and the availability of low-cost CCD cameras, it is nowadays relatively easy to access surface displacements across the fracture process zone using for instance Digital Image Correlation (DIC). The rich information provided by correlation techniques prompted the development of versatile inverse parameter identification procedures combining finite element (FE) simulations and full field kinematic data. The focus of the present paper is to assess the effectiveness of these methods in the identification of cohesive zone models. In particular, the analysis is developed in the framework of the variance based global sensitivity analysis. The sensitivity of kinematic data to the sought cohesive properties is explored through the computation of the so-called Sobol sensitivity indexes. The results show that the global sensitivity analysis can help to ascertain the most influential cohesive parameters which need to be incorporated in the identification process. In addition, it is shown that suitable displacement sampling in time and space can lead to optimized measurements for identification purposes.
Global sensitivity analysis in the identification of cohesive models using full-field kinematic data
Alfano, Marco
2015-03-01
Failure of adhesive bonded structures often occurs concurrent with the formation of a non-negligible fracture process zone in front of a macroscopic crack. For this reason, the analysis of damage and fracture is effectively carried out using the cohesive zone model (CZM). The crucial aspect of the CZM approach is the precise determination of the traction-separation relation. Yet it is usually determined empirically, by using calibration procedures combining experimental data, such as load-displacement or crack length data, with finite element simulation of fracture. Thanks to the recent progress in image processing, and the availability of low-cost CCD cameras, it is nowadays relatively easy to access surface displacements across the fracture process zone using for instance Digital Image Correlation (DIC). The rich information provided by correlation techniques prompted the development of versatile inverse parameter identification procedures combining finite element (FE) simulations and full field kinematic data. The focus of the present paper is to assess the effectiveness of these methods in the identification of cohesive zone models. In particular, the analysis is developed in the framework of the variance based global sensitivity analysis. The sensitivity of kinematic data to the sought cohesive properties is explored through the computation of the so-called Sobol sensitivity indexes. The results show that the global sensitivity analysis can help to ascertain the most influential cohesive parameters which need to be incorporated in the identification process. In addition, it is shown that suitable displacement sampling in time and space can lead to optimized measurements for identification purposes.
Bedane, T.; Di Maio, L.; Scarfato, P.; Incarnato, L.; Marra, F.
2015-12-01
The barrier performance of multilayer polymeric films for food applications has been significantly improved by incorporating oxygen scavenging materials. The scavenging activity depends on parameters such as diffusion coefficient, solubility, concentration of scavenger loaded and the number of available reactive sites. These parameters influence the barrier performance of the film in different ways. Virtualization of the process is useful to characterize, design and optimize the barrier performance based on physical configuration of the films. Also, the knowledge of values of parameters is important to predict the performances. Inverse modeling and sensitivity analysis are sole way to find reasonable values of poorly defined, unmeasured parameters and to analyze the most influencing parameters. Thus, the objective of this work was to develop a model to predict barrier properties of multilayer film incorporated with reactive layers and to analyze and characterize their performances. Polymeric film based on three layers of Polyethylene terephthalate (PET), with a core reactive layer, at different thickness configurations was considered in the model. A one dimensional diffusion equation with reaction was solved numerically to predict the concentration of oxygen diffused into the polymer taking into account the reactive ability of the core layer. The model was solved using commercial software for different film layer configurations and sensitivity analysis based on inverse modeling was carried out to understand the effect of physical parameters. The results have shown that the use of sensitivity analysis can provide physical understanding of the parameters which highly affect the gas permeation into the film. Solubility and the number of available reactive sites were the factors mainly influencing the barrier performance of three layered polymeric film. Multilayer films slightly modified the steady transport properties in comparison to net PET, giving a small reduction
Sensitivity and Uncertainty Analysis for coolant void reactivity in a CANDU Fuel Lattice Cell Model
Energy Technology Data Exchange (ETDEWEB)
Yoo, Seung Yeol; Shim, Hyung Jin [Seoul National University, Seoul (Korea, Republic of)
2016-10-15
In this study, the EPBM is implemented in Seoul National university Monte Carlo (MC) code, McCARD which has the k uncertainty evaluation capability by the adjoint-weighted perturbation (AWP) method. The implementation is verified by comparing the sensitivities of the k-eigenvalue difference to the microscopic cross sections computed by the DPBM and the direct subtractions for the TMI-1 pin-cell problem. The uncertainty of the coolant void reactivity (CVR) in a CANDU fuel lattice model due to the ENDF/B-VII.1 covariance data is calculated by its sensitivities estimated by the EPBM. The method based on the eigenvalue perturbation theory (EPBM) utilizes the 1st order adjoint-weighted perturbation (AWP) technique to estimate the sensitivity of the eigenvalue difference. Furthermore this method can be easily applied in a S/U analysis code system equipped with the eigenvalue sensitivity calculation capability. The EPBM is implemented in McCARD code and verified by showing good agreement with reference solution. Then the McCARD S/U analysis have been performed with the EPBM module for the CVR in CANDU fuel lattice problem. It shows that the uncertainty contributions of nu of {sup 235}U and gamma reaction of {sup 238}U are dominant.
Designing novel cellulase systems through agent-based modeling and global sensitivity analysis
Apte, Advait A; Senger, Ryan S; Fong, Stephen S
2014-01-01
Experimental techniques allow engineering of biological systems to modify functionality; however, there still remains a need to develop tools to prioritize targets for modification. In this study, agent-based modeling (ABM) was used to build stochastic models of complexed and non-complexed cellulose hydrolysis, including enzymatic mechanisms for endoglucanase, exoglucanase, and β-glucosidase activity. Modeling results were consistent with experimental observations of higher efficiency in complexed systems than non-complexed systems and established relationships between specific cellulolytic mechanisms and overall efficiency. Global sensitivity analysis (GSA) of model results identified key parameters for improving overall cellulose hydrolysis efficiency including: (1) the cellulase half-life, (2) the exoglucanase activity, and (3) the cellulase composition. Overall, the following parameters were found to significantly influence cellulose consumption in a consolidated bioprocess (CBP): (1) the glucose uptake rate of the culture, (2) the bacterial cell concentration, and (3) the nature of the cellulase enzyme system (complexed or non-complexed). Broadly, these results demonstrate the utility of combining modeling and sensitivity analysis to identify key parameters and/or targets for experimental improvement. PMID:24830736
Uncertainty and sensitivity analysis of fission gas behavior in engineering-scale fuel modeling
Energy Technology Data Exchange (ETDEWEB)
Pastore, Giovanni, E-mail: Giovanni.Pastore@inl.gov [Fuel Modeling and Simulation, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, ID 83415-3840 (United States); Swiler, L.P., E-mail: LPSwile@sandia.gov [Optimization and Uncertainty Quantification, Sandia National Laboratories, P.O. Box 5800, Albuquerque, NM 87185-1318 (United States); Hales, J.D., E-mail: Jason.Hales@inl.gov [Fuel Modeling and Simulation, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, ID 83415-3840 (United States); Novascone, S.R., E-mail: Stephen.Novascone@inl.gov [Fuel Modeling and Simulation, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, ID 83415-3840 (United States); Perez, D.M., E-mail: Danielle.Perez@inl.gov [Fuel Modeling and Simulation, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, ID 83415-3840 (United States); Spencer, B.W., E-mail: Benjamin.Spencer@inl.gov [Fuel Modeling and Simulation, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, ID 83415-3840 (United States); Luzzi, L., E-mail: Lelio.Luzzi@polimi.it [Politecnico di Milano, Department of Energy, Nuclear Engineering Division, via La Masa 34, I-20156 Milano (Italy); Van Uffelen, P., E-mail: Paul.Van-Uffelen@ec.europa.eu [European Commission, Joint Research Centre, Institute for Transuranium Elements, Hermann-von-Helmholtz-Platz 1, D-76344 Karlsruhe (Germany); Williamson, R.L., E-mail: Richard.Williamson@inl.gov [Fuel Modeling and Simulation, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, ID 83415-3840 (United States)
2015-01-15
The role of uncertainties in fission gas behavior calculations as part of engineering-scale nuclear fuel modeling is investigated using the BISON fuel performance code with a recently implemented physics-based model for fission gas release and swelling. Through the integration of BISON with the DAKOTA software, a sensitivity analysis of the results to selected model parameters is carried out based on UO{sub 2} single-pellet simulations covering different power regimes. The parameters are varied within ranges representative of the relative uncertainties and consistent with the information in the open literature. The study leads to an initial quantitative assessment of the uncertainty in fission gas behavior predictions with the parameter characterization presently available. Also, the relative importance of the single parameters is evaluated. Moreover, a sensitivity analysis is carried out based on simulations of a fuel rod irradiation experiment, pointing out a significant impact of the considered uncertainties on the calculated fission gas release and cladding diametral strain. The results of the study indicate that the commonly accepted deviation between calculated and measured fission gas release by a factor of 2 approximately corresponds to the inherent modeling uncertainty at high fission gas release. Nevertheless, significantly higher deviations may be expected for values around 10% and lower. Implications are discussed in terms of directions of research for the improved modeling of fission gas behavior for engineering purposes.
Designing novel cellulase systems through agent-based modeling and global sensitivity analysis.
Apte, Advait A; Senger, Ryan S; Fong, Stephen S
2014-01-01
Experimental techniques allow engineering of biological systems to modify functionality; however, there still remains a need to develop tools to prioritize targets for modification. In this study, agent-based modeling (ABM) was used to build stochastic models of complexed and non-complexed cellulose hydrolysis, including enzymatic mechanisms for endoglucanase, exoglucanase, and β-glucosidase activity. Modeling results were consistent with experimental observations of higher efficiency in complexed systems than non-complexed systems and established relationships between specific cellulolytic mechanisms and overall efficiency. Global sensitivity analysis (GSA) of model results identified key parameters for improving overall cellulose hydrolysis efficiency including: (1) the cellulase half-life, (2) the exoglucanase activity, and (3) the cellulase composition. Overall, the following parameters were found to significantly influence cellulose consumption in a consolidated bioprocess (CBP): (1) the glucose uptake rate of the culture, (2) the bacterial cell concentration, and (3) the nature of the cellulase enzyme system (complexed or non-complexed). Broadly, these results demonstrate the utility of combining modeling and sensitivity analysis to identify key parameters and/or targets for experimental improvement.
International Nuclear Information System (INIS)
Simonson, S.A.; Ballinger, R.G.; Christensen, R.A.
1990-01-01
Irradiation of an aqueous environment results, in general, in a steady state concentration of oxidizing chemical species in solution. Although the effect may be beneficial to the metal in contact with the solution in some cases, say by producing a more protective film, it is generally believed to be detrimental. The ability to predict the concentrations of the oxidizing species and from this begin to analyze the detrimental behavior on the metals requires computer codes that model the chemical reactions, production rates, and diffusion characteristics of the species being produced by irradiation. The large number of parameters and the complexity of the interactions involved in the predictions of irradiation effects on metals degradation requires a more sophisticated approach to determining the sensitivities of the final results. Monte Carlo techniques are too computationally intensive for practical use in determining sensitivities. The paper presents an approach, adjoint sensitivity analysis, that is more practical, i.e., three computer runs versus thousands, and also a more accurate measure of the sensitivities of the model
Uncertainty and Sensitivity Analysis for an Ibuprofen Synthesis Model Based on Hoechst Path
DEFF Research Database (Denmark)
da Conceicao Do Carmo Montes, Frederico; Gernaey, Krist V.; Sin, Gürkan
2017-01-01
into consideration the effects of temperature, acidity, and the choice of the catalyst. Parameter estimation and uncertainty analysis were conducted on the kinetic model parameters using experimental data available in the literature. Finally, one factor at a time sensitivity analysis in the form of deviations......The pharmaceutical industry faces several challenges and barriers when implementing new or improving current pharmaceutical processes, such as competition from generic drug manufacturers and stricter regulations from the U.S. Food and Drug Administration and the European Medicine agency. The demand...... for efficient and reliable models to simulate and design/improve pharmaceutical processes is therefore increasing. For the case of ibuprofen, a well-known anti-inflammatory drug, the existing models do not include its complete synthesis path, usually referring only to one out of aset of different reactions...
Matschek, Janine; Bullinger, Eric; von Haeseler, Friedrich; Skalej, Martin; Findeisen, Rolf
2017-02-01
Radiofrequency ablation is a valuable tool in the treatment of many diseases, especially cancer. However, controlled heating up to apoptosis of the desired target tissue in complex situations, e.g. in the spine, is challenging and requires experienced interventionalists. For such challenging situations a mathematical model of radiofrequency ablation allows to understand, improve and optimise the outcome of the medical therapy. The main contribution of this work is the derivation of a tailored, yet expandable mathematical model, for the simulation, analysis, planning and control of radiofrequency ablation in complex situations. The dynamic model consists of partial differential equations that describe the potential and temperature distribution during intervention. To account for multipolar operation, time-dependent boundary conditions are introduced. Spatially distributed parameters, like tissue conductivity and blood perfusion, allow to describe the complex 3D environment representing diverse involved tissue types in the spine. To identify the key parameters affecting the prediction quality of the model, the influence of the parameters on the temperature distribution is investigated via a sensitivity analysis. Simulations underpin the quality of the derived model and the analysis approach. The proposed modelling and analysis schemes set the basis for intervention planning, state- and parameter estimation, and control. Copyright © 2016. Published by Elsevier Inc.
International Nuclear Information System (INIS)
Marrel, A.; Marie, N.; De Lozzo, M.
2015-01-01
Within the framework of the generation IV Sodium Fast Reactors, the safety in case of severe accidents is assessed. From this statement, CEA has developed a new physical tool to model the accident initiated by the Total Instantaneous Blockage (TIB) of a sub-assembly. This TIB simulator depends on many uncertain input parameters. This paper aims at proposing a global methodology combining several advanced statistical techniques in order to perform a global sensitivity analysis of this TIB simulator. The objective is to identify the most influential uncertain inputs for the various TIB outputs involved in the safety analysis. The proposed statistical methodology combining several advanced statistical techniques enables to take into account the constraints on the TIB simulator outputs (positivity constraints) and to deal simultaneously with various outputs. To do this, a space-filling design is used and the corresponding TIB model simulations are performed. Based on this learning sample, an efficient constrained Gaussian process metamodel is fitted on each TIB model outputs. Then, using the metamodels, classical sensitivity analyses are made for each TIB output. Multivariate global sensitivity analyses based on aggregated indices are also performed, providing additional valuable information. Main conclusions on the influence of each uncertain input are derived. - Highlights: • Physical-statistical tool for Sodium Fast Reactors TIB accident. • 27 uncertain parameters (core state, lack of physical knowledge) are highlighted. • Constrained Gaussian process efficiently predicts TIB outputs (safety criteria). • Multivariate sensitivity analyses reveal that three inputs are mainly influential. • The type of corium propagation (thermal or hydrodynamic) is the most influential
Global sensitivity analysis of a dynamic model for gene expression in Drosophila embryos
McCarthy, Gregory D.; Drewell, Robert A.
2015-01-01
It is well known that gene regulation is a tightly controlled process in early organismal development. However, the roles of key processes involved in this regulation, such as transcription and translation, are less well understood, and mathematical modeling approaches in this field are still in their infancy. In recent studies, biologists have taken precise measurements of protein and mRNA abundance to determine the relative contributions of key factors involved in regulating protein levels in mammalian cells. We now approach this question from a mathematical modeling perspective. In this study, we use a simple dynamic mathematical model that incorporates terms representing transcription, translation, mRNA and protein decay, and diffusion in an early Drosophila embryo. We perform global sensitivity analyses on this model using various different initial conditions and spatial and temporal outputs. Our results indicate that transcription and translation are often the key parameters to determine protein abundance. This observation is in close agreement with the experimental results from mammalian cells for various initial conditions at particular time points, suggesting that a simple dynamic model can capture the qualitative behavior of a gene. Additionally, we find that parameter sensitivites are temporally dynamic, illustrating the importance of conducting a thorough global sensitivity analysis across multiple time points when analyzing mathematical models of gene regulation. PMID:26157608
Sensitivity analysis using two-dimensional models of the Whiteshell geosphere
Energy Technology Data Exchange (ETDEWEB)
Scheier, N. W.; Chan, T.; Stanchell, F. W.
1992-12-01
As part of the assessment of the environmental impact of disposing of immobilized nuclear fuel waste in a vault deep within plutonic rock, detailed modelling of groundwater flow, heat transport and containment transport through the geosphere is being performed using the MOTIF finite-element computer code. The first geosphere model is being developed using data from the Whiteshell Research Area, with a hypothetical disposal vault at a depth of 500 m. This report briefly describes the conceptual model and then describes in detail the two-dimensional simulations used to help initially define an adequate three-dimensional representation, select a suitable form for the simplified model to be used in the overall systems assessment with the SYVAC computer code, and perform some sensitivity analysis. The sensitivity analysis considers variations in the rock layer properties, variations in fracture zone configurations, the impact of grouting a vault/fracture zone intersection, and variations in boundary conditions. This study shows that the configuration of major fracture zones can have a major influence on groundwater flow patterns. The flows in the major fracture zones can have high velocities and large volumes. The proximity of the radionuclide source to a major fracture zone may strongly influence the time it takes for a radionuclide to be transported to the surface. (auth)
Bayesian Sensitivity Analysis of a Cardiac Cell Model Using a Gaussian Process Emulator
Chang, Eugene T Y; Strong, Mark; Clayton, Richard H
2015-01-01
Models of electrical activity in cardiac cells have become important research tools as they can provide a quantitative description of detailed and integrative physiology. However, cardiac cell models have many parameters, and how uncertainties in these parameters affect the model output is difficult to assess without undertaking large numbers of model runs. In this study we show that a surrogate statistical model of a cardiac cell model (the Luo-Rudy 1991 model) can be built using Gaussian process (GP) emulators. Using this approach we examined how eight outputs describing the action potential shape and action potential duration restitution depend on six inputs, which we selected to be the maximum conductances in the Luo-Rudy 1991 model. We found that the GP emulators could be fitted to a small number of model runs, and behaved as would be expected based on the underlying physiology that the model represents. We have shown that an emulator approach is a powerful tool for uncertainty and sensitivity analysis in cardiac cell models. PMID:26114610
Energy Technology Data Exchange (ETDEWEB)
Avila Moreno, R.; Barrdahl, R.; Haegg, C.
1995-05-01
The main objective of the present study was to carry out a screening and a sensitivity analysis of the SSI TOOLBOX source term model SOSIM. This model is a part of the SSI TOOLBOX for radiological impact assessment of the Swedish disposal concept for high-level waste KBS-3. The outputs of interest for this purpose were: the total released fraction, the time of total release, the time and value of maximum release rate, the dose rates after direct releases of the biosphere. The source term equations were derived and simple equations and methods were proposed for calculation of these. A literature survey has been performed in order to determine a characteristic variation range and a nominal value for each model parameter. In order to reduce the model uncertainties the authors recommend a change in the initial boundary condition for solution of the diffusion equation for highly soluble nuclides. 13 refs.
Sensitivity analysis and calibration of a dynamic physically based slope stability model
Zieher, Thomas; Rutzinger, Martin; Schneider-Muntau, Barbara; Perzl, Frank; Leidinger, David; Formayer, Herbert; Geitner, Clemens
2017-06-01
Physically based modelling of slope stability on a catchment scale is still a challenging task. When applying a physically based model on such a scale (1 : 10 000 to 1 : 50 000), parameters with a high impact on the model result should be calibrated to account for (i) the spatial variability of parameter values, (ii) shortcomings of the selected model, (iii) uncertainties of laboratory tests and field measurements or (iv) parameters that cannot be derived experimentally or measured in the field (e.g. calibration constants). While systematic parameter calibration is a common task in hydrological modelling, this is rarely done using physically based slope stability models. In the present study a dynamic, physically based, coupled hydrological-geomechanical slope stability model is calibrated based on a limited number of laboratory tests and a detailed multitemporal shallow landslide inventory covering two landslide-triggering rainfall events in the Laternser valley, Vorarlberg (Austria). Sensitive parameters are identified based on a local one-at-a-time sensitivity analysis. These parameters (hydraulic conductivity, specific storage, angle of internal friction for effective stress, cohesion for effective stress) are systematically sampled and calibrated for a landslide-triggering rainfall event in August 2005. The identified model ensemble, including 25 behavioural model runs with the highest portion of correctly predicted landslides and non-landslides, is then validated with another landslide-triggering rainfall event in May 1999. The identified model ensemble correctly predicts the location and the supposed triggering timing of 73.0 % of the observed landslides triggered in August 2005 and 91.5 % of the observed landslides triggered in May 1999. Results of the model ensemble driven with raised precipitation input reveal a slight increase in areas potentially affected by slope failure. At the same time, the peak run-off increases more markedly, suggesting that
Probabilistic sensitivity analysis for the 'initial defect in the canister' reference model
International Nuclear Information System (INIS)
Cormenzana, J. L.
2013-08-01
In Posiva Oy's Safety Case 'TURVA-2012' the repository system scenarios leading to radionuclide releases have been identified in Formulation of Radionuclide Release Scenarios. Three potential causes of canister failure and radionuclide release are considered: (i) the presence of an initial defect in the copper shell of one canister that penetrates the shell completely, (ii) corrosion of the copper overpack, that occurs more rapidly if buffer density is reduced, e.g. by erosion, (iii) shear movement on fractures intersecting the deposition hole. All three failure modes are analysed deterministically in Assessment of Radionuclide Release Scenarios, and for the 'initial defect in the canister' reference model a probabilistic sensitivity analysis (PSA) has been carried out. The main steps of the PSA have been: quantification of the uncertainties in the model input parameters through the creation of probability density distributions (PDFs), Monte Carlo simulations of the evolution of the system up to 106 years using parameters values sampled from the previous PDFs. Monte Carlo simulations with 10,000 individual calculations (realisations) have been used in the PSA, quantification of the uncertainty in the model outputs due to uncertainty in the input parameters (uncertainty analysis), and identification of the parameters whose uncertainty have the greatest effect on the uncertainty in the model outputs (sensitivity analysis) Since the biosphere is not included in the Monte Carlo simulations of the system, the model outputs studied are not doses, but total and radionuclide-specific normalised release rates from the near-field and to the biosphere. These outputs are calculated dividing the activity release rates by the constraints on the activity fluxes to the environment set out by the Finnish regulator. Two different cases are analysed in the PSA: (i) the 'hole forever' case, in which the small hole through the copper overpack remains unchanged during the assessment
Girard, Sylvain; Mallet, Vivien; Korsakissok, Irène; Mathieu, Anne
2016-04-01
Simulations of the atmospheric dispersion of radionuclides involve large uncertainties originating from the limited knowledge of meteorological input data, composition, amount and timing of emissions, and some model parameters. The estimation of these uncertainties is an essential complement to modeling for decision making in case of an accidental release. We have studied the relative influence of a set of uncertain inputs on several outputs from the Eulerian model Polyphemus/Polair3D on the Fukushima case. We chose to use the variance-based sensitivity analysis method of Sobol'. This method requires a large number of model evaluations which was not achievable directly due to the high computational cost of Polyphemus/Polair3D. To circumvent this issue, we built a mathematical approximation of the model using Gaussian process emulation. We observed that aggregated outputs are mainly driven by the amount of emitted radionuclides, while local outputs are mostly sensitive to wind perturbations. The release height is notably influential, but only in the vicinity of the source. Finally, averaging either spatially or temporally tends to cancel out interactions between uncertain inputs.
Sensitive analysis and modifications to reflood-related constitutive models of RELAP5
International Nuclear Information System (INIS)
Li Dong; Liu Xiaojing; Yang Yanhua
2014-01-01
Previous system code calculation reveals that the cladding temperature is underestimated and quench front appears too early during reflood process. To find out the parameters shows important effect on the results, sensitive analysis is performed on parameters of constitutive physical models. Based on the phenomenological and theoretical analysis, four parameters are selected: wall to vapor film boiling heat transfer coefficient, wall to liquid film boiling heat transfer coefficient, dry wall interfacial friction coefficient and minimum droplet diameter. In order to improve the reflood simulation ability of RELAP5 code, the film boiling heat transfer model and dry wall interfacial friction model which are corresponding models of those influential parameters are studied. Modifications have been made and installed into RELAP5 code. Six tests of FEBA are simulated by RELAP5 to study the predictability of reflood-related physical models. A dispersed flow film boiling heat transfer (DFFB) model is applied when void fraction is above 0.9. And a factor is multiplied to the post-CHF drag coefficient to fit the experiment better. Finally, the six FEBA tests are calculated again so as to assess the modifications. Better results are obtained which prove the advantage of the modified models. (author)
Sensitivity analysis of a discrete fracture network model for performance assessment of Aberg
International Nuclear Information System (INIS)
Outters, N.; Shuttle, D.
2000-12-01
This report presents a sensitivity analysis of pathway simulations in a DFN model. The DFN model consists of two sets of stochastic fractures at different scales and the canister locations of a hypothetical repository layout. The hydrogeological base case model is defined by constant head boundary conditions on the edges of a 2000 x 2000 x 1000 m 3 block. The pathway analysis carried out by the program PAWorks provides pathway parameters (pathway length, pathway width, transport aperture, reactive surface area, pathway transmissivity), canister statistics (average number of pathways per canister, percentage of canister locations with pathways) and visualisation of pathways. The project provided the following results from the alternative cases: Case 1: Model with a 100 m thick fracture network at the repository scale instead of 50 m in the base case. The model is little sensitive to the increase of the thickness of the local fracture network. Case 2: Model including fracture networks where the mean size and size standard deviation is twice the ones used in the base case. The travel times to the biosphere is slightly shortened by increasing the fracture diameter. Case 3: Two models with alternative hydraulic boundary conditions: two different flux boundary conditions are tested instead of head boundary conditions in the base case. The advective travel time is shortened by changing the boundary conditions in both alternative cases; in some cases it is reduced to less than a year. Case 4: Study of alternative pathway search algorithms: the pathway search is here based on minimum travel time. The pathway search algorithm of PAWorks based on minimum travel time gives much more optimistic results than the base case where the maximum flow rate was used. The mean travel time is about 5000 years. Due to editorial reasons only a subset of all this information is treated in this report
Ely, D. Matthew
2006-01-01
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.
Sensitivity of a numerical wave model on wind re-analysis datasets
Lavidas, George; Venugopal, Vengatesan; Friedrich, Daniel
2017-03-01
Wind is the dominant process for wave generation. Detailed evaluation of metocean conditions strengthens our understanding of issues concerning potential offshore applications. However, the scarcity of buoys and high cost of monitoring systems pose a barrier to properly defining offshore conditions. Through use of numerical wave models, metocean conditions can be hindcasted and forecasted providing reliable characterisations. This study reports the sensitivity of wind inputs on a numerical wave model for the Scottish region. Two re-analysis wind datasets with different spatio-temporal characteristics are used, the ERA-Interim Re-Analysis and the CFSR-NCEP Re-Analysis dataset. Different wind products alter results, affecting the accuracy obtained. The scope of this study is to assess different available wind databases and provide information concerning the most appropriate wind dataset for the specific region, based on temporal, spatial and geographic terms for wave modelling and offshore applications. Both wind input datasets delivered results from the numerical wave model with good correlation. Wave results by the 1-h dataset have higher peaks and lower biases, in expense of a high scatter index. On the other hand, the 6-h dataset has lower scatter but higher biases. The study shows how wind dataset affects the numerical wave modelling performance, and that depending on location and study needs, different wind inputs should be considered.
A sensitivity analysis of a radiological assessment model for Arctic waters
DEFF Research Database (Denmark)
Nielsen, S.P.
1998-01-01
A model based on compartment analysis has been developed to simulate the dispersion of radionuclides in Arctic waters for an assessment of doses to man. The model predicts concentrations of radionuclides in the marine environment and doses to man from a range of exposure pathways. A parameter sen...... scavenging, water-sediment interaction, biological uptake, ice transport and fish migration. Two independent evaluations of the release of radioactivity from dumped nuclear waste in the Kara Sea have been used as source terms for the dose calculations.......A model based on compartment analysis has been developed to simulate the dispersion of radionuclides in Arctic waters for an assessment of doses to man. The model predicts concentrations of radionuclides in the marine environment and doses to man from a range of exposure pathways. A parameter...... sensitivity analysis has identified components of the model that are potentially important contributors to the predictive accuracy of doses to individuals of critical groups as well as to the world population. The components investigated include features associated with water transport and mixing, particle...
Directory of Open Access Journals (Sweden)
Hui Wan
2015-06-01
Full Text Available Sensitivity analysis is a fundamental approach to identify the most significant and sensitive parameters, helping us to understand complex hydrological models, particularly for time-consuming distributed flood forecasting models based on complicated theory with numerous parameters. Based on Sobol’ method, this study compared the sensitivity and interactions of distributed flood forecasting model parameters with and without accounting for correlation. Four objective functions: (1 Nash–Sutcliffe efficiency (ENS; (2 water balance coefficient (WB; (3 peak discharge efficiency (EP; and (4 time to peak efficiency (ETP were implemented to the Liuxihe model with hourly rainfall-runoff data collected in the Nanhua Creek catchment, Pearl River, China. Contrastive results for the sensitivity and interaction analysis were also illustrated among small, medium, and large flood magnitudes. Results demonstrated that the choice of objective functions had no effect on the sensitivity classification, while it had great influence on the sensitivity ranking for both uncorrelated and correlated cases. The Liuxihe model behaved and responded uniquely to various flood conditions. The results also indicated that the pairwise parameters interactions revealed a non-ignorable contribution to the model output variance. Parameters with high first or total order sensitivity indices presented a corresponding high second order sensitivity indices and correlation coefficients with other parameters. Without considering parameter correlations, the variance contributions of highly sensitive parameters might be underestimated and those of normally sensitive parameters might be overestimated. This research laid a basic foundation to improve the understanding of complex model behavior.
Numerical model analysis of the shaded dye-sensitized solar cell module
International Nuclear Information System (INIS)
Chen Shuanghong; Weng Jian; Huang Yang; Zhang Changneng; Hu Linhua; Kong Fantai; Wang Lijun; Dai Songyuan
2010-01-01
On the basis of a numerical model analysis, the photovoltaic performance of a partially shadowed dye-sensitized solar cell (DSC) module is investigated. In this model, the electron continuity equation and the Butler-Vollmer equation are applied considering electron transfer via the interface of transparent conducting oxide/electrolyte in the shaded DSC. The simulation results based on this model are consistent with experimental results. The influence of shading ratio, connection types and the intensity of irradiance has been analysed according to experiments and numerical simulation. It is found that the performance of the DSC obviously declines with an increase in the shaded area due to electron recombination at the TCO/electrolyte interface and that the output power loss of the shadowed DSC modules in series is much larger than that in parallel due to the 'breakdown' occurring at the TCO/electrolyte interface. The impact of shadow on the DSC performance is stronger with increase in irradiation intensity.
Sensitivity analysis of the near-road dispersion model RLINE - An evaluation at Detroit, Michigan
Milando, Chad W.; Batterman, Stuart A.
2018-05-01
The development of accurate and appropriate exposure metrics for health effect studies of traffic-related air pollutants (TRAPs) remains challenging and important given that traffic has become the dominant urban exposure source and that exposure estimates can affect estimates of associated health risk. Exposure estimates obtained using dispersion models can overcome many of the limitations of monitoring data, and such estimates have been used in several recent health studies. This study examines the sensitivity of exposure estimates produced by dispersion models to meteorological, emission and traffic allocation inputs, focusing on applications to health studies examining near-road exposures to TRAP. Daily average concentrations of CO and NOx predicted using the Research Line source model (RLINE) and a spatially and temporally resolved mobile source emissions inventory are compared to ambient measurements at near-road monitoring sites in Detroit, MI, and are used to assess the potential for exposure measurement error in cohort and population-based studies. Sensitivity of exposure estimates is assessed by comparing nominal and alternative model inputs using statistical performance evaluation metrics and three sets of receptors. The analysis shows considerable sensitivity to meteorological inputs; generally the best performance was obtained using data specific to each monitoring site. An updated emission factor database provided some improvement, particularly at near-road sites, while the use of site-specific diurnal traffic allocations did not improve performance compared to simpler default profiles. Overall, this study highlights the need for appropriate inputs, especially meteorological inputs, to dispersion models aimed at estimating near-road concentrations of TRAPs. It also highlights the potential for systematic biases that might affect analyses that use concentration predictions as exposure measures in health studies.
Context Sensitive Modeling of Cancer Drug Sensitivity.
Directory of Open Access Journals (Sweden)
Bo-Juen Chen
Full Text Available Recent screening of drug sensitivity in large panels of cancer cell lines provides a valuable resource towards developing algorithms that predict drug response. Since more samples provide increased statistical power, most approaches to prediction of drug sensitivity pool multiple cancer types together without distinction. However, pan-cancer results can be misleading due to the confounding effects of tissues or cancer subtypes. On the other hand, independent analysis for each cancer-type is hampered by small sample size. To balance this trade-off, we present CHER (Contextual Heterogeneity Enabled Regression, an algorithm that builds predictive models for drug sensitivity by selecting predictive genomic features and deciding which ones should-and should not-be shared across different cancers, tissues and drugs. CHER provides significantly more accurate models of drug sensitivity than comparable elastic-net-based models. Moreover, CHER provides better insight into the underlying biological processes by finding a sparse set of shared and type-specific genomic features.
Lumen, Annie; McNally, Kevin; George, Nysia; Fisher, Jeffrey W; Loizou, George D
2015-01-01
A deterministic biologically based dose-response model for the thyroidal system in a near-term pregnant woman and the fetus was recently developed to evaluate quantitatively thyroid hormone perturbations. The current work focuses on conducting a quantitative global sensitivity analysis on this complex model to identify and characterize the sources and contributions of uncertainties in the predicted model output. The workflow and methodologies suitable for computationally expensive models, such as the Morris screening method and Gaussian Emulation processes, were used for the implementation of the global sensitivity analysis. Sensitivity indices, such as main, total and interaction effects, were computed for a screened set of the total thyroidal system descriptive model input parameters. Furthermore, a narrower sub-set of the most influential parameters affecting the model output of maternal thyroid hormone levels were identified in addition to the characterization of their overall and pair-wise parameter interaction quotients. The characteristic trends of influence in model output for each of these individual model input parameters over their plausible ranges were elucidated using Gaussian Emulation processes. Through global sensitivity analysis we have gained a better understanding of the model behavior and performance beyond the domains of observation by the simultaneous variation in model inputs over their range of plausible uncertainties. The sensitivity analysis helped identify parameters that determine the driving mechanisms of the maternal and fetal iodide kinetics, thyroid function and their interactions, and contributed to an improved understanding of the system modeled. We have thus demonstrated the use and application of global sensitivity analysis for a biologically based dose-response model for sensitive life-stages such as pregnancy that provides richer information on the model and the thyroidal system modeled compared to local sensitivity analysis.
Directory of Open Access Journals (Sweden)
Annie eLumen
2015-05-01
Full Text Available A deterministic biologically based dose-response model for the thyroidal system in a near-term pregnant woman and the fetus was recently developed to evaluate quantitatively thyroid hormone perturbations. The current work focuses on conducting a quantitative global sensitivity analysis on this complex model to identify and characterize the sources and contributions of uncertainties in the predicted model output. The workflow and methodologies suitable for computationally expensive models, such as the Morris screening method and Gaussian Emulation processes, were used for the implementation of the global sensitivity analysis. Sensitivity indices, such as main, total and interaction effects, were computed for a screened set of the total thyroidal system descriptive model input parameters. Furthermore, a narrower sub-set of the most influential parameters affecting the model output of maternal thyroid hormone levels were identified in addition to the characterization of their overall and pair-wise parameter interaction quotients. The characteristic trends of influence in model output for each of these individual model input parameters over their plausible ranges were elucidated using Gaussian Emulation processes. Through global sensitivity analysis we have gained a better understanding of the model behavior and performance beyond the domains of observation by the simultaneous variation in model inputs over their range of plausible uncertainties. The sensitivity analysis helped identify parameters that determine the driving mechanisms of the maternal and fetal iodide kinetics, thyroid function and their interactions, and contributed to an improved understanding of the system modeled. We have thus demonstrated the use and application of global sensitivity analysis for a biologically based dose-response model for sensitive life-stages such as pregnancy that provides richer information on the model and the thyroidal system modeled compared to local
Energy Technology Data Exchange (ETDEWEB)
Farahani, Aref Zarnooshe [Islamic Azad Univ., Tehran (Iran, Islamic Republic of). Dept. of Nuclear Engineering, Science and Research Branch; Yousefpour, Faramarz [Nuclear Science and Technology Research Institute, Tehran (Iran, Islamic Republic of); Hoseyni, Seyed Mohsen [Islamic Azad Univ., Tehran (Iran, Islamic Republic of). Dept. of Basic Sciences; Islamic Azad Univ., Tehran (Iran, Islamic Republic of). Young Researchers and Elite Club
2017-07-15
Development of a steady-state model is the first step in nuclear safety analysis. The developed model should be qualitatively analyzed first, then a sensitivity analysis is required on the number of nodes for models of different systems to ensure the reliability of the obtained results. This contribution aims to show through sensitivity analysis, the independence of modeling results to the number of nodes in a qualified MELCOR model for a Westinghouse type pressurized power plant. For this purpose, and to minimize user error, the nuclear analysis software, SNAP, is employed. Different sensitivity cases were developed by modification of the existing model and refinement of the nodes for the simulated systems including steam generators, reactor coolant system and also reactor core and its connecting flow paths. By comparing the obtained results to those of the original model no significant difference is observed which is indicative of the model independence to the finer nodes.
Isoprene emissions modelling for West Africa: MEGAN model evaluation and sensitivity analysis
Directory of Open Access Journals (Sweden)
J. Ferreira
2010-09-01
Full Text Available Isoprene emissions are the largest source of reactive carbon to the atmosphere, with the tropics being a major source region. These natural emissions are expected to change with changing climate and human impact on land use. As part of the African Monsoon Multidisciplinary Analyses (AMMA project the Model of Emissions of Gases and Aerosols from Nature (MEGAN has been used to estimate the spatial and temporal distribution of isoprene emissions over the West African region. During the AMMA field campaign, carried out in July and August 2006, isoprene mixing ratios were measured on board the FAAM BAe-146 aircraft. These data have been used to make a qualitative evaluation of the model performance.
MEGAN was firstly applied to a large area covering much of West Africa from the Gulf of Guinea in the south to the desert in the north and was able to capture the large scale spatial distribution of isoprene emissions as inferred from the observed isoprene mixing ratios. In particular the model captures the transition from the forested area in the south to the bare soils in the north, but some discrepancies have been identified over the bare soil, mainly due to the emission factors used. Sensitivity analyses were performed to assess the model response to changes in driving parameters, namely Leaf Area Index (LAI, Emission Factors (EF, temperature and solar radiation.
A high resolution simulation was made of a limited area south of Niamey, Niger, where the higher concentrations of isoprene were observed. This is used to evaluate the model's ability to simulate smaller scale spatial features and to examine the influence of the driving parameters on an hourly basis through a case study of a flight on 17 August 2006.
This study highlights the complex interactions between land surface processes and the meteorological dynamics and chemical composition of the PBL. This has implications for quantifying the impact of biogenic emissions
Flores-Alsina, Xavier; Rodriguez-Roda, Ignasi; Sin, Gürkan; Gernaey, Krist V
2009-01-01
The objective of this paper is to perform an uncertainty and sensitivity analysis of the predictions of the Benchmark Simulation Model (BSM) No. 1, when comparing four activated sludge control strategies. The Monte Carlo simulation technique is used to evaluate the uncertainty in the BSM1 predictions, considering the ASM1 bio-kinetic parameters and influent fractions as input uncertainties while the Effluent Quality Index (EQI) and the Operating Cost Index (OCI) are focused on as model outputs. The resulting Monte Carlo simulations are presented using descriptive statistics indicating the degree of uncertainty in the predicted EQI and OCI. Next, the Standard Regression Coefficients (SRC) method is used for sensitivity analysis to identify which input parameters influence the uncertainty in the EQI predictions the most. The results show that control strategies including an ammonium (S(NH)) controller reduce uncertainty in both overall pollution removal and effluent total Kjeldahl nitrogen. Also, control strategies with an external carbon source reduce the effluent nitrate (S(NO)) uncertainty increasing both their economical cost and variability as a trade-off. Finally, the maximum specific autotrophic growth rate (micro(A)) causes most of the variance in the effluent for all the evaluated control strategies. The influence of denitrification related parameters, e.g. eta(g) (anoxic growth rate correction factor) and eta(h) (anoxic hydrolysis rate correction factor), becomes less important when a S(NO) controller manipulating an external carbon source addition is implemented.
Sensitivity Analysis of DRASTIC Model in Vulnerability Assessment of Shahrood Alluvial Aquifer
Directory of Open Access Journals (Sweden)
Shadi Abolhasan Almasi
2017-07-01
Full Text Available Groundwater vulnerability assessment is typically accomplished as a management tool to protect groundwater resources. In this research, the DRASTIC model which is an empirical one used for evaluating the potential of an aquifer for pollution was employed to evaluate the vulnerability of Shahrood alluvial aquifer. Moreover, the sensitivity of the model paramneters was assessed to identify the ones with greatest effect on vulnerability. The model layers including depth to groundwater table level, recharge, aquifer media, topography, impact of unsaturated zone, and hydraulic conductivity were prepared and classified in the ArcGIS software based on analyses of both the available data and the layer of surface soil texture using Aster satellite images. Once the vulnerability index was calculated, the sensitivity map of Shahroud aquifer vulnerability was analyzed using the two parameter removal and single parameter sensitivity methods. These were further verified by textural analysis of soil samples from different parts of the region. The layers with appropriate weights were overlaid and the DRASTIC index of the aquifer was estimated at 28 to 148. The highest vulnerability was detected in the northern margins and southwestern parts of the aquifer while other parts were characterized by medium to low vulnerability. The low nitrogen concentration observed in the farm areas and its rise to 45 mg/l in the northern stretches of the aquifer bear witness to the accuracy of the zoning rendered by the DRASTIC model. Based on the vulnerability map of Sharoud aquifer, it was found that 1.6% of the aquifer’s area has a very high vulnerability or potential for pollution followed by 10%, 28.8%, and 18.9% of the area were identified as having high, medium and low potentials for pollution, respecytively. The remaining (i.e., 40.5% was found to have no risk of pollution.
International Nuclear Information System (INIS)
Rakopoulos, C.D.; Rakopoulos, D.C.; Giakoumis, E.G.; Kyritsis, D.C.
2004-01-01
The present two zone model of a direct injection (DI) Diesel engine divides the cylinder contents into a non-burning zone of air and another homogeneous zone in which fuel is continuously supplied from the injector and burned with entrained air from the air zone. The growth of the fuel spray zone, which comprises a number of fuel-air conical jets equal to the injector nozzle holes, is carefully modelled by incorporating jet mixing, thus determining the amount of oxygen available for combustion. The mass, energy and state equations are applied in each of the two zones to yield local temperatures and cylinder pressure histories. The concentration of the various constituents in the exhaust gases are calculated by adopting a chemical equilibrium scheme for the C-H-O system of the 11 species considered, together with chemical rate equations for the calculation of nitric oxide (NO). A model for evaluation of the soot formation and oxidation rates is included. The theoretical results from the relevant computer program are compared very favourably with the measurements from an experimental investigation conducted on a fully automated test bed, standard 'Hydra', DI Diesel engine installed at the authors' laboratory. In-cylinder pressure and temperature histories, nitric oxide concentration and soot density are among the interesting quantities tested for various loads and injection timings. As revealed, the model is sensitive to the selection of the constants of the fuel preparation and reaction sub-models, so that a relevant sensitivity analysis is undertaken. This leads to a better understanding of the physical mechanisms governed by these constants and also paves the way for construction of a reliable and relatively simple multi-zone model, which incorporates in each zone (packet) the philosophy of the present two zone model
Energy Technology Data Exchange (ETDEWEB)
Rakopoulos, C.D.; Rakopoulos, D.C.; Giakoumis, E.G. [National Technical University of Athens (Greece). Mechanical Engineering Dept.; Kyritsis, D.C. [University of Illinois at Urbana-Champaign, Urbana, IL (United States). Dept. of Mechanical and Industrial Engineering
2004-06-01
The present two zone model of a direct injection (DI) diesel engine divides the cylinder contents into a non-burning zone of air and another homogeneous zone in which fuel is continuously supplied from the injector and burned with entrained air from the air zone. The growth of the fuel spray zone, which comprises a number of fuel-air conical jets equal to the injector nozzle holes, is carefully modelled by incorporating jet mixing, thus determining the amount of oxygen available for combustion. The mass, energy and state equations are applied in each of the two zones to yield local temperatures and cylinder pressure histories. The concentration of the various constituents in the exhaust gases are calculated by adopting a chemical equilibrium scheme for the C-H-O system of the 11 species considered, together with chemical rate equations for the calculation of nitric oxide (NO). A model for evaluation of the soot formation and oxidation rates is included. The theoretical results from the relevant computer program are compared very favourably with the measurements from an experimental investigation conducted on a fully automated test bed, standard ''Hydra'', DI diesel engine installed at the authors' laboratory. In-cylinder pressure and temperature histories, nitric oxide concentration and soot density are among the interesting quantities tested for various loads and injection timings. As revealed, the model is sensitive to the selection of the constants of the fuel preparation and reaction sub-models, so that a relevant sensitivity analysis is undertaken. This leads to a better understanding of the physical mechanisms governed by these constants and also paves the way for construction of a reliable and relatively simple multi-zone model, which incorporates in each zone (packet) the philosophy of the present two zone model. (author)
Flow analysis with WaSiM-ETH – model parameter sensitivity at different scales
Directory of Open Access Journals (Sweden)
J. Cullmann
2006-01-01
Full Text Available WaSiM-ETH (Gurtz et al., 2001, a widely used water balance simulation model, is tested for its suitability to serve for flow analysis in the context of rainfall runoff modelling and flood forecasting. In this paper, special focus is on the resolution of the process domain in space as well as in time. We try to couple model runs with different calculation time steps in order to reduce the effort arising from calculating the whole flow hydrograph at the hourly time step. We aim at modelling on the daily time step for water balance purposes, switching to the hourly time step whenever high-resolution information is necessary (flood forecasting. WaSiM-ETH is used at different grid resolutions, thus we try to become clear about being able to transfer the model in spatial resolution. We further use two different approaches for the overland flow time calculation within the sub-basins of the test watershed to gain insights about the process dynamics portrayed by the model. Our findings indicate that the model is very sensitive to time and space resolution and cannot be transferred across scales without recalibration.
Munoz-Carpena, R.; Muller, S. J.; Chu, M.; Kiker, G. A.; Perz, S. G.
2014-12-01
Model Model complexity resulting from the need to integrate environmental system components cannot be understated. In particular, additional emphasis is urgently needed on rational approaches to guide decision making through uncertainties surrounding the integrated system across decision-relevant scales. However, in spite of the difficulties that the consideration of modeling uncertainty represent for the decision process, it should not be avoided or the value and science behind the models will be undermined. These two issues; i.e., the need for coupled models that can answer the pertinent questions and the need for models that do so with sufficient certainty, are the key indicators of a model's relevance. Model relevance is inextricably linked with model complexity. Although model complexity has advanced greatly in recent years there has been little work to rigorously characterize the threshold of relevance in integrated and complex models. Formally assessing the relevance of the model in the face of increasing complexity would be valuable because there is growing unease among developers and users of complex models about the cumulative effects of various sources of uncertainty on model outputs. In particular, this issue has prompted doubt over whether the considerable effort going into further elaborating complex models will in fact yield the expected payback. New approaches have been proposed recently to evaluate the uncertainty-complexity-relevance modeling trilemma (Muller, Muñoz-Carpena and Kiker, 2011) by incorporating state-of-the-art global sensitivity and uncertainty analysis (GSA/UA) in every step of the model development so as to quantify not only the uncertainty introduced by the addition of new environmental components, but the effect that these new components have over existing components (interactions, non-linear responses). Outputs from the analysis can also be used to quantify system resilience (stability, alternative states, thresholds or tipping
International Nuclear Information System (INIS)
Aneljung, Maria; Sassner, Mona; Gustafsson, Lars-Goeran
2007-11-01
between measured and calculated surface water discharges, but the model generally underestimates the total runoff from the area. The model also overestimates the groundwater levels, and the modelled groundwater level amplitudes are too small in many boreholes. A number of likely or potential reasons for these deviations can be identified: The surface stream network description in the model is incomplete. This implies that too little overland water is drained from the area by the streams, which creates ponded areas in the model that do not exist in reality. These areas are characterized by large evaporation and infiltration, contributing to groundwater recharge and reducing transpiration from the groundwater table, in turn creating high and relatively stable groundwater levels compared to those measured at the site. In order to improve the agreement between measured and modelled surface water discharges, the evapotranspiration was reduced in the model; in effect, this implied a reduction of the potential evapotranspiration. This probably caused a larger groundwater recharge and less transpiration during summer, thereby reducing the variations in the modelled groundwater levels. If the MIKE 11 stream network is updated, the potential evapotranspiration could be increased again, such that the modelling of groundwater dynamics is improved. The bottom boundary condition and the hydraulic conductivity of the bedrock may have a large effect on model-calculated near-surface/surface water flows in Laxemar. A sensitivity analysis shows that lowering the hydraulic head at the bottom boundary (located at 150 metres below sea level) lowers the groundwater levels in the Quaternary deposits, but also implies smaller surface water discharges. Lowering the hydraulic conductivity of the bedrock would increase groundwater flows to Quaternary deposits in groundwater discharge areas, which raises groundwater levels and reduces fluctuation amplitudes. An alternative model approach, using a
Directory of Open Access Journals (Sweden)
Rehan Balqis M.
2016-01-01
Full Text Available Current practice in flood frequency analysis assumes that the stochastic properties of extreme floods follow that of stationary conditions. As human intervention and anthropogenic climate change influences in hydrometeorological variables are becoming evident in some places, there have been suggestions that nonstationary statistics would be better to represent the stochastic properties of the extreme floods. The probabilistic estimation of non-stationary models, however, is surrounded with uncertainty related to scarcity of observations and modelling complexities hence the difficulty to project the future condition. In the face of uncertain future and the subjectivity of model choices, this study attempts to demonstrate the practical implications of applying a nonstationary model and compares it with a stationary model in flood risk assessment. A fully integrated framework to simulate decision makers’ behaviour in flood frequency analysis is thereby developed. The framework is applied to hypothetical flood risk management decisions and the outcomes are compared with those of known underlying future conditions. Uncertainty of the economic performance of the risk-based decisions is assessed through Monte Carlo simulations. Sensitivity of the results is also tested by varying the possible magnitude of future changes. The application provides quantitative and qualitative comparative results that satisfy a preliminary analysis of whether the nonstationary model complexity should be applied to improve the economic performance of decisions. Results obtained from the case study shows that the relative differences of competing models for all considered possible future changes are small, suggesting that stationary assumptions are preferred to a shift to nonstationary statistics for practical application of flood risk management. Nevertheless, nonstationary assumption should also be considered during a planning stage in addition to stationary assumption
Demonstration uncertainty/sensitivity analysis using the health and economic consequence model CRAC2
International Nuclear Information System (INIS)
Alpert, D.J.; Iman, R.L.; Johnson, J.D.; Helton, J.C.
1984-12-01
The techniques for performing uncertainty/sensitivity analyses compiled as part of the MELCOR program appear to be well suited for use with a health and economic consequence model. Two replicate samples of size 50 gave essentially identical results, indicating that for this case, a Latin hypercube sample of size 50 seems adequate to represent the distribution of results. Though the intent of this study was a demonstration of uncertainty/sensitivity analysis techniques, a number of insights relevant to health and economic consequence modeling can be gleaned: uncertainties in early deaths are significantly greater than uncertainties in latent cancer deaths; though the magnitude of the source term is the largest source of variation in estimated distributions of early deaths, a number of additional parameters are also important; even with the release fractions for a full SST1, one quarter of the CRAC2 runs gave no early deaths; and comparison of the estimates of mean early deaths for a full SST1 release in this study with those of recent point estimates for similar conditions indicates that the recent estimates may be significant overestimations of early deaths. Estimates of latent cancer deaths, however, are roughly comparable. An analysis of the type described here can provide insights in a number of areas. First, the variability in the results gives an indication of the potential uncertainty associated with the calculations. Second, the sensitivity of the results to assumptions about the input variables can be determined. Research efforts can then be concentrated on reducing the uncertainty in the variables which are the largest contributors to uncertainty in results
Energy Technology Data Exchange (ETDEWEB)
Biftu, G.F.; Beersing, A.; Wu, S.; Ade, F. [Golder Associates, Calgary, AB (Canada)
2005-07-01
An outline of a new approach to assessing the sensitivity and uncertainty associated with surface water modelling results using Hydrological Simulation Program-Fortran (HSPF) was presented, as well as the results of a sensitivity and uncertainty analysis. The HSPF model is often used to characterize the hydrological processes in watersheds within the oil sands region. Typical applications of HSPF included calibration of the model parameters using data from gauged watersheds, as well as validation of calibrated models with data sets. Additionally, simulations are often conducted to make flow predictions to support the environmental impact assessment (EIA) process. However, a key aspect of the modelling components of the EIA process is the sensitivity and uncertainty of the modelling results as compared to model parameters. Many of the variations in the HSPF model's outputs are caused by a small number of model parameters and outputs. A sensitivity analysis was performed to identify and focus on key parameters and assumptions that have the most influence on the model's outputs. Analysis entailed varying each parameter in turn, within a range, and examining the resulting relative changes in the model outputs. This analysis consisted of the selection of probability distributions to characterize the uncertainty in the model's key sensitive parameters, as well as the use of Monte Carlo and HSPF simulation to determine the uncertainty in model outputs. tabs, figs.
Sensitivity Analysis of Input Parameters for a Dynamic Food Chain Model DYNACON
International Nuclear Information System (INIS)
Hwang, Won Tae; Lee, Geun Chang; Han, Moon Hee; Cho, Gyu Seong
2000-01-01
The sensitivity analysis of input parameters for a dynamic food chain model DYNACON was conducted as a function of deposition data for the long-lived radionuclides ( 137 Cs, 90 Sr). Also, the influence of input parameters for the short and long-terms contamination of selected foodstuffs (cereals, leafy vegetables, milk) was investigated. The input parameters were sampled using the LHS technique, and their sensitivity indices represented as PRCC. The sensitivity index was strongly dependent on contamination period as well as deposition data. In case of deposition during the growing stages of plants, the input parameters associated with contamination by foliar absorption were relatively important in long-term contamination as well as short-term contamination. They were also important in short-term contamination in case of deposition during the non-growing stages. In long-term contamination, the influence of input parameters associated with foliar absorption decreased, while the influence of input parameters associated with root uptake increased. These phenomena were more remarkable in case of the deposition of non-growing stages than growing stages, and in case of 90 Sr deposition than 137 Cs deposition. In case of deposition during growing stages of pasture, the input parameters associated with the characteristics of cattle such as feed-milk transfer factor and daily intake rate of cattle were relatively important in contamination of milk
Aires, Filipe; Rossow, William B.; Hansen, James E. (Technical Monitor)
2001-01-01
A new approach is presented for the analysis of feedback processes in a nonlinear dynamical system by observing its variations. The new methodology consists of statistical estimates of the sensitivities between all pairs of variables in the system based on a neural network modeling of the dynamical system. The model can then be used to estimate the instantaneous, multivariate and nonlinear sensitivities, which are shown to be essential for the analysis of the feedbacks processes involved in the dynamical system. The method is described and tested on synthetic data from the low-order Lorenz circulation model where the correct sensitivities can be evaluated analytically.
Li, Wei Bo; Greiter, Matthias; Oeh, Uwe; Hoeschen, Christoph
2011-12-01
The reliability of biokinetic models is essential for the assessment of internal doses and a radiation risk analysis for the public and occupational workers exposed to radionuclides. In the present study, a method for assessing the reliability of biokinetic models by means of uncertainty and sensitivity analysis was developed. In the first part of the paper, the parameter uncertainty was analyzed for two biokinetic models of zirconium (Zr); one was reported by the International Commission on Radiological Protection (ICRP), and one was developed at the Helmholtz Zentrum München-German Research Center for Environmental Health (HMGU). In the second part of the paper, the parameter uncertainties and distributions of the Zr biokinetic models evaluated in Part I are used as the model inputs for identifying the most influential parameters in the models. Furthermore, the most influential model parameter on the integral of the radioactivity of Zr over 50 y in source organs after ingestion was identified. The results of the systemic HMGU Zr model showed that over the first 10 d, the parameters of transfer rates between blood and other soft tissues have the largest influence on the content of Zr in the blood and the daily urinary excretion; however, after day 1,000, the transfer rate from bone to blood becomes dominant. For the retention in bone, the transfer rate from blood to bone surfaces has the most influence out to the endpoint of the simulation; the transfer rate from blood to the upper larger intestine contributes a lot in the later days; i.e., after day 300. The alimentary tract absorption factor (fA) influences mostly the integral of radioactivity of Zr in most source organs after ingestion.
Subsurface stormflow modeling with sensitivity analysis using a Latin-hypercube sampling technique
International Nuclear Information System (INIS)
Gwo, J.P.; Toran, L.E.; Morris, M.D.; Wilson, G.V.
1994-09-01
Subsurface stormflow, because of its dynamic and nonlinear features, has been a very challenging process in both field experiments and modeling studies. The disposal of wastes in subsurface stormflow and vadose zones at Oak Ridge National Laboratory, however, demands more effort to characterize these flow zones and to study their dynamic flow processes. Field data and modeling studies for these flow zones are relatively scarce, and the effect of engineering designs on the flow processes is poorly understood. On the basis of a risk assessment framework and a conceptual model for the Oak Ridge Reservation area, numerical models of a proposed waste disposal site were built, and a Latin-hypercube simulation technique was used to study the uncertainty of model parameters. Four scenarios, with three engineering designs, were simulated, and the effectiveness of the engineering designs was evaluated. Sensitivity analysis of model parameters suggested that hydraulic conductivity was the most influential parameter. However, local heterogeneities may alter flow patterns and result in complex recharge and discharge patterns. Hydraulic conductivity, therefore, may not be used as the only reference for subsurface flow monitoring and engineering operations. Neither of the two engineering designs, capping and French drains, was found to be effective in hydrologically isolating downslope waste trenches. However, pressure head contours indicated that combinations of both designs may prove more effective than either one alone
Numeric-modeling sensitivity analysis of the performance of wind turbine arrays
Energy Technology Data Exchange (ETDEWEB)
Lissaman, P.B.S.; Gyatt, G.W.; Zalay, A.D.
1982-06-01
An evaluation of the numerical model created by Lissaman for predicting the performance of wind turbine arrays has been made. Model predictions of the wake parameters have been compared with both full-scale and wind tunnel measurements. Only limited, full-scale data were available, while wind tunnel studies showed difficulties in representing real meteorological conditions. Nevertheless, several modifications and additions have been made to the model using both theoretical and empirical techniques and the new model shows good correlation with experiment. The larger wake growth rate and shorter near wake length predicted by the new model lead to reduced interference effects on downstream turbines and hence greater array efficiencies. The array model has also been re-examined and now incorporates the ability to show the effects of real meteorological conditions such as variations in wind speed and unsteady winds. The resulting computer code has been run to show the sensitivity of array performance to meteorological, machine, and array parameters. Ambient turbulence and windwise spacing are shown to dominate, while hub height ratio is seen to be relatively unimportant. Finally, a detailed analysis of the Goodnoe Hills wind farm in Washington has been made to show how power output can be expected to vary with ambient turbulence, wind speed, and wind direction.
Uncertainty, sensitivity analysis and the role of data based mechanistic modeling in hydrology
Ratto, M.; Young, P. C.; Romanowicz, R.; Pappenberger, F.; Saltelli, A.; Pagano, A.
2007-05-01
In this paper, we discuss a joint approach to calibration and uncertainty estimation for hydrologic systems that combines a top-down, data-based mechanistic (DBM) modelling methodology; and a bottom-up, reductionist modelling methodology. The combined approach is applied to the modelling of the River Hodder catchment in North-West England. The top-down DBM model provides a well identified, statistically sound yet physically meaningful description of the rainfall-flow data, revealing important characteristics of the catchment-scale response, such as the nature of the effective rainfall nonlinearity and the partitioning of the effective rainfall into different flow pathways. These characteristics are defined inductively from the data without prior assumptions about the model structure, other than it is within the generic class of nonlinear differential-delay equations. The bottom-up modelling is developed using the TOPMODEL, whose structure is assumed a priori and is evaluated by global sensitivity analysis (GSA) in order to specify the most sensitive and important parameters. The subsequent exercises in calibration and validation, performed with Generalized Likelihood Uncertainty Estimation (GLUE), are carried out in the light of the GSA and DBM analyses. This allows for the pre-calibration of the the priors used for GLUE, in order to eliminate dynamical features of the TOPMODEL that have little effect on the model output and would be rejected at the structure identification phase of the DBM modelling analysis. In this way, the elements of meaningful subjectivity in the GLUE approach, which allow the modeler to interact in the modelling process by constraining the model to have a specific form prior to calibration, are combined with other more objective, data-based benchmarks for the final uncertainty estimation. GSA plays a major role in building a bridge between the hypothetico-deductive (bottom-up) and inductive (top-down) approaches and helps to improve the
Parameter sensitivity analysis of a lumped-parameter model of a chain of lymphangions in series.
Jamalian, Samira; Bertram, Christopher D; Richardson, William J; Moore, James E
2013-12-01
Any disruption of the lymphatic system due to trauma or injury can lead to edema. There is no effective cure for lymphedema, partly because predictive knowledge of lymphatic system reactions to interventions is lacking. A well-developed model of the system could greatly improve our understanding of its function. Lymphangions, defined as the vessel segment between two valves, are the individual pumping units. Based on our previous lumped-parameter model of a chain of lymphangions, this study aimed to identify the parameters that affect the system output the most using a sensitivity analysis. The system was highly sensitive to minimum valve resistance, such that variations in this parameter caused an order-of-magnitude change in time-average flow rate for certain values of imposed pressure difference. Average flow rate doubled when contraction frequency was increased within its physiological range. Optimum lymphangion length was found to be some 13-14.5 diameters. A peak of time-average flow rate occurred when transmural pressure was such that the pressure-diameter loop for active contractions was centered near maximum passive vessel compliance. Increasing the number of lymphangions in the chain improved the pumping in the presence of larger adverse pressure differences. For a given pressure difference, the optimal number of lymphangions increased with the total vessel length. These results indicate that further experiments to estimate valve resistance more accurately are necessary. The existence of an optimal value of transmural pressure may provide additional guidelines for increasing pumping in areas affected by edema.
Directory of Open Access Journals (Sweden)
Iulian N. BUJOREANU
2011-01-01
Full Text Available Sensitivity analysis represents such a well known and deeply analyzed subject that anyone to enter the field feels like not being able to add anything new. Still, there are so many facets to be taken into consideration.The paper introduces the reader to the various ways sensitivity analysis is implemented and the reasons for which it has to be implemented in most analyses in the decision making processes. Risk analysis is of outmost importance in dealing with resource allocation and is presented at the beginning of the paper as the initial cause to implement sensitivity analysis. Different views and approaches are added during the discussion about sensitivity analysis so that the reader develops an as thoroughly as possible opinion on the use and UTILITY of the sensitivity analysis. Finally, a round-up conclusion brings us to the question of the possibility of generating the future and analyzing it before it unfolds so that, when it happens it brings less uncertainty.
Energy Technology Data Exchange (ETDEWEB)
Aneljung, Maria; Sassner, Mona; Gustafsson, Lars-Goeran (DHI Sverige AB, Lilla Bommen 1, SE-411 04 Goeteborg (Sweden))
2007-11-15
between measured and calculated surface water discharges, but the model generally underestimates the total runoff from the area. The model also overestimates the groundwater levels, and the modelled groundwater level amplitudes are too small in many boreholes. A number of likely or potential reasons for these deviations can be identified: The surface stream network description in the model is incomplete. This implies that too little overland water is drained from the area by the streams, which creates ponded areas in the model that do not exist in reality. These areas are characterized by large evaporation and infiltration, contributing to groundwater recharge and reducing transpiration from the groundwater table, in turn creating high and relatively stable groundwater levels compared to those measured at the site. In order to improve the agreement between measured and modelled surface water discharges, the evapotranspiration was reduced in the model; in effect, this implied a reduction of the potential evapotranspiration. This probably caused a larger groundwater recharge and less transpiration during summer, thereby reducing the variations in the modelled groundwater levels. If the MIKE 11 stream network is updated, the potential evapotranspiration could be increased again, such that the modelling of groundwater dynamics is improved. The bottom boundary condition and the hydraulic conductivity of the bedrock may have a large effect on model-calculated near-surface/surface water flows in Laxemar. A sensitivity analysis shows that lowering the hydraulic head at the bottom boundary (located at 150 metres below sea level) lowers the groundwater levels in the Quaternary deposits, but also implies smaller surface water discharges. Lowering the hydraulic conductivity of the bedrock would increase groundwater flows to Quaternary deposits in groundwater discharge areas, which raises groundwater levels and reduces fluctuation amplitudes. An alternative model approach, using a
Soil dynamics and accelerated erosion: a sensitivity analysis of the LPJ Dynamic vegetation model
Bouchoms, Samuel; Van Oost, Kristof; Vanacker, Veerle; Kaplan, Jed O.; Vanwalleghem, Tom
2013-04-01
It is widely accepted that humans have become a major geomorphic force by disturbing natural vegetation patterns. Land conversion for agriculture purposes removes the protection of soils by the natural vegetation and leads to increased soil erosion by one to two orders of magnitude, breaking the balance that exists between the loss of soils and its production. Accelerated erosion and deposition have a strong influence on evolution and heterogeneity of basic soil characteristics (soil thickness, hydrology, horizon development,…) as well as on organic matter storage and cycling. Yet, since they are operating at a long time scale, those processes are not represented in state-of-art Dynamic Global Vegetation Models, which is a clear lack when exploring vegetation dynamics over past centuries. The main objectives of this paper are (i) to test the sensitivity of a Dynamic Global Vegetation Model, in terms of NPP and organic matter turnover, variations in state variables in response to accelerated erosion and (ii) to assess the performance of the model under the impact of erosion for a case-study in Central Spain. We evaluated the Lund-Postdam-Jena Dynamic Vegetation Model (LPJ DVGM) (Sitch et al, 2003) which simulates vegetation growth and carbon pools at the surface and in the soil based on climatic, pedologic and topographic variables. We assessed its reactions to changes in key soil properties that are affected by erosion such as texture and soil depth. We present the results of where we manipulated soil texture and bulk density while keeping the environmental drivers of climate, slope and altitude constant. For parameters exhibiting a strong control on NPP or SOM, a factorial analysis was conducted to test for interaction effects. The simulations show an important dependence on the clay content, especially for the slow cycling carbon pools and the biomass production, though the underground litter seems to be mostly influenced by the silt content. The fast cycling C
Lewis, Bryan; Cimbala, John; Wouden, Alex
2011-11-01
Turbulence models are generally developed to study common academic geometries, such as flat plates and channels. Creating quality computational grids for such geometries is trivial, and allows stringent requirements to be met for boundary layer grid refinement. However, engineering applications, such as flow through hydroturbines, require the analysis of complex, highly curved geometries. To produce body-fitted grids for such geometries, the mesh quality requirements must be relaxed. Relaxing these requirements, along with the complexity of rotating flows, forces turbulence models to be employed beyond their developed scope. This study explores the solution sensitivity to boundary layer grid quality for various turbulence models and boundary conditions currently implemented in OpenFOAM. The following models are resented: k-omega, k-omega SST, k-epsilon, realizable k-epsilon, and RNG k-epsilon. Standard wall functions, adaptive wall functions, and sub-grid integration are compared using various grid refinements. The chosen geometry is the GAMM Francis Turbine because experimental data and comparison computational results are available for this turbine. This research was supported by a grant from the DoE and a National Defense Science and Engineering Graduate Fellowship.
Sun, Huaiwei; Zhu, Yan; Yang, Jinzhong; Wang, Xiugui
2015-11-01
As the amount of water resources that can be utilized for agricultural production is limited, the reuse of treated wastewater (TWW) for irrigation is a practical solution to alleviate the water crisis in China. The process-based models, which estimate nitrogen dynamics under irrigation, are widely used to investigate the best irrigation and fertilization management practices in developed and developing countries. However, for modeling such a complex system for wastewater reuse, it is critical to conduct a sensitivity analysis to determine numerous input parameters and their interactions that contribute most to the variance of the model output for the development of process-based model. In this study, application of a comprehensive global sensitivity analysis for nitrogen dynamics was reported. The objective was to compare different global sensitivity analysis (GSA) on the key parameters for different model predictions of nitrogen and crop growth modules. The analysis was performed as two steps. Firstly, Morris screening method, which is one of the most commonly used screening method, was carried out to select the top affected parameters; then, a variance-based global sensitivity analysis method (extended Fourier amplitude sensitivity test, EFAST) was used to investigate more thoroughly the effects of selected parameters on model predictions. The results of GSA showed that strong parameter interactions exist in crop nitrogen uptake, nitrogen denitrification, crop yield, and evapotranspiration modules. Among all parameters, one of the soil physical-related parameters named as the van Genuchten air entry parameter showed the largest sensitivity effects on major model predictions. These results verified that more effort should be focused on quantifying soil parameters for more accurate model predictions in nitrogen- and crop-related predictions, and stress the need to better calibrate the model in a global sense. This study demonstrates the advantages of the GSA on a
DEFF Research Database (Denmark)
Feyissa, Aberham Hailu; Gernaey, Krist; Adler-Nissen, Jens
2012-01-01
to uncertainty in the model predictions. The aim of the current paper is to address this uncertainty challenge in the modelling of food production processes using a combination of uncertainty and sensitivity analysis, where the uncertainty analysis and global sensitivity analysis were applied to a heat and mass......Similar to other processes, the modelling of heat and mass transfer during food processing involves uncertainty in the values of input parameters (heat and mass transfer coefficients, evaporation rate parameters, thermo-physical properties, initial and boundary conditions) which leads...
Sensitivity Analysis Without Assumptions.
Ding, Peng; VanderWeele, Tyler J
2016-05-01
Unmeasured confounding may undermine the validity of causal inference with observational studies. Sensitivity analysis provides an attractive way to partially circumvent this issue by assessing the potential influence of unmeasured confounding on causal conclusions. However, previous sensitivity analysis approaches often make strong and untestable assumptions such as having an unmeasured confounder that is binary, or having no interaction between the effects of the exposure and the confounder on the outcome, or having only one unmeasured confounder. Without imposing any assumptions on the unmeasured confounder or confounders, we derive a bounding factor and a sharp inequality such that the sensitivity analysis parameters must satisfy the inequality if an unmeasured confounder is to explain away the observed effect estimate or reduce it to a particular level. Our approach is easy to implement and involves only two sensitivity parameters. Surprisingly, our bounding factor, which makes no simplifying assumptions, is no more conservative than a number of previous sensitivity analysis techniques that do make assumptions. Our new bounding factor implies not only the traditional Cornfield conditions that both the relative risk of the exposure on the confounder and that of the confounder on the outcome must satisfy but also a high threshold that the maximum of these relative risks must satisfy. Furthermore, this new bounding factor can be viewed as a measure of the strength of confounding between the exposure and the outcome induced by a confounder.
International Nuclear Information System (INIS)
Hwang, W.T.; Lee, G.C.; Suh, K.S.; Kim, E.H.; Choi, Y.G.; Han, M.H.; Cho, G.S.
2000-01-01
The sensitivity analysis of input parameters for a dynamic food chain model DYNACON was performed as a function of deposition time for the long-lived radionuclides ( 137 Cs, 90 Sr) and the selected foodstuffs (cereals, milk). The influence of input parameters for short and long-term contaminations of the foodstuffs after a deposition was also investigated. The input parameters were sampled using a Latin hypercube sampling (LHS) technique, and their sensitivity indices were quantified as partial rank correlation coefficient (PRCC). PRCCs were strongly dependent on the contamination period of foodstuffs as well as the deposition time of radionuclides. In case of deposition during growing stage of agricultural plants, the input parameters associated with contamination by foliar absorption were relatively influential in long-term contamination as well as short-term contamination. They were also influential in short-term contamination in case of deposition during non-growing stage. As the contamination period is longer, the influence of parameters associated with contamination by root uptake was increased. This phenomenon was more remarkable in case of the deposition during non-growing stage than growing stage, and in case of 90 Sr deposition than 137 Cs deposition. In case of deposition during growing stage of pasture, the characteristic parameters of cattle such as feed-milk transfer factor and daily intake rate were relatively influential in the contamination of milk. (author)
Directory of Open Access Journals (Sweden)
K. E. Christian
2018-02-01
Full Text Available Making sense of modeled atmospheric composition requires not only comparison to in situ measurements but also knowing and quantifying the sensitivity of the model to its input factors. Using a global sensitivity method involving the simultaneous perturbation of many chemical transport model input factors, we find the model uncertainty for ozone (O3, hydroxyl radical (OH, and hydroperoxyl radical (HO2 mixing ratios, and apportion this uncertainty to specific model inputs for the DC-8 flight tracks corresponding to the NASA Intercontinental Chemical Transport Experiment (INTEX campaigns of 2004 and 2006. In general, when uncertainties in modeled and measured quantities are accounted for, we find agreement between modeled and measured oxidant mixing ratios with the exception of ozone during the Houston flights of the INTEX-B campaign and HO2 for the flights over the northernmost Pacific Ocean during INTEX-B. For ozone and OH, modeled mixing ratios were most sensitive to a bevy of emissions, notably lightning NOx, various surface NOx sources, and isoprene. HO2 mixing ratios were most sensitive to CO and isoprene emissions as well as the aerosol uptake of HO2. With ozone and OH being generally overpredicted by the model, we find better agreement between modeled and measured vertical profiles when reducing NOx emissions from surface as well as lightning sources.
International Nuclear Information System (INIS)
Helton, J.C.; Johnson, J.D.; McKay, M.D.; Shiver, A.W.; Sprung, J.L.
1995-01-01
Uncertainty and sensitivity analysis techniques based on Latin hypercube sampling, partial correlation analysis and stepwise regression analysis are used in an investigation with the MACCS model of the early health effects associated with a severe accident at a nuclear power station. The primary purpose of this study is to provide guidance on the variables to be considered in future review work to reduce the uncertainty in the important variables used in the calculation of reactor accident consequences. The effects of 34 imprecisely known input variables on the following reactor accident consequences are studied: number of early fatalities, number of cases of prodromal vomiting, population dose within 10 mi of the reactor, population dose within 1000 mi of the reactor, individual early fatality probability within 1 mi of the reactor, and maximum early fatality distance. When the predicted variables are considered collectively, the following input variables were found to be the dominant contributors to uncertainty: scaling factor for horizontal dispersion, dry deposition velocity, inhalation protection factor for nonevacuees, groundshine shielding factor for nonevacuees, early fatality hazard function alpha value for bone marrow exposure, and scaling factor for vertical dispersion
Saad, Bilal Mohammed
2017-09-18
This work focuses on the simulation of CO2 storage in deep underground formations under uncertainty and seeks to understand the impact of uncertainties in reservoir properties on CO2 leakage. To simulate the process, a non-isothermal two-phase two-component flow system with equilibrium phase exchange is used. Since model evaluations are computationally intensive, instead of traditional Monte Carlo methods, we rely on polynomial chaos (PC) expansions for representation of the stochastic model response. A non-intrusive approach is used to determine the PC coefficients. We establish the accuracy of the PC representations within a reasonable error threshold through systematic convergence studies. In addition to characterizing the distributions of model observables, we compute probabilities of excess CO2 leakage. Moreover, we consider the injection rate as a design parameter and compute an optimum injection rate that ensures that the risk of excess pressure buildup at the leaky well remains below acceptable levels. We also provide a comprehensive analysis of sensitivities of CO2 leakage, where we compute the contributions of the random parameters, and their interactions, to the variance by computing first, second, and total order Sobol’ indices.
Huan, Xun; Safta, Cosmin; Sargsyan, Khachik; Geraci, Gianluca; Eldred, Michael S.; Vane, Zachary P.; Lacaze, Guilhem; Oefelein, Joseph C.; Najm, Habib N.
2018-03-01
The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress toward optimal engine designs requires accurate flow simulations together with uncertainty quantification. However, performing uncertainty quantification for scramjet simulations is challenging due to the large number of uncertain parameters involved and the high computational cost of flow simulations. These difficulties are addressed in this paper by developing practical uncertainty quantification algorithms and computational methods, and deploying them in the current study to large-eddy simulations of a jet in crossflow inside a simplified HIFiRE Direct Connect Rig scramjet combustor. First, global sensitivity analysis is conducted to identify influential uncertain input parameters, which can help reduce the systems stochastic dimension. Second, because models of different fidelity are used in the overall uncertainty quantification assessment, a framework for quantifying and propagating the uncertainty due to model error is presented. These methods are demonstrated on a nonreacting jet-in-crossflow test problem in a simplified scramjet geometry, with parameter space up to 24 dimensions, using static and dynamic treatments of the turbulence subgrid model, and with two-dimensional and three-dimensional geometries.
Saad, Bilal Mohammed; Alexanderian, Alen; Prudhomme, Serge; Knio, Omar
2017-01-01
This work focuses on the simulation of CO2 storage in deep underground formations under uncertainty and seeks to understand the impact of uncertainties in reservoir properties on CO2 leakage. To simulate the process, a non-isothermal two-phase two-component flow system with equilibrium phase exchange is used. Since model evaluations are computationally intensive, instead of traditional Monte Carlo methods, we rely on polynomial chaos (PC) expansions for representation of the stochastic model response. A non-intrusive approach is used to determine the PC coefficients. We establish the accuracy of the PC representations within a reasonable error threshold through systematic convergence studies. In addition to characterizing the distributions of model observables, we compute probabilities of excess CO2 leakage. Moreover, we consider the injection rate as a design parameter and compute an optimum injection rate that ensures that the risk of excess pressure buildup at the leaky well remains below acceptable levels. We also provide a comprehensive analysis of sensitivities of CO2 leakage, where we compute the contributions of the random parameters, and their interactions, to the variance by computing first, second, and total order Sobol’ indices.
Energy Technology Data Exchange (ETDEWEB)
Huan, Xun [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Safta, Cosmin [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Sargsyan, Khachik [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Geraci, Gianluca [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Eldred, Michael S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Vane, Zachary P. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Lacaze, Guilhem [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Oefelein, Joseph C. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Najm, Habib N. [Sandia National Lab. (SNL-CA), Livermore, CA (United States)
2018-02-09
The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress toward optimal engine designs requires accurate flow simulations together with uncertainty quantification. However, performing uncertainty quantification for scramjet simulations is challenging due to the large number of uncertain parameters involved and the high computational cost of flow simulations. These difficulties are addressed in this paper by developing practical uncertainty quantification algorithms and computational methods, and deploying them in the current study to large-eddy simulations of a jet in crossflow inside a simplified HIFiRE Direct Connect Rig scramjet combustor. First, global sensitivity analysis is conducted to identify influential uncertain input parameters, which can help reduce the system’s stochastic dimension. Second, because models of different fidelity are used in the overall uncertainty quantification assessment, a framework for quantifying and propagating the uncertainty due to model error is presented. Finally, these methods are demonstrated on a nonreacting jet-in-crossflow test problem in a simplified scramjet geometry, with parameter space up to 24 dimensions, using static and dynamic treatments of the turbulence subgrid model, and with two-dimensional and three-dimensional geometries.
Ellis, Alicia M.; Garcia, Andres J.; Focks, Dana A.; Morrison, Amy C.; Scott, Thomas W.
2011-01-01
Models can be useful tools for understanding the dynamics and control of mosquito-borne disease. More detailed models may be more realistic and better suited for understanding local disease dynamics; however, evaluating model suitability, accuracy, and performance becomes increasingly difficult with greater model complexity. Sensitivity analysis is a technique that permits exploration of complex models by evaluating the sensitivity of the model to changes in parameters. Here, we present results of sensitivity analyses of two interrelated complex simulation models of mosquito population dynamics and dengue transmission. We found that dengue transmission may be influenced most by survival in each life stage of the mosquito, mosquito biting behavior, and duration of the infectious period in humans. The importance of these biological processes for vector-borne disease models and the overwhelming lack of knowledge about them make acquisition of relevant field data on these biological processes a top research priority. PMID:21813844
Uncertainty Quantification and Sensitivity Analysis in the CICE v5.1 Sea Ice Model
Urrego-Blanco, J. R.; Urban, N. M.
2015-12-01
Changes in the high latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with mid latitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. In this work we characterize parametric uncertainty in Los Alamos Sea Ice model (CICE) and quantify the sensitivity of sea ice area, extent and volume with respect to uncertainty in about 40 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 40-dimensional parameter space. This approach requires a very large number of model evaluations, which are expensive to run. A more computationally efficient approach is implemented by training and cross-validating a surrogate (emulator) of the sea ice model with model output from 400 model runs. The emulator is used to make predictions of sea ice extent, area, and volume at several model configurations, which are then used to compute the Sobol sensitivity indices of the 40 parameters. A ranking based on the sensitivity indices indicates that model output is most sensitive to snow parameters such as conductivity and grain size, and the drainage of melt ponds. The main effects and interactions among the most influential parameters are also estimated by a non-parametric regression technique based on generalized additive models. It is recommended research to be prioritized towards more accurately determining these most influential parameters values by observational studies or by improving existing parameterizations in the sea ice model.
International Nuclear Information System (INIS)
Aneljung, Maria; Gustafsson, Lars-Goeran
2007-04-01
. Differences in the aquifer refilling process subsequent to dry periods, for example a too slow refill when the groundwater table rises after dry summers. This may be due to local deviations in the applied pF-curves in the unsaturated zone description. Differences in near-surface groundwater elevations. For example, the calculated groundwater level reaches the ground surface during the fall and spring at locations where the measured groundwater depth is just below the ground surface. This may be due to the presence of near-surface high-conductive layers. A sensitivity analysis has been made on calibration parameters. For parameters that have 'global' effects, such as the hydraulic conductivity in the saturated zone, the analysis was performed using the 'full' model. For parameters with more local effects, such as parameters influencing the evapotranspiration and the net recharge, the model was scaled down to a column model, representing two different type areas. The most important conclusions that can be drawn from the sensitivity analysis are the following: The results indicate that the horizontal hydraulic conductivity generally should be increased at topographic highs, and reduced at local depressions in the topography. The results indicate that no changes should be made to the vertical hydraulic conductivity at locations where the horizontal conductivity has been increased, and that the vertical conductivity generally should be decreased where the horizontal conductivity has been decreased. The vegetation parameters that have the largest influence on the total groundwater recharge are the root mass distribution and the crop coefficient. The unsaturated zone parameter that have the largest influence on the total groundwater recharge is the effective porosity given in the pF-curve. In addition, the shape of the pF-curve above the water content at field capacity is also of great importance. The general conclusion is that the surrounding conditions have large effects on water
Energy Technology Data Exchange (ETDEWEB)
Aneljung, Maria; Gustafsson, Lars-Goeran [DHI Water and Environment AB, Goeteborg (Sweden)
2007-04-15
. Differences in the aquifer refilling process subsequent to dry periods, for example a too slow refill when the groundwater table rises after dry summers. This may be due to local deviations in the applied pF-curves in the unsaturated zone description. Differences in near-surface groundwater elevations. For example, the calculated groundwater level reaches the ground surface during the fall and spring at locations where the measured groundwater depth is just below the ground surface. This may be due to the presence of near-surface high-conductive layers. A sensitivity analysis has been made on calibration parameters. For parameters that have 'global' effects, such as the hydraulic conductivity in the saturated zone, the analysis was performed using the 'full' model. For parameters with more local effects, such as parameters influencing the evapotranspiration and the net recharge, the model was scaled down to a column model, representing two different type areas. The most important conclusions that can be drawn from the sensitivity analysis are the following: The results indicate that the horizontal hydraulic conductivity generally should be increased at topographic highs, and reduced at local depressions in the topography. The results indicate that no changes should be made to the vertical hydraulic conductivity at locations where the horizontal conductivity has been increased, and that the vertical conductivity generally should be decreased where the horizontal conductivity has been decreased. The vegetation parameters that have the largest influence on the total groundwater recharge are the root mass distribution and the crop coefficient. The unsaturated zone parameter that have the largest influence on the total groundwater recharge is the effective porosity given in the pF-curve. In addition, the shape of the pF-curve above the water content at field capacity is also of great importance. The general conclusion is that the surrounding conditions have
Global optimization and sensitivity analysis
International Nuclear Information System (INIS)
Cacuci, D.G.
1990-01-01
A new direction for the analysis of nonlinear models of nuclear systems is suggested to overcome fundamental limitations of sensitivity analysis and optimization methods currently prevalent in nuclear engineering usage. This direction is toward a global analysis of the behavior of the respective system as its design parameters are allowed to vary over their respective design ranges. Presented is a methodology for global analysis that unifies and extends the current scopes of sensitivity analysis and optimization by identifying all the critical points (maxima, minima) and solution bifurcation points together with corresponding sensitivities at any design point of interest. The potential applicability of this methodology is illustrated with test problems involving multiple critical points and bifurcations and comprising both equality and inequality constraints
Random variables in forest policy: A systematic sensitivity analysis using CGE models
International Nuclear Information System (INIS)
Alavalapati, J.R.R.
1999-01-01
Computable general equilibrium (CGE) models are extensively used to simulate economic impacts of forest policies. Parameter values used in these models often play a central role in their outcome. Since econometric studies and best guesses are the main sources of these parameters, some randomness exists about the 'true' values of these parameters. Failure to incorporate this randomness into these models may limit the degree of confidence in the validity of the results. In this study, we conduct a systematic sensitivity analysis (SSA) to assess the economic impacts of: 1) a 1 % increase in tax on Canadian lumber and wood products exports to the United States (US), and 2) a 1% decrease in technical change in the lumber and wood products and pulp and paper sectors of the US and Canada. We achieve this task by using an aggregated version of global trade model developed by Hertel (1997) and the automated SSA procedure developed by Arndt and Pearson (1996). The estimated means and standard deviations suggest that certain impacts are more likely than others. For example, an increase in export tax is likely to cause a decrease in Canadian income, while an increase in US income is unlikely. On the other hand, a decrease in US welfare is likely, while an increase in Canadian welfare is unlikely, in response to an increase in tax. It is likely that income and welfare both fall in Canada and the US in response to a decrease in the technical change in lumber and wood products and pulp and paper sectors 21 refs, 1 fig, 5 tabs
Directory of Open Access Journals (Sweden)
J. D. Herman
2013-07-01
Full Text Available The increase in spatially distributed hydrologic modeling warrants a corresponding increase in diagnostic methods capable of analyzing complex models with large numbers of parameters. Sobol' sensitivity analysis has proven to be a valuable tool for diagnostic analyses of hydrologic models. However, for many spatially distributed models, the Sobol' method requires a prohibitive number of model evaluations to reliably decompose output variance across the full set of parameters. We investigate the potential of the method of Morris, a screening-based sensitivity approach, to provide results sufficiently similar to those of the Sobol' method at a greatly reduced computational expense. The methods are benchmarked on the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM over a six-month period in the Blue River watershed, Oklahoma, USA. The Sobol' method required over six million model evaluations to ensure reliable sensitivity indices, corresponding to more than 30 000 computing hours and roughly 180 gigabytes of storage space. We find that the method of Morris is able to correctly screen the most and least sensitive parameters with 300 times fewer model evaluations, requiring only 100 computing hours and 1 gigabyte of storage space. The method of Morris proves to be a promising diagnostic approach for global sensitivity analysis of highly parameterized, spatially distributed hydrologic models.
Meyer, P. D.; Yabusaki, S.; Curtis, G. P.; Ye, M.; Fang, Y.
2011-12-01
A three-dimensional, variably-saturated flow and multicomponent biogeochemical reactive transport model of uranium bioremediation was used to generate synthetic data . The 3-D model was based on a field experiment at the U.S. Dept. of Energy Rifle Integrated Field Research Challenge site that used acetate biostimulation of indigenous metal reducing bacteria to catalyze the conversion of aqueous uranium in the +6 oxidation state to immobile solid-associated uranium in the +4 oxidation state. A key assumption in past modeling studies at this site was that a comprehensive reaction network could be developed largely through one-dimensional modeling. Sensitivity analyses and parameter estimation were completed for a 1-D reactive transport model abstracted from the 3-D model to test this assumption, to identify parameters with the greatest potential to contribute to model predictive uncertainty, and to evaluate model structure and data limitations. Results showed that sensitivities of key biogeochemical concentrations varied in space and time, that model nonlinearities and/or parameter interactions have a significant impact on calculated sensitivities, and that the complexity of the model's representation of processes affecting Fe(II) in the system may make it difficult to correctly attribute observed Fe(II) behavior to modeled processes. Non-uniformity of the 3-D simulated groundwater flux and averaging of the 3-D synthetic data for use as calibration targets in the 1-D modeling resulted in systematic errors in the 1-D model parameter estimates and outputs. This occurred despite using the same reaction network for 1-D modeling as used in the data-generating 3-D model. Predictive uncertainty of the 1-D model appeared to be significantly underestimated by linear parameter uncertainty estimates.
International Nuclear Information System (INIS)
Guerrieri, A.
2009-01-01
In this report the largest Lyapunov characteristic exponent of a high dimensional atmospheric global circulation model of intermediate complexity has been estimated numerically. A sensitivity analysis has been carried out by varying the equator-to-pole temperature difference, the space resolution and the value of some parameters employed by the model. Chaotic and non-chaotic regimes of circulation have been found. [it
Models for Risk Aggregation and Sensitivity Analysis: An Application to Bank Economic Capital
Directory of Open Access Journals (Sweden)
Hulusi Inanoglu
2009-12-01
Full Text Available A challenge in enterprise risk measurement for diversified financial institutions is developing a coherent approach to aggregating different risk types. This has been motivated by rapid financial innovation, developments in supervisory standards (Basel 2 and recent financial turmoil. The main risks faced - market, credit and operational – have distinct distributional properties, and historically have been modeled in differing frameworks. We contribute to the modeling effort by providing tools and insights to practitioners and regulators. First, we extend the scope of the analysis to liquidity and interest rate risk, having Basel Pillar II of Basel implications. Second, we utilize data from major banking institutions’ loss experience from supervisory call reports, which allows us to explore the impact of business mix and inter-risk correlations on total risk. Third, we estimate and compare alternative established frameworks for risk aggregation (including copula models on the same data-sets across banks, comparing absolute total risk measures (Value-at-Risk – VaR and proportional diversification benefits-PDB, goodness-of-fit (GOF of the model as data as well as the variability of the VaR estimate with respect to sampling error in parameter. This benchmarking and sensitivity analysis suggests that practitioners consider implementing a simple non-parametric methodology (empirical copula simulation- ECS in order to quantify integrated risk, in that it is found to be more conservatism and stable than the other models. We observe that ECS produces 20% to 30% higher VaR relative to the standard Gaussian copula simulation (GCS, while the variance-covariance approximation (VCA is much lower. ECS yields the highest PDBs than other methodologies (127% to 243%, while Archimadean Gumbel copula simulation (AGCS is the lowest (10-21%. Across the five largest banks we fail to find the effect of business mix to exert a directionally consistent impact on
Sensitivity analysis for the coupling of a subglacial hydrology model with a 3D ice-sheet model.
Bertagna, L.; Perego, M.; Gunzburger, M.; Hoffman, M. J.; Price, S. F.
2017-12-01
When studying the movement of ice sheets, one of the most important factors that influence the velocity of the ice is the amount of friction against the bedrock. Usually, this is modeled by a friction coefficient that may depend on the bed geometry and other quantities, such as the temperature and/or water pressure at the ice-bedrock interface. These quantities are often assumed to be known (either by indirect measurements or by means of parameter estimation) and constant in time. Here, we present a 3D computational model for the simulation of the ice dynamics which incorporates a 2D model proposed by Hewitt (2011) for the subglacial water pressure. The hydrology model is fully coupled with the Blatter-Pattyn model for the ice sheet flow, as the subglacial water pressure appears in the expression for the ice friction coefficient, and the ice velocity appears as a source term in the hydrology model. We will present results on real geometries, and perform a sensitivity analysis with respect to the hydrology model parameters.
International Nuclear Information System (INIS)
Helton, J.C.; Johnson, J.D.; McKay, M.D.; Shiver, A.W.; Sprung, J.L.
1995-01-01
Uncertainty and sensitivity analysis techniques based on Latin hypercube sampling, partial correlation analysis and stepwise regression analysis were used in an investigation with the MACCS model of the early health effects associated with a severe accident at a nuclear power station. The following results were obtained in tests to check the robustness of the analysis techniques: two independent Latin hypercube samples produced similar uncertainty and sensitivity analysis results; setting important variables to best-estimate values produced substantial reductions in uncertainty, while setting the less important variables to best-estimate values had little effect on uncertainty; similar sensitivity analysis results were obtained when the original uniform and loguniform distributions assigned to the 34 imprecisely known input variables were changed to left-triangular distributions and then to right-triangular distributions; and analyses with rank-transformed and logarithmically-transformed data produced similar results and substantially outperformed analyses with raw (i.e., untransformed) data
International Nuclear Information System (INIS)
Helton, J.C.; Johnson, J.D.; Rollstin, J.A.; Shiver, A.W.; Sprung, J.L.
1995-01-01
Uncertainty and sensitivity analysis techniques based on Latin hypercube sampling, partial correlation analysis and stepwise regression analysis are used in an investigation with the MACCS model of the chronic exposure pathways associated with a severe accident at a nuclear power station. The primary purpose of this study is to provide guidance on the variables to be considered in future review work to reduce the uncertainty in the important variables used in the calculation of reactor accident consequences. The effects of 75 imprecisely known input variables on the following reactor accident consequences are studied: crop growing season dose, crop long-term dose, water ingestion dose, milk growing season dose, long-term groundshine dose, long-term inhalation dose, total food pathways dose, total ingestion pathways dose, total long-term pathways dose, total latent cancer fatalities, area-dependent cost, crop disposal cost, milk disposal cost, population-dependent cost, total economic cost, condemnation area, condemnation population, crop disposal area and milk disposal area. When the predicted variables are considered collectively, the following input variables were found to be the dominant contributors to uncertainty: dry deposition velocity, transfer of cesium from animal feed to milk, transfer of cesium from animal feed to meat, ground concentration of Cs-134 at which the disposal of milk products will be initiated, transfer of Sr-90 from soil to legumes, maximum allowable ground concentration of Sr-90 for production of crops, fraction of cesium entering surface water that is consumed in drinking water, groundshine shielding factor, scale factor defining resuspension, dose reduction associated with decontamination, and ground concentration of 1-131 at which disposal of crops will be initiated due to accidents that occur during the growing season
International Nuclear Information System (INIS)
Helton, J.C.; Johnson, J.D.; Rollstin, J.A.; Shiver, A.W.; Sprung, J.L.
1995-01-01
Uncertainty and sensitivity analysis techniques based on Latin hypercube sampling, partial correlation analysis and stepwise regression analysis are used in an investigation with the MACCS model of the food pathways associated with a severe accident at a nuclear power station. The primary purpose of this study is to provide guidance on the variables to be considered in future review work to reduce the uncertainty in the important variables used in the calculation of reactor accident consequences. The effects of 87 imprecisely-known input variables on the following reactor accident consequences are studied: crop growing season dose, crop long-term dose, milk growing season dose, total food pathways dose, total ingestion pathways dose, total long-term pathways dose, area dependent cost, crop disposal cost, milk disposal cost, condemnation area, crop disposal area and milk disposal area. When the predicted variables are considered collectively, the following input variables were found to be the dominant contributors to uncertainty: fraction of cesium deposition on grain fields that is retained on plant surfaces and transferred directly to grain, maximum allowable ground concentrations of Cs-137 and Sr-90 for production of crops, ground concentrations of Cs-134, Cs-137 and I-131 at which the disposal of milk will be initiated due to accidents that occur during the growing season, ground concentrations of Cs-134, I-131 and Sr-90 at which the disposal of crops will be initiated due to accidents that occur during the growing season, rate of depletion of Cs-137 and Sr-90 from the root zone, transfer of Sr-90 from soil to legumes, transfer of Cs-137 from soil to pasture, transfer of cesium from animal feed to meat, and the transfer of cesium, iodine and strontium from animal feed to milk
International Nuclear Information System (INIS)
Helton, J.C.; Johnson, J.D.; Rollstin, J.A.; Shiver, A.W.; Sprung, J.L.
1995-01-01
Uncertainty and sensitivity analysis techniques based on Latin hypercube sampling, partial correlation analysis and stepwise regression analysis are used in an investigation with the MACCS model of the food pathways associated with a severe accident at a nuclear power station. The primary purpose of this study is to provide guidance on the variables to be considered in future review work to reduce the uncertainty in the important variables used in the calculation of reactor accident consequences. The effects of 87 imprecisely-known input variables on the following reactor accident consequences are studied: crop growing-season dose, crop long-term dose, milk growing-season dose, total food pathways dose, total ingestion pathways dose, total long-term pathways dose, area dependent cost, crop disposal cost, milk disposal cost, condemnation area, crop disposal area and milk disposal area. When the predicted variables are considered collectively, the following input variables were found to be the dominant contributors to uncertainty: fraction of cesium deposition on grain fields that is retained on plant surfaces and transferred directly to grain, maximum allowable ground concentrations of Cs-137 and Sr-90 for production of crops, ground concentrations of Cs-134, Cs-137 and I-131 at which the disposal of milk will be initiated due to accidents that occur during the growing season, ground concentrations of Cs-134, I-131 and Sr-90 at which the disposal of crops will be initiated due to accidents that occur during the growing season, rate of depletion of Cs-137 and Sr-90 from the root zone, transfer of Sr-90 from soil to legumes, transfer of Cs-137 from soil to pasture, transfer of cesium from animal feed to meat, and the transfer of cesium, iodine and strontium from animal feed to milk
International Nuclear Information System (INIS)
Helton, J.C; Johnson, J.D; Rollstin, J.A; Shiver, A.W; Sprung, J.L
1995-01-01
Uncertainty and sensitivity analysis techniques based on Latin hypercube sampling, partial correlation analysis and stepwise regression analysis are used in an investigation with the MACCS model of the chronic exposure pathways associated with a severe accident at a nuclear power station. The primary purpose of this study is to provide guidance on the variables to be considered in future review work to reduce the uncertainty in the important variables used in the calculation of reactor accident consequences. The effects of 75 imprecisely known input variables on the following reactor accident consequences are studied: crop growing-season dose, crop long-term dose, water ingestion dose, milk growing-season dose, long-term groundshine dose, long-term inhalation dose, total food pathways dose, total ingestion pathways dose, total long-term pathways dose, total latent cancer fatalities, area-dependent cost, crop disposal cost, milk disposal cost, population-dependent cost, total economic cost, condemnation area, condemnation population, crop disposal area and milk disposal area. When the predicted variables are considered collectively, the following input variables were found to be the dominant contributors to uncertainty: dry deposition velocity, transfer of cesium from animal feed to milk, transfer of cesium from animal feed to meet, ground concentration of Cs-134 at which the disposal of milk products will be initiated, transfer of Sr-90 from soil to legumes, maximum allowable ground concentration of Sr-90 for production of crops, fraction of cesium entering surface water that is consumed in drinking water, groundshine shielding factor, scale factor defining resuspension, dose reduction associated with decontamination, and ground concentration of I-131 at which disposal of crops will be initiated due to accidents that occur during the growing season. Reducing the uncertainty in the preceding variables was found to substantially reduce the uncertainty in the
Energy Technology Data Exchange (ETDEWEB)
Morris, Edgar [Argonne National Lab. (ANL), Argonne, IL (United States)
2014-10-01
The Used Fuel Disposition Campaign (UFDC), as part of the DOE Office of Nuclear Energy’s (DOE-NE) Fuel Cycle Technology program (FCT) is investigating the disposal of high level radioactive waste (HLW) and spent nuclear fuela (SNF) in a variety of geologic media. The feasibility of disposing SNF and HLW in clay media has been investigated and has been shown to be promising [Ref. 1]. In addition the disposal of these wastes in clay media is being investigated in Belgium, France, and Switzerland. Thus, Argillaceous media is one of the environments being considered by UFDC. As identified by researchers at Sandia National Laboratory, potentially suitable formations that may exist in the U.S. include mudstone, clay, shale, and argillite formations [Ref. 1]. These formations encompass a broad range of material properties. In this report, reference to clay media is intended to cover the full range of material properties. This report presents the status of the development of a simulation model for evaluating the performance of generic clay media. The clay Generic Disposal System Model (GDSM) repository performance simulation tool has been developed with the flexibility to evaluate not only different properties, but different waste streams/forms and different repository designs and engineered barrier configurations/ materials that could be used to dispose of these wastes.
Interference and Sensitivity Analysis.
VanderWeele, Tyler J; Tchetgen Tchetgen, Eric J; Halloran, M Elizabeth
2014-11-01
Causal inference with interference is a rapidly growing area. The literature has begun to relax the "no-interference" assumption that the treatment received by one individual does not affect the outcomes of other individuals. In this paper we briefly review the literature on causal inference in the presence of interference when treatments have been randomized. We then consider settings in which causal effects in the presence of interference are not identified, either because randomization alone does not suffice for identification, or because treatment is not randomized and there may be unmeasured confounders of the treatment-outcome relationship. We develop sensitivity analysis techniques for these settings. We describe several sensitivity analysis techniques for the infectiousness effect which, in a vaccine trial, captures the effect of the vaccine of one person on protecting a second person from infection even if the first is infected. We also develop two sensitivity analysis techniques for causal effects in the presence of unmeasured confounding which generalize analogous techniques when interference is absent. These two techniques for unmeasured confounding are compared and contrasted.
Jiménez-Martínez, Joaquín; Candela, Lucila; Molinero, Jorge; Tamoh, Karim
2010-12-01
For semi-arid regions, methods of assessing aquifer recharge usually consider the potential evapotranspiration. Actual evapotranspiration rates can be below potential rates for long periods of time, even in irrigated systems. Accurate estimations of aquifer recharge in semi-arid areas under irrigated agriculture are essential for sustainable water-resources management. A method to estimate aquifer recharge from irrigated farmland has been tested. The water-balance-modelling approach was based on VisualBALAN v. 2.0, a computer code that simulates water balance in the soil, vadose zone and aquifer. The study was carried out in the Campo de Cartagena (SE Spain) in the period 1999-2008 for three different groups of crops: annual row crops (lettuce and melon), perennial vegetables (artichoke) and fruit trees (citrus). Computed mean-annual-recharge values (from irrigation+precipitation) during the study period were 397 mm for annual row crops, 201 mm for perennial vegetables and 194 mm for fruit trees: 31.4, 20.7 and 20.5% of the total applied water, respectively. The effects of rainfall events on the final recharge were clearly observed, due to the continuously high water content in soil which facilitated the infiltration process. A sensitivity analysis to assess the reliability and uncertainty of recharge estimations was carried out.
Lammoglia, Sabine-Karen; Makowski, David; Moeys, Julien; Justes, Eric; Barriuso, Enrique; Mamy, Laure
2017-02-15
STICS-MACRO is a process-based model simulating the fate of pesticides in the soil-plant system as a function of agricultural practices and pedoclimatic conditions. The objective of this work was to evaluate the influence of crop management practices on water and pesticide flows in contrasted environmental conditions. We used the Morris screening sensitivity analysis method to identify the most influential cropping practices. Crop residues management and tillage practices were shown to have strong effects on water percolation and pesticide leaching. In particular, the amount of organic residues added to soil was found to be the most influential input. The presence of a mulch could increase soil water content so water percolation and pesticide leaching. Conventional tillage was also found to decrease pesticide leaching, compared to no-till, which is consistent with many field observations. The effects of the soil, crop and climate conditions tested in this work were less important than those of cropping practices. STICS-MACRO allows an ex ante evaluation of cropping systems and agricultural practices, and of the related pesticides environmental impacts. Copyright © 2016 Elsevier B.V. All rights reserved.
International Nuclear Information System (INIS)
Horwedel, J.E.; Wright, R.Q.; Maerker, R.E.
1990-01-01
A sensitivity analysis of EQ3, a computer code which has been proposed to be used as one link in the overall performance assessment of a national high-level waste repository, has been performed. EQ3 is a geochemical modeling code used to calculate the speciation of a water and its saturation state with respect to mineral phases. The model chosen for the sensitivity analysis is one which is used as a test problem in the documentation of the EQ3 code. Sensitivities are calculated using both the CHAIN and ADGEN options of the GRESS code compiled under G-float FORTRAN on the VAX/VMS and verified by perturbation runs. The analyses were performed with a preliminary Version 1.0 of GRESS which contains several new algorithms that significantly improve the application of ADGEN. Use of ADGEN automates the implementation of the well-known adjoint technique for the efficient calculation of sensitivities of a given response to all the input data. Application of ADGEN to EQ3 results in the calculation of sensitivities of a particular response to 31,000 input parameters in a run time of only 27 times that of the original model. Moreover, calculation of the sensitivities for each additional response increases this factor by only 2.5 percent. This compares very favorably with a running-time factor of 31,000 if direct perturbation runs were used instead. 6 refs., 8 tabs
This paper assesses the impact of different likelihood functions in identifying sensitive parameters of the highly parameterized, spatially distributed Soil and Water Assessment Tool (SWAT) watershed model for multiple variables at multiple sites. The global one-factor-at-a-time (OAT) method of Morr...
El Habachi, Aimad; Moissenet, Florent; Duprey, Sonia; Cheze, Laurence; Dumas, Raphaël
2015-07-01
Sensitivity analysis is a typical part of biomechanical models evaluation. For lower limb multi-body models, sensitivity analyses have been mainly performed on musculoskeletal parameters, more rarely on the parameters of the joint models. This study deals with a global sensitivity analysis achieved on a lower limb multi-body model that introduces anatomical constraints at the ankle, tibiofemoral, and patellofemoral joints. The aim of the study was to take into account the uncertainty of parameters (e.g. 2.5 cm on the positions of the skin markers embedded in the segments, 5° on the orientation of hinge axis, 2.5 mm on the origin and insertion of ligaments) using statistical distributions and propagate it through a multi-body optimisation method used for the computation of joint kinematics from skin markers during gait. This will allow us to identify the most influential parameters on the minimum of the objective function of the multi-body optimisation (i.e. the sum of the squared distances between measured and model-determined skin marker positions) and on the joint angles and displacements. To quantify this influence, a Fourier-based algorithm of global sensitivity analysis coupled with a Latin hypercube sampling is used. This sensitivity analysis shows that some parameters of the motor constraints, that is to say the distances between measured and model-determined skin marker positions, and the kinematic constraints are highly influencing the joint kinematics obtained from the lower limb multi-body model, for example, positions of the skin markers embedded in the shank and pelvis, parameters of the patellofemoral hinge axis, and parameters of the ankle and tibiofemoral ligaments. The resulting standard deviations on the joint angles and displacements reach 36° and 12 mm. Therefore, personalisation, customisation or identification of these most sensitive parameters of the lower limb multi-body models may be considered as essential.
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.
Lasa, A.; Borodin, D.; Canik, J. M.; Klepper, C. C.; Groth, M.; Kirschner, A.; Airila, M. I.; Borodkina, I.; Ding, R.; Contributors, JET
2018-01-01
Experiments at JET showed locally enhanced, asymmetric beryllium (Be) erosion at outer wall limiters when magnetically connected ICRH antennas were in operation. A first modeling effort using the 3D erosion and scrape-off layer impurity transport modeling code ERO reproduced qualitatively the experimental outcome. However, local plasma parameters—in particular when 3D distributions are of interest—can be difficult to determine from available diagnostics and so erosion / impurity transport modeling input relies on output from other codes and simplified models, increasing uncertainties in the outcome. In the present contribution, we introduce and evaluate the impact of improved models and parameters with largest uncertainties of processes that impact impurity production and transport across the scrape-off layer, when simulated in ERO: (i) the magnetic geometry has been revised, for affecting the separatrix position (located 50-60 mm away from limiter surface) and thus the background plasma profiles; (ii) connection lengths between components, which lead to shadowing of ion fluxes, are also affected by the magnetic configuration; (iii) anomalous transport of ionized impurities, defined by the perpendicular diffusion coefficient, has been revisited; (iv) erosion yields that account for energy and angular distributions of background plasma ions under the present enhanced sheath potential and oblique magnetic field, have been introduced; (v) the effect of additional erosion sources, such as charge-exchange neutral fluxes, which are dominant in recessed areas like antennas, has been evaluated; (vi) chemically assisted release of Be in molecular form has been included. Sensitivity analysis highlights a qualitative effect (i.e. change in emission patterns) of magnetic shadowing, anomalous diffusion, and inclusion of neutral fluxes and molecular release of Be. The separatrix location, and energy and angular distribution of background plasma fluxes impact erosion
Inverse modelling of atmospheric tracers: non-Gaussian methods and second-order sensitivity analysis
Directory of Open Access Journals (Sweden)
M. Bocquet
2008-02-01
Full Text Available For a start, recent techniques devoted to the reconstruction of sources of an atmospheric tracer at continental scale are introduced. A first method is based on the principle of maximum entropy on the mean and is briefly reviewed here. A second approach, which has not been applied in this field yet, is based on an exact Bayesian approach, through a maximum a posteriori estimator. The methods share common grounds, and both perform equally well in practice. When specific prior hypotheses on the sources are taken into account such as positivity, or boundedness, both methods lead to purposefully devised cost-functions. These cost-functions are not necessarily quadratic because the underlying assumptions are not Gaussian. As a consequence, several mathematical tools developed in data assimilation on the basis of quadratic cost-functions in order to establish a posteriori analysis, need to be extended to this non-Gaussian framework. Concomitantly, the second-order sensitivity analysis needs to be adapted, as well as the computations of the averaging kernels of the source and the errors obtained in the reconstruction. All of these developments are applied to a real case of tracer dispersion: the European Tracer Experiment [ETEX]. Comparisons are made between a least squares cost function (similar to the so-called 4D-Var approach and a cost-function which is not based on Gaussian hypotheses. Besides, the information content of the observations which is used in the reconstruction is computed and studied on the application case. A connection with the degrees of freedom for signal is also established. As a by-product of these methodological developments, conclusions are drawn on the information content of the ETEX dataset as seen from the inverse modelling point of view.
Huang, Jiacong; Gao, Junfeng; Yan, Renhua
2016-08-15
Phosphorus (P) export from lowland polders has caused severe water pollution. Numerical models are an important resource that help water managers control P export. This study coupled three models, i.e., Phosphorus Dynamic model for Polders (PDP), Integrated Catchments model of Phosphorus dynamics (INCA-P) and Universal Soil Loss Equation (USLE), to describe the P dynamics in polders. Based on the coupled models and a dataset collected from Polder Jian in China, sensitivity analysis were carried out to analyze the cause-effect relationships between environmental factors and P export from Polder Jian. The sensitivity analysis results showed that P export from Polder Jian were strongly affected by air temperature, precipitation and fertilization. Proper fertilization management should be a strategic priority for reducing P export from Polder Jian. This study demonstrated the success of model coupling, and its application in investigating potential strategies to support pollution control in polder systems. Copyright © 2016. Published by Elsevier B.V.
Directory of Open Access Journals (Sweden)
Z. Xu
2018-01-01
Full Text Available Long-distance seawater intrusion has been widely observed through the subsurface conduit system in coastal karst aquifers as a source of groundwater contaminant. In this study, seawater intrusion in a dual-permeability karst aquifer with conduit networks is studied by the two-dimensional density-dependent flow and transport SEAWAT model. Local and global sensitivity analyses are used to evaluate the impacts of boundary conditions and hydrological characteristics on modeling seawater intrusion in a karst aquifer, including hydraulic conductivity, effective porosity, specific storage, and dispersivity of the conduit network and of the porous medium. The local sensitivity analysis evaluates the parameters' sensitivities for modeling seawater intrusion, specifically in the Woodville Karst Plain (WKP. A more comprehensive interpretation of parameter sensitivities, including the nonlinear relationship between simulations and parameters, and/or parameter interactions, is addressed in the global sensitivity analysis. The conduit parameters and boundary conditions are important to the simulations in the porous medium because of the dynamical exchanges between the two systems. The sensitivity study indicates that salinity and head simulations in the karst features, such as the conduit system and submarine springs, are critical for understanding seawater intrusion in a coastal karst aquifer. The evaluation of hydraulic conductivity sensitivity in the continuum SEAWAT model may be biased since the conduit flow velocity is not accurately calculated by Darcy's equation as a function of head difference and hydraulic conductivity. In addition, dispersivity is no longer an important parameter in an advection-dominated karst aquifer with a conduit system, compared to the sensitivity results in a porous medium aquifer. In the end, the extents of seawater intrusion are quantitatively evaluated and measured under different scenarios with the variabilities of
International Nuclear Information System (INIS)
Reyes F, M. C.; Del Valle G, E.; Gomez T, A. M.; Sanchez E, V.
2015-09-01
A methodology was implemented to carry out a sensitivity and uncertainty analysis for cross sections used in a coupled model for Trace/Parcs in a transient of control rod fall of a BWR-5. A model of the reactor core for the neutronic code Parcs was used, in which the assemblies located in the core are described. Thermo-hydraulic model in Trace was a simple model, where only a component type Chan was designed to represent all the core assemblies, which it was within a single vessel and boundary conditions were established. The thermo-hydraulic part was coupled with the neutron part, first for the steady state and then a transient of control rod fall was carried out for the sensitivity and uncertainty analysis. To carry out the analysis of cross sections used in the coupled model Trace/Parcs during the transient, the Probability Density Functions for 22 parameters selected from the total of neutronic parameters that use Parcs were generated, obtaining 100 different cases for the coupled model Trace/Parcs, each one with a database of different cross sections. All these cases were executed with the coupled model, obtaining in consequence 100 different output files for the transient of control rod fall doing emphasis in the nominal power, for which an uncertainty analysis was realized at the same time generate the band of uncertainty. With this analysis is possible to observe the ranges of results of the elected responses varying the selected uncertainty parameters. The sensitivity analysis complements the uncertainty analysis, identifying the parameter or parameters with more influence on the results and thus focuses on these parameters in order to better understand their effects. Beyond the obtained results, because is not a model with real operation data, the importance of this work is to know the application of the methodology to carry out the sensitivity and uncertainty analyses. (Author)
Sullivan, Adam John
In chapter 1, we consider the biases that may arise when an unmeasured confounder is omitted from a structural equation model (SEM) and sensitivity analysis techniques to correct for such biases. We give an analysis of which effects in an SEM are and are not biased by an unmeasured confounder. It is shown that a single unmeasured confounder will bias not just one but numerous effects in an SEM. We present sensitivity analysis techniques to correct for biases in total, direct, and indirect effects when using SEM analyses, and illustrate these techniques with a study of aging and cognitive function. In chapter 2, we consider longitudinal mediation with latent growth curves. We define the direct and indirect effects using counterfactuals and consider the assumptions needed for identifiability of those effects. We develop models with a binary treatment/exposure followed by a model where treatment/exposure changes with time allowing for treatment/exposure-mediator interaction. We thus formalize mediation analysis with latent growth curve models using counterfactuals, makes clear the assumptions and extends these methods to allow for exposure mediator interactions. We present and illustrate the techniques with a study on Multiple Sclerosis(MS) and depression. In chapter 3, we report on a pilot study in blended learning that took place during the Fall 2013 and Summer 2014 semesters here at Harvard. We blended the traditional BIO 200: Principles of Biostatistics and created ID 200: Principles of Biostatistics and epidemiology. We used materials from the edX course PH207x: Health in Numbers: Quantitative Methods in Clinical & Public Health Research and used. These materials were used as a video textbook in which students would watch a given number of these videos prior to class. Using surveys as well as exam data we informally assess these blended classes from the student's perspective as well as a comparison of these students with students in another course, BIO 201
Sensitivity Assessment of Ozone Models
Energy Technology Data Exchange (ETDEWEB)
Shorter, Jeffrey A.; Rabitz, Herschel A.; Armstrong, Russell A.
2000-01-24
The activities under this contract effort were aimed at developing sensitivity analysis techniques and fully equivalent operational models (FEOMs) for applications in the DOE Atmospheric Chemistry Program (ACP). MRC developed a new model representation algorithm that uses a hierarchical, correlated function expansion containing a finite number of terms. A full expansion of this type is an exact representation of the original model and each of the expansion functions is explicitly calculated using the original model. After calculating the expansion functions, they are assembled into a fully equivalent operational model (FEOM) that can directly replace the original mode.
Zokagoa, Jean-Marie; Soulaïmani, Azzeddine
2012-06-01
This article presents a reduced-order model (ROM) of the shallow water equations (SWEs) for use in sensitivity analyses and Monte-Carlo type applications. Since, in the real world, some of the physical parameters and initial conditions embedded in free-surface flow problems are difficult to calibrate accurately in practice, the results from numerical hydraulic models are almost always corrupted with uncertainties. The main objective of this work is to derive a ROM that ensures appreciable accuracy and a considerable acceleration in the calculations so that it can be used as a surrogate model for stochastic and sensitivity analyses in real free-surface flow problems. The ROM is derived using the proper orthogonal decomposition (POD) method coupled with Galerkin projections of the SWEs, which are discretised through a finite-volume method. The main difficulty of deriving an efficient ROM is the treatment of the nonlinearities involved in SWEs. Suitable approximations that provide rapid online computations of the nonlinear terms are proposed. The proposed ROM is applied to the simulation of hypothetical flood flows in the Bordeaux breakwater, a portion of the 'Rivière des Prairies' located near Laval (a suburb of Montreal, Quebec). A series of sensitivity analyses are performed by varying the Manning roughness coefficient and the inflow discharge. The results are satisfactorily compared to those obtained by the full-order finite volume model.
Lee, Yeonok; Wu, Hulin
2012-01-01
Differential equation models are widely used for the study of natural phenomena in many fields. The study usually involves unknown factors such as initial conditions and/or parameters. It is important to investigate the impact of unknown factors (parameters and initial conditions) on model outputs in order to better understand the system the model represents. Apportioning the uncertainty (variation) of output variables of a model according to the input factors is referred to as sensitivity analysis. In this paper, we focus on the global sensitivity analysis of ordinary differential equation (ODE) models over a time period using the multivariate adaptive regression spline (MARS) as a meta model based on the concept of the variance of conditional expectation (VCE). We suggest to evaluate the VCE analytically using the MARS model structure of univariate tensor-product functions which is more computationally efficient. Our simulation studies show that the MARS model approach performs very well and helps to significantly reduce the computational cost. We present an application example of sensitivity analysis of ODE models for influenza infection to further illustrate the usefulness of the proposed method.
Sensitivity and uncertainty analysis for the annual phosphorus loss estimator model.
Bolster, Carl H; Vadas, Peter A
2013-07-01
Models are often used to predict phosphorus (P) loss from agricultural fields. Although it is commonly recognized that model predictions are inherently uncertain, few studies have addressed prediction uncertainties using P loss models. In this study we assessed the effect of model input error on predictions of annual P loss by the Annual P Loss Estimator (APLE) model. Our objectives were (i) to conduct a sensitivity analyses for all APLE input variables to determine which variables the model is most sensitive to, (ii) to determine whether the relatively easy-to-implement first-order approximation (FOA) method provides accurate estimates of model prediction uncertainties by comparing results with the more accurate Monte Carlo simulation (MCS) method, and (iii) to evaluate the performance of the APLE model against measured P loss data when uncertainties in model predictions and measured data are included. Our results showed that for low to moderate uncertainties in APLE input variables, the FOA method yields reasonable estimates of model prediction uncertainties, although for cases where manure solid content is between 14 and 17%, the FOA method may not be as accurate as the MCS method due to a discontinuity in the manure P loss component of APLE at a manure solid content of 15%. The estimated uncertainties in APLE predictions based on assumed errors in the input variables ranged from ±2 to 64% of the predicted value. Results from this study highlight the importance of including reasonable estimates of model uncertainty when using models to predict P loss. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.
Parametric sensitivity analysis for the helium dimers on a model potential
Directory of Open Access Journals (Sweden)
Nelson Henrique Teixeira Lemes
2012-01-01
Full Text Available Potential parameters sensitivity analysis for helium unlike molecules, HeNe, HeAr, HeKr and HeXe is the subject of this work. Number of bound states these rare gas dimers can support, for different angular momentum, will be presented and discussed. The variable phase method, together with the Levinson's theorem, is used to explore the quantum scattering process at very low collision energy using the Tang and Toennies potential. These diatomic dimers can support a bound state even for relative angular momentum equal to five, as in HeXe. Vibrational excited states, with zero angular momentum, are also possible for HeKr and HeXe. Results from sensitive analysis will give acceptable order of magnitude on potentials parameters.
Renewable Energy Deployment in Colorado and the West: A Modeling Sensitivity and GIS Analysis
Energy Technology Data Exchange (ETDEWEB)
Barrows, Clayton [National Renewable Energy Lab. (NREL), Golden, CO (United States); Mai, Trieu [National Renewable Energy Lab. (NREL), Golden, CO (United States); Haase, Scott [National Renewable Energy Lab. (NREL), Golden, CO (United States); Melius, Jennifer [National Renewable Energy Lab. (NREL), Golden, CO (United States); Mooney, Meghan [National Renewable Energy Lab. (NREL), Golden, CO (United States)
2016-03-01
The Resource Planning Model is a capacity expansion model designed for a regional power system, such as a utility service territory, state, or balancing authority. We apply a geospatial analysis to Resource Planning Model renewable energy capacity expansion results to understand the likelihood of renewable development on various lands within Colorado.
Sensitivity analysis using probability bounding
International Nuclear Information System (INIS)
Ferson, Scott; Troy Tucker, W.
2006-01-01
Probability bounds analysis (PBA) provides analysts a convenient means to characterize the neighborhood of possible results that would be obtained from plausible alternative inputs in probabilistic calculations. We show the relationship between PBA and the methods of interval analysis and probabilistic uncertainty analysis from which it is jointly derived, and indicate how the method can be used to assess the quality of probabilistic models such as those developed in Monte Carlo simulations for risk analyses. We also illustrate how a sensitivity analysis can be conducted within a PBA by pinching inputs to precise distributions or real values
DEFF Research Database (Denmark)
Lund, Henrik; Sorknæs, Peter; Mathiesen, Brian Vad
2018-01-01
of electricity, which have been introduced in recent decades. These uncertainties pose a challenge to the design and assessment of future energy strategies and investments, especially in the economic assessment of renewable energy versus business-as-usual scenarios based on fossil fuels. From a methodological...... point of view, the typical way of handling this challenge has been to predict future prices as accurately as possible and then conduct a sensitivity analysis. This paper includes a historical analysis of such predictions, leading to the conclusion that they are almost always wrong. Not only...... are they wrong in their prediction of price levels, but also in the sense that they always seem to predict a smooth growth or decrease. This paper introduces a new method and reports the results of applying it on the case of energy scenarios for Denmark. The method implies the expectation of fluctuating fuel...
Wu, Yiping; Liu, Shuguang; Huang, Zhihong; Yan, Wende
2014-01-01
Ecosystem models are useful tools for understanding ecological processes and for sustainable management of resources. In biogeochemical field, numerical models have been widely used for investigating carbon dynamics under global changes from site to regional and global scales. However, it is still challenging to optimize parameters and estimate parameterization uncertainty for complex process-based models such as the Erosion Deposition Carbon Model (EDCM), a modified version of CENTURY, that consider carbon, water, and nutrient cycles of ecosystems. This study was designed to conduct the parameter identifiability, optimization, sensitivity, and uncertainty analysis of EDCM using our developed EDCM-Auto, which incorporated a comprehensive R package—Flexible Modeling Framework (FME) and the Shuffled Complex Evolution (SCE) algorithm. Using a forest flux tower site as a case study, we implemented a comprehensive modeling analysis involving nine parameters and four target variables (carbon and water fluxes) with their corresponding measurements based on the eddy covariance technique. The local sensitivity analysis shows that the plant production-related parameters (e.g., PPDF1 and PRDX) are most sensitive to the model cost function. Both SCE and FME are comparable and performed well in deriving the optimal parameter set with satisfactory simulations of target variables. Global sensitivity and uncertainty analysis indicate that the parameter uncertainty and the resulting output uncertainty can be quantified, and that the magnitude of parameter-uncertainty effects depends on variables and seasons. This study also demonstrates that using the cutting-edge R functions such as FME can be feasible and attractive for conducting comprehensive parameter analysis for ecosystem modeling.
Chemical kinetic functional sensitivity analysis: Elementary sensitivities
International Nuclear Information System (INIS)
Demiralp, M.; Rabitz, H.
1981-01-01
Sensitivity analysis is considered for kinetics problems defined in the space--time domain. This extends an earlier temporal Green's function method to handle calculations of elementary functional sensitivities deltau/sub i//deltaα/sub j/ where u/sub i/ is the ith species concentration and α/sub j/ is the jth system parameter. The system parameters include rate constants, diffusion coefficients, initial conditions, boundary conditions, or any other well-defined variables in the kinetic equations. These parameters are generally considered to be functions of position and/or time. Derivation of the governing equations for the sensitivities and the Green's funciton are presented. The physical interpretation of the Green's function and sensitivities is given along with a discussion of the relation of this work to earlier research
International Nuclear Information System (INIS)
Luo Yuzhou; Zhang Minghua
2009-01-01
The Soil and Water Assessment Tool (SWAT) was calibrated for hydrology conditions in an agricultural watershed of Orestimba Creek, California, and applied to simulate fate and transport of two organophosphate pesticides chlorpyrifos and diazinon. The model showed capability in evaluating pesticide fate and transport processes in agricultural fields and instream network. Management-oriented sensitivity analysis was conducted by applied stochastic SWAT simulations for pesticide distribution. Results of sensitivity analysis identified the governing processes in pesticide outputs as surface runoff, soil erosion, and sedimentation in the study area. By incorporating sensitive parameters in pesticide transport simulation, effects of structural best management practices (BMPs) in improving surface water quality were demonstrated by SWAT modeling. This study also recommends conservation practices designed to reduce field yield and in-stream transport capacity of sediment, such as filter strip, grassed waterway, crop residue management, and tailwater pond to be implemented in the Orestimba Creek watershed. - Selected structural BMPs are recommended for reducing loads of OP pesticides.
Energy Technology Data Exchange (ETDEWEB)
Luo Yuzhou [University of California, Davis, CA 95616 (United States); Wenzhou Medical College, Wenzhou 325035 (China); Zhang Minghua, E-mail: mhzhang@ucdavis.ed [University of California, Davis, CA 95616 (United States); Wenzhou Medical College, Wenzhou 325035 (China)
2009-12-15
The Soil and Water Assessment Tool (SWAT) was calibrated for hydrology conditions in an agricultural watershed of Orestimba Creek, California, and applied to simulate fate and transport of two organophosphate pesticides chlorpyrifos and diazinon. The model showed capability in evaluating pesticide fate and transport processes in agricultural fields and instream network. Management-oriented sensitivity analysis was conducted by applied stochastic SWAT simulations for pesticide distribution. Results of sensitivity analysis identified the governing processes in pesticide outputs as surface runoff, soil erosion, and sedimentation in the study area. By incorporating sensitive parameters in pesticide transport simulation, effects of structural best management practices (BMPs) in improving surface water quality were demonstrated by SWAT modeling. This study also recommends conservation practices designed to reduce field yield and in-stream transport capacity of sediment, such as filter strip, grassed waterway, crop residue management, and tailwater pond to be implemented in the Orestimba Creek watershed. - Selected structural BMPs are recommended for reducing loads of OP pesticides.
Luo, Yuzhou; Zhang, Minghua
2009-12-01
The Soil and Water Assessment Tool (SWAT) was calibrated for hydrology conditions in an agricultural watershed of Orestimba Creek, California, and applied to simulate fate and transport of two organophosphate pesticides chlorpyrifos and diazinon. The model showed capability in evaluating pesticide fate and transport processes in agricultural fields and instream network. Management-oriented sensitivity analysis was conducted by applied stochastic SWAT simulations for pesticide distribution. Results of sensitivity analysis identified the governing processes in pesticide outputs as surface runoff, soil erosion, and sedimentation in the study area. By incorporating sensitive parameters in pesticide transport simulation, effects of structural best management practices (BMPs) in improving surface water quality were demonstrated by SWAT modeling. This study also recommends conservation practices designed to reduce field yield and in-stream transport capacity of sediment, such as filter strip, grassed waterway, crop residue management, and tailwater pond to be implemented in the Orestimba Creek watershed.
International Nuclear Information System (INIS)
Campolongo, Francesca; Braddock, Roger
1999-01-01
Sensitivity analysis screening methods aim to isolate the most important factors in experiments involving a large number of significant factors and interactions. This paper extends the one-factor-at-a-time screening method proposed by Morris. The new method, in addition to the 'overall' sensitivity measures already provided by the traditional Morris method, offers estimates of the two-factor interaction effects. The number of model evaluations required is O(k 2 ), where k is the number of model input factors. The efficient sampling strategy in the parameter space is based on concepts of graph theory and on the solution of the 'handcuffed prisoner problem'
DEFF Research Database (Denmark)
Yuan, Hao; Sin, Gürkan
2011-01-01
Uncertainty and sensitivity analyses are carried out to investigate the predictive accuracy of the filtration models for describing non-Fickian transport and hyperexponential deposition. Five different modeling approaches, involving the elliptic equation with different types of distributed...... filtration coefficients and the CTRW equation expressed in Laplace space, are selected to simulate eight experiments. These experiments involve both porous media and colloid-medium interactions of different heterogeneity degrees. The uncertainty of elliptic equation predictions with distributed filtration...... coefficients is larger than that with a single filtration coefficient. The uncertainties of model predictions from the elliptic equation and CTRW equation in Laplace space are minimal for solute transport. Higher uncertainties of parameter estimation and model outputs are observed in the cases with the porous...
Energy Technology Data Exchange (ETDEWEB)
Hoseyni, Seyed Mohsen [Islamic Azad Univ., Tehran (Iran, Islamic Republic of). Dept. of Basic Sciences; Islamic Azad Univ., Tehran (Iran, Islamic Republic of). Young Researchers and Elite Club; Pourgol-Mohammad, Mohammad [Sahand Univ. of Technology, Tabriz (Iran, Islamic Republic of). Dept. of Mechanical Engineering; Yousefpour, Faramarz [Nuclear Science and Technology Research Institute, Tehran (Iran, Islamic Republic of)
2017-03-15
This paper deals with simulation, sensitivity and uncertainty analysis of LP-FP-2 experiment of LOFT test facility. The test facility simulates the major components and system response of a pressurized water reactor during a LOCA. MELCOR code is used for predicting the fission product release from the core fuel elements in LOFT LP-FP-2 experiment. Moreover, sensitivity and uncertainty analysis is performed for different CORSOR models simulating release of fission products in severe accident calculations for nuclear power plants. The calculated values for the fission product release are compared under different modeling options to the experimental data available from the experiment. In conclusion, the performance of 8 CORSOR modeling options is assessed for available modeling alternatives in the code structure.
DEFF Research Database (Denmark)
Ramin, Elham; Sin, Gürkan; Mikkelsen, Peter Steen
2011-01-01
Uncertainty derived from one of the process models – such as one-dimensional secondary settling tank (SST) models – can impact the output of the other process models, e.g., biokinetic (ASM1), as well as the integrated wastewater treatment plant (WWTP) models. The model structure and parameter...... and from the last aerobic bioreactor upstream to the SST (Garrett/hydraulic method). For model structure uncertainty, two one-dimensional secondary settling tank (1-D SST) models are assessed, including a first-order model (the widely used Takács-model), in which the feasibility of using measured...... uncertainty of settler models can therefore propagate, and add to the uncertainties in prediction of any plant performance criteria. Here we present an assessment of the relative significance of secondary settling model performance in WWTP simulations. We perform a global sensitivity analysis (GSA) based...
Casadebaig, Pierre; Zheng, Bangyou; Chapman, Scott; Huth, Neil; Faivre, Robert; Chenu, Karine
2015-01-01
A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and de...
DEFF Research Database (Denmark)
Ramin, Elham; Flores Alsina, Xavier; Sin, Gürkan
2014-01-01
This study investigates the sensitivity of wastewater treatment plant (WWTP) model performance to the selection of one-dimensional secondary settling tanks (1-D SST) models with first-order and second-order mathematical structures. We performed a global sensitivity analysis (GSA) on the benchmark...... simulation model No.2 with the input uncertainty associated to the biokinetic parameters in the activated sludge model No. 1 (ASM1), a fractionation parameter in the primary clarifier, and the settling parameters in the SST model. Based on the parameter sensitivity rankings obtained in this study......, the settling parameters were found to be as influential as the biokinetic parameters on the uncertainty of WWTP model predictions, particularly for biogas production and treated water quality. However, the sensitivity measures were found to be dependent on the 1-D SST models selected. Accordingly, we suggest...
Sensitivity analysis of a simple linear model of a savanna ecosystem at Nyslvley
CSIR Research Space (South Africa)
Getz, WA
1975-12-01
Full Text Available parameters is analysed. The results obtained from this analysis are discussed and some general statements on important structures in the Nylsvley ecosystem that emerge from the analysis of the model are made. In particular certain conclusions are drawn...
Myers, Jerry G.; Young, M.; Goodenow, Debra A.; Keenan, A.; Walton, M.; Boley, L.
2015-01-01
Model and simulation (MS) credibility is defined as, the quality to elicit belief or trust in MS results. NASA-STD-7009 [1] delineates eight components (Verification, Validation, Input Pedigree, Results Uncertainty, Results Robustness, Use History, MS Management, People Qualifications) that address quantifying model credibility, and provides guidance to the model developers, analysts, and end users for assessing the MS credibility. Of the eight characteristics, input pedigree, or the quality of the data used to develop model input parameters, governing functions, or initial conditions, can vary significantly. These data quality differences have varying consequences across the range of MS application. NASA-STD-7009 requires that the lowest input data quality be used to represent the entire set of input data when scoring the input pedigree credibility of the model. This requirement provides a conservative assessment of model inputs, and maximizes the communication of the potential level of risk of using model outputs. Unfortunately, in practice, this may result in overly pessimistic communication of the MS output, undermining the credibility of simulation predictions to decision makers. This presentation proposes an alternative assessment mechanism, utilizing results parameter robustness, also known as model input sensitivity, to improve the credibility scoring process for specific simulations.
DEFF Research Database (Denmark)
Ferrari, A.; Gutierrez, S.; Sin, Gürkan
2016-01-01
A steady state model for a production scale milk drying process was built to help process understanding and optimization studies. It involves a spray chamber and also internal/external fluid beds. The model was subjected to a comprehensive statistical analysis for quality assurance using...
Saloranta, Tuomo M; Andersen, Tom; Naes, Kristoffer
2006-01-01
Rate constant bioaccumulation models are applied to simulate the flow of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in the coastal marine food web of Frierfjorden, a contaminated fjord in southern Norway. We apply two different ways to parameterize the rate constants in the model, global sensitivity analysis of the models using Extended Fourier Amplitude Sensitivity Test (Extended FAST) method, as well as results from general linear system theory, in order to obtain a more thorough insight to the system's behavior and to the flow pathways of the PCDD/Fs. We calibrate our models against observed body concentrations of PCDD/Fs in the food web of Frierfjorden. Differences between the predictions from the two models (using the same forcing and parameter values) are of the same magnitude as their individual deviations from observations, and the models can be said to perform about equally well in our case. Sensitivity analysis indicates that the success or failure of the models in predicting the PCDD/F concentrations in the food web organisms highly depends on the adequate estimation of the truly dissolved concentrations in water and sediment pore water. We discuss the pros and cons of such models in understanding and estimating the present and future concentrations and bioaccumulation of persistent organic pollutants in aquatic food webs.
Directory of Open Access Journals (Sweden)
Dugwon Seo
2010-05-01
Full Text Available Sensitivity analysis is critically needed to better understand the microwave emission model for soil moisture retrieval using passive microwave remote sensing data. The vegetation b-factor along with vegetation water content and surface characteristics has significant impact in model prediction. This study evaluates the sensitivity of the b-factor, which is function of vegetation type. The analysis is carried out using Passive and Active L and S-band airborne sensor (PALS and measured field soil moisture from Southern Great Plains experiment (SGP99. The results show that the relative sensitivity of the b-factor is 86% in wet soil condition and 88% in high vegetated condition compared to the sensitivity of the soil moisture. Apparently, the b-factor is found to be more sensitive than the vegetation water content, surface roughness and surface temperature; therefore, the effect of the b-factor is fairly large to the microwave emission in certain conditions. Understanding the dependence of the b-factor on the soil and vegetation is important in studying the soil moisture retrieval algorithm, which can lead to potential improvements in model development for the Soil Moisture Active-Passive (SMAP mission.
Sensitivity Analysis of Viscoelastic Structures
Directory of Open Access Journals (Sweden)
A.M.G. de Lima
2006-01-01
Full Text Available In the context of control of sound and vibration of mechanical systems, the use of viscoelastic materials has been regarded as a convenient strategy in many types of industrial applications. Numerical models based on finite element discretization have been frequently used in the analysis and design of complex structural systems incorporating viscoelastic materials. Such models must account for the typical dependence of the viscoelastic characteristics on operational and environmental parameters, such as frequency and temperature. In many applications, including optimal design and model updating, sensitivity analysis based on numerical models is a very usefull tool. In this paper, the formulation of first-order sensitivity analysis of complex frequency response functions is developed for plates treated with passive constraining damping layers, considering geometrical characteristics, such as the thicknesses of the multi-layer components, as design variables. Also, the sensitivity of the frequency response functions with respect to temperature is introduced. As an example, response derivatives are calculated for a three-layer sandwich plate and the results obtained are compared with first-order finite-difference approximations.
DEFF Research Database (Denmark)
Montes, Frederico C. C.; Gernaey, Krist; Sin, Gürkan
2018-01-01
A dynamic plantwide model was developed for the synthesis of the Active pharmaceutical Ingredient (API) ibuprofen, following the Hoescht synthesis process. The kinetic parameters, reagents, products and by-products of the different reactions were adapted from literature, and the different process...... operations integrated until the end process, crystallization and isolation of the ibuprofen crystals. The dynamic model simulations were validated against available measurements from literature and then used as enabling tool to analyze the robustness of design space. To this end, sensitivity of the design...... space towards input disturbances and process uncertainties (from physical and model parameters) is studied using Monte Carlo simulations. The results quantify the uncertainty of the quality of product attributes, with particular focus on crystal size distribution and ibuprofen crystalized. The ranking...
Energy Technology Data Exchange (ETDEWEB)
Ostafew, C. [Azure Dynamics Corp., Toronto, ON (Canada)
2010-07-01
This presentation included a sensitivity analysis of electric vehicle components on overall efficiency. The presentation provided an overview of drive cycles and discussed the major contributors to range in terms of rolling resistance; aerodynamic drag; motor efficiency; and vehicle mass. Drive cycles that were presented included: New York City Cycle (NYCC); urban dynamometer drive cycle; and US06. A summary of the findings were presented for each of the major contributors. Rolling resistance was found to have a balanced effect on each drive cycle and proportional to range. In terms of aerodynamic drive, there was a large effect on US06 range. A large effect was also found on NYCC range in terms of motor efficiency and vehicle mass. figs.
Sensitivity analysis in remote sensing
Ustinov, Eugene A
2015-01-01
This book contains a detailed presentation of general principles of sensitivity analysis as well as their applications to sample cases of remote sensing experiments. An emphasis is made on applications of adjoint problems, because they are more efficient in many practical cases, although their formulation may seem counterintuitive to a beginner. Special attention is paid to forward problems based on higher-order partial differential equations, where a novel matrix operator approach to formulation of corresponding adjoint problems is presented. Sensitivity analysis (SA) serves for quantitative models of physical objects the same purpose, as differential calculus does for functions. SA provides derivatives of model output parameters (observables) with respect to input parameters. In remote sensing SA provides computer-efficient means to compute the jacobians, matrices of partial derivatives of observables with respect to the geophysical parameters of interest. The jacobians are used to solve corresponding inver...
Sensitivity analysis of the noise-induced oscillatory multistability in Higgins model of glycolysis
Ryashko, Lev
2018-03-01
A phenomenon of the noise-induced oscillatory multistability in glycolysis is studied. As a basic deterministic skeleton, we consider the two-dimensional Higgins model. The noise-induced generation of mixed-mode stochastic oscillations is studied in various parametric zones. Probabilistic mechanisms of the stochastic excitability of equilibria and noise-induced splitting of randomly forced cycles are analysed by the stochastic sensitivity function technique. A parametric zone of supersensitive Canard-type cycles is localized and studied in detail. It is shown that the generation of mixed-mode stochastic oscillations is accompanied by the noise-induced transitions from order to chaos.
Sensitivity analysis of the boundary layer height on idealised cities (model study)
Energy Technology Data Exchange (ETDEWEB)
Schayes, G. [Univ. of Louvain, Louvain-la-Neuve (Belgium); Grossi, P. [Joint Research Center, Ispra (Italy)
1997-10-01
The behaviour of the typical diurnal variation of the atmospheric boundary layer (ABL) over cities is a complex function of very numerous environmental parameters. Two types of geographical situations have been retained: (i) inland city only surrounded by uniform fields, (ii) coastal city, thus influenced by the sea/land breeze effect. We have used the three-dimensional Thermal Vorticity-mode Mesoscale (TVM) model developed jointly by the UCL (Belgium) and JRC (Italy). In this study it has been used in 2-D mode allowing to perform many sensitivity runs. This implies that a kind of infinitely wide city has been effectively stimulated, but this does not affect the conclusions for the ABL height. The sensibility study has been performed for two turbulence closure schemes, for various assumptions for the ABL height definition in the model, and for a selected parameter, the soil water content. (LN)
Sensitivity Analysis on Fire Modeling of Main Control Board Fire Using Fire Dynamics Simulator
International Nuclear Information System (INIS)
Kang, Dae Il; Lim, Ho Gon
2015-01-01
In this study, sensitivity analyses for an MCB fire were performed to identify the effects on the MCR forced abandonment time according to the changes of height and number for fire initiation places. Hanul Unit 3 NPP was selected as a reference plant for this study. In this study, sensitivity analyses for an MCB fire were performed to identify the effects on the MCR forced abandonment time according to the changes of height and number of fire initiation places. A main control board (MCB) fire can cause a forced main control room (MCR) abandonment of the operators as well as the function failures or spurious operations of the control and instrumentation-related components. If the MCR cannot be habitable, a safe shutdown from outside the MCR can be achieved and maintained at an alternate shutdown panel independent from the MCR. When the fire modeling for an electrical cabinet such as an MCB was performed, its many input parameters can affect the fire simulation results. This study results showed that the decrease in the height of fire ignition place and the use of single fire ignition place in fire modeling for the propagating fire shortened MCR abandonment time
Sensitivity analysis with the regional climate model COSMO-CLM over the CORDEX-MENA domain
Bucchignani, E.; Cattaneo, L.; Panitz, H.-J.; Mercogliano, P.
2016-02-01
The results of a sensitivity work based on ERA-Interim driven COSMO-CLM simulations over the Middle East-North Africa (CORDEX-MENA) domain are presented. All simulations were performed at 0.44° spatial resolution. The purpose of this study was to ascertain model performances with respect to changes in physical and tuning parameters which are mainly related to surface, convection, radiation and cloud parameterizations. Evaluation was performed for the whole CORDEX-MENA region and six sub-regions, comparing a set of 26 COSMO-CLM runs against a combination of available ground observations, satellite products and reanalysis data to assess temperature, precipitation, cloud cover and mean sea level pressure. The model proved to be very sensitive to changes in physical parameters. The optimized configuration allows COSMO-CLM to improve the simulated main climate features of this area. Its main characteristics consist in the new parameterization of albedo, based on Moderate Resolution Imaging Spectroradiometer data, and the new parameterization of aerosol, based on NASA-GISS AOD distributions. When applying this configuration, Mean Absolute Error values for the considered variables are as follows: about 1.2 °C for temperature, about 15 mm/month for precipitation, about 9 % for total cloud cover, and about 0.6 hPa for mean sea level pressure.
Sensitivity Analysis on Fire Modeling of Main Control Board Fire Using Fire Dynamics Simulator
Energy Technology Data Exchange (ETDEWEB)
Kang, Dae Il; Lim, Ho Gon [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)
2015-10-15
In this study, sensitivity analyses for an MCB fire were performed to identify the effects on the MCR forced abandonment time according to the changes of height and number for fire initiation places. Hanul Unit 3 NPP was selected as a reference plant for this study. In this study, sensitivity analyses for an MCB fire were performed to identify the effects on the MCR forced abandonment time according to the changes of height and number of fire initiation places. A main control board (MCB) fire can cause a forced main control room (MCR) abandonment of the operators as well as the function failures or spurious operations of the control and instrumentation-related components. If the MCR cannot be habitable, a safe shutdown from outside the MCR can be achieved and maintained at an alternate shutdown panel independent from the MCR. When the fire modeling for an electrical cabinet such as an MCB was performed, its many input parameters can affect the fire simulation results. This study results showed that the decrease in the height of fire ignition place and the use of single fire ignition place in fire modeling for the propagating fire shortened MCR abandonment time.
Kulasiri, Don; Liang, Jingyi; He, Yao; Samarasinghe, Sandhya
2017-04-21
We investigate the epistemic uncertainties of parameters of a mathematical model that describes the dynamics of CaMKII-NMDAR complex related to memory formation in synapses using global sensitivity analysis (GSA). The model, which was published in this journal, is nonlinear and complex with Ca 2+ patterns with different level of frequencies as inputs. We explore the effects of parameter on the key outputs of the model to discover the most sensitive ones using GSA and partial ranking correlation coefficient (PRCC) and to understand why they are sensitive and others are not based on the biology of the problem. We also extend the model to add presynaptic neurotransmitter vesicles release to have action potentials as inputs of different frequencies. We perform GSA on this extended model to show that the parameter sensitivities are different for the extended model as shown by PRCC landscapes. Based on the results of GSA and PRCC, we reduce the original model to a less complex model taking the most important biological processes into account. We validate the reduced model against the outputs of the original model. We show that the parameter sensitivities are dependent on the inputs and GSA would make us understand the sensitivities and the importance of the parameters. A thorough phenomenological understanding of the relationships involved is essential to interpret the results of GSA and hence for the possible model reduction. Copyright © 2017 Elsevier Ltd. All rights reserved.
Energy Technology Data Exchange (ETDEWEB)
Cacuci, D. G. [Commiss Energy Atom, Direct Energy Nucl, Saclay, (France); Cacuci, D. G.; Balan, I. [Univ Karlsruhe, Inst Nucl Technol and Reactor Safetly, Karlsruhe, (Germany); Ionescu-Bujor, M. [Forschungszentrum Karlsruhe, Fus Program, D-76021 Karlsruhe, (Germany)
2008-07-01
In Part II of this work, the adjoint sensitivity analysis procedure developed in Part I is applied to perform sensitivity analysis of several dynamic reliability models of systems of increasing complexity, culminating with the consideration of the International Fusion Materials Irradiation Facility (IFMIF) accelerator system. Section II presents the main steps of a procedure for the automated generation of Markov chains for reliability analysis, including the abstraction of the physical system, construction of the Markov chain, and the generation and solution of the ensuing set of differential equations; all of these steps have been implemented in a stand-alone computer code system called QUEFT/MARKOMAG-S/MCADJSEN. This code system has been applied to sensitivity analysis of dynamic reliability measures for a paradigm '2-out-of-3' system comprising five components and also to a comprehensive dynamic reliability analysis of the IFMIF accelerator system facilities for the average availability and, respectively, the system's availability at the final mission time. The QUEFT/MARKOMAG-S/MCADJSEN has been used to efficiently compute sensitivities to 186 failure and repair rates characterizing components and subsystems of the first-level fault tree of the IFMIF accelerator system. (authors)
International Nuclear Information System (INIS)
Cacuci, D. G.; Cacuci, D. G.; Balan, I.; Ionescu-Bujor, M.
2008-01-01
In Part II of this work, the adjoint sensitivity analysis procedure developed in Part I is applied to perform sensitivity analysis of several dynamic reliability models of systems of increasing complexity, culminating with the consideration of the International Fusion Materials Irradiation Facility (IFMIF) accelerator system. Section II presents the main steps of a procedure for the automated generation of Markov chains for reliability analysis, including the abstraction of the physical system, construction of the Markov chain, and the generation and solution of the ensuing set of differential equations; all of these steps have been implemented in a stand-alone computer code system called QUEFT/MARKOMAG-S/MCADJSEN. This code system has been applied to sensitivity analysis of dynamic reliability measures for a paradigm '2-out-of-3' system comprising five components and also to a comprehensive dynamic reliability analysis of the IFMIF accelerator system facilities for the average availability and, respectively, the system's availability at the final mission time. The QUEFT/MARKOMAG-S/MCADJSEN has been used to efficiently compute sensitivities to 186 failure and repair rates characterizing components and subsystems of the first-level fault tree of the IFMIF accelerator system. (authors)
International Nuclear Information System (INIS)
Su Rui; Wang Ju; Chen Weiming; Zong Zihua; Zhao Honggang
2008-01-01
CRP-GEORC concept model is an artificial system of geological disposal for High-Level radioactive waste. Sensitivity analysis and uncertainties simulation of the migration of radionuclide Se-79 and I-129 in the far field of this system by using GoldSim Code have been conducted. It can be seen from the simulation results that variables used to describe the geological features and characterization of groundwater flow are sensitive variables of whole geological disposal system. The uncertainties of parameters have remarkable influence on the simulation results. (authors)
Stochastic sensitivity analysis of the biosphere model for Canadian nuclear fuel waste management
International Nuclear Information System (INIS)
Reid, J.A.K.; Corbett, B.J.
1993-01-01
The biosphere model, BIOTRAC, was constructed to assess Canada's concept for nuclear fuel waste disposal in a vault deep in crystalline rock at some as yet undetermined location in the Canadian Shield. The model is therefore very general and based on the shield as a whole. BIOTRAC is made up of four linked submodels for surface water, soil, atmosphere, and food chain and dose. The model simulates physical conditions and radionuclide flows from the discharge of a hypothetical nuclear fuel waste disposal vault through groundwater, a well, a lake, air, soil, and plants to a critical group of individuals, i.e., those who are most exposed and therefore receive the highest dose. This critical group is totally self-sufficient and is represented by the International Commission for Radiological Protection reference man for dose prediction. BIOTRAC is a dynamic model that assumes steady-state physical conditions for each simulation, and deals with variation and uncertainty through Monte Carlo simulation techniques. This paper describes SENSYV, a technique for analyzing pathway and parameter sensitivities for the BIOTRAC code run in stochastic mode. Results are presented for 129 I from the disposal of used fuel, and they confirm the importance of doses via the soil/plant/man and the air/plant/man ingestion pathways. The results also indicate that the lake/well water use switch, the aquatic iodine mass loading parameter, the iodine soil evasion rate, and the iodine plant/soil concentration ratio are important parameters
Sensitivity Analysis of Multiple Informant Models When Data Are Not Missing at Random
Blozis, Shelley A.; Ge, Xiaojia; Xu, Shu; Natsuaki, Misaki N.; Shaw, Daniel S.; Neiderhiser, Jenae M.; Scaramella, Laura V.; Leve, Leslie D.; Reiss, David
2013-01-01
Missing data are common in studies that rely on multiple informant data to evaluate relationships among variables for distinguishable individuals clustered within groups. Estimation of structural equation models using raw data allows for incomplete data, and so all groups can be retained for analysis even if only 1 member of a group contributes…
Noacco, V.; Wagener, T.; Pianosi, F.; Philp, T.
2017-12-01
Insurance companies provide insurance against a wide range of threats, such as natural catastrophes, nuclear incidents and terrorism. To quantify risk and support investment decisions, mathematical models are used, for example to set the premiums charged to clients that protect from financial loss, should deleterious events occur. While these models are essential tools for adequately assessing the risk attached to an insurer's portfolio, their development is costly and their value for decision-making may be limited by an incomplete understanding of uncertainty and sensitivity. Aside from the business need to understand risk and uncertainty, the insurance sector also faces regulation which requires them to test their models in such a way that uncertainties are appropriately captured and that plans are in place to assess the risks and their mitigation. The building and testing of models constitutes a high cost for insurance companies, and it is a time intensive activity. This study uses an established global sensitivity analysis toolbox (SAFE) to more efficiently capture the uncertainties and sensitivities embedded in models used by a leading re/insurance firm, with structured approaches to validate these models and test the impact of assumptions on the model predictions. It is hoped that this in turn will lead to better-informed and more robust business decisions.
Dynamic modeling and sensitivity analysis of solar thermal energy conversion systems
Hamilton, C. L.
1977-01-01
Since the energy input to solar thermal conversion systems is both time variant and probabilistic, it is unlikely that simple steady-state methods for estimating lifetime performance will provide satisfactory results. The work described here uses dynamic modeling to begin identifying what must be known about input radiation and system dynamic characteristics to estimate performance reliably. Daily operation of two conceptual solar energy systems was simulated under varying operating strategies with time-dependent radiation intensity ranging from smooth input of several magnitudes to input of constant total energy whose intensity oscillated with periods from 1/4 hour to 6 hours. Integrated daily system output and efficiency were functions of both level and dynamic characteristics of insolation. Sensitivity of output to changes in total input was greater than one.
Sánchez-Canales, M; López-Benito, A; Acuña, V; Ziv, G; Hamel, P; Chaplin-Kramer, R; Elorza, F J
2015-01-01
Climate change and land-use change are major factors influencing sediment dynamics. Models can be used to better understand sediment production and retention by the landscape, although their interpretation is limited by large uncertainties, including model parameter uncertainties. The uncertainties related to parameter selection may be significant and need to be quantified to improve model interpretation for watershed management. In this study, we performed a sensitivity analysis of the InVEST (Integrated Valuation of Environmental Services and Tradeoffs) sediment retention model in order to determine which model parameters had the greatest influence on model outputs, and therefore require special attention during calibration. The estimation of the sediment loads in this model is based on the Universal Soil Loss Equation (USLE). The sensitivity analysis was performed in the Llobregat basin (NE Iberian Peninsula) for exported and retained sediment, which support two different ecosystem service benefits (avoided reservoir sedimentation and improved water quality). Our analysis identified the model parameters related to the natural environment as the most influential for sediment export and retention. Accordingly, small changes in variables such as the magnitude and frequency of extreme rainfall events could cause major changes in sediment dynamics, demonstrating the sensitivity of these dynamics to climate change in Mediterranean basins. Parameters directly related to human activities and decisions (such as cover management factor, C) were also influential, especially for sediment exported. The importance of these human-related parameters in the sediment export process suggests that mitigation measures have the potential to at least partially ameliorate climate-change driven changes in sediment exportation. Copyright © 2014 Elsevier B.V. All rights reserved.
Marsudi, Hidayat, Noor; Wibowo, Ratno Bagus Edy
2017-12-01
In this article, we present a deterministic model for the transmission dynamics of HIV/AIDS in which condom campaign and antiretroviral therapy are both important for the disease management. We calculate the effective reproduction number using the next generation matrix method and investigate the local and global stability of the disease-free equilibrium of the model. Sensitivity analysis of the effective reproduction number with respect to the model parameters were carried out. Our result shows that efficacy rate of condom campaign, transmission rate for contact with the asymptomatic infective, progression rate from the asymptomatic infective to the pre-AIDS infective, transmission rate for contact with the pre-AIDS infective, ARV therapy rate, proportion of the susceptible receiving condom campaign and proportion of the pre-AIDS receiving ARV therapy are highly sensitive parameters that effect the transmission dynamics of HIV/AIDS infection.
Sensitivity Analysis of Wavelet Neural Network Model for Short-Term Traffic Volume Prediction
Directory of Open Access Journals (Sweden)
Jinxing Shen
2013-01-01
Full Text Available In order to achieve a more accurate and robust traffic volume prediction model, the sensitivity of wavelet neural network model (WNNM is analyzed in this study. Based on real loop detector data which is provided by traffic police detachment of Maanshan, WNNM is discussed with different numbers of input neurons, different number of hidden neurons, and traffic volume for different time intervals. The test results show that the performance of WNNM depends heavily on network parameters and time interval of traffic volume. In addition, the WNNM with 4 input neurons and 6 hidden neurons is the optimal predictor with more accuracy, stability, and adaptability. At the same time, a much better prediction record will be achieved with the time interval of traffic volume are 15 minutes. In addition, the optimized WNNM is compared with the widely used back-propagation neural network (BPNN. The comparison results indicated that WNNM produce much lower values of MAE, MAPE, and VAPE than BPNN, which proves that WNNM performs better on short-term traffic volume prediction.
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
With the fast growth of Chinese economic,more and more capital will be invested in environmental projects.How to select the environmental investment projects(alternatives)for obtaining the best environmental quality and economic benefits is an important problem for the decision makers.The purpose of this paper is to develop a decision-making model to rank a finite number of alternatives with several and sometimes conflicting criteria.A model for ranking the projects of municipal sewage treatment plants is proposed by using exports' information and the data of the real projects.And,the ranking result is given based on the PROMETHEE method. Furthermore,by means of the concept of the weight stability intervals(WSI),the sensitivity of the ranking results to the size of criteria values and the change of weights value of criteria are discussed.The result shows that some criteria,such as"proportion of benefit to projoct cost",will influence the ranking result of alternatives very strong while others not.The influence are not only from the value of criterion but also from the changing the weight of criterion.So,some criteria such as"proportion of benefit to projoct cost" are key critera for ranking the projects. Decision makers must be cautious to them.
A Sensitivity Analysis of a Computer Model-Based Leak Detection System for Oil Pipelines
Directory of Open Access Journals (Sweden)
Zhe Lu
2017-08-01
Full Text Available Improving leak detection capability to eliminate undetected releases is an area of focus for the energy pipeline industry, and the pipeline companies are working to improve existing methods for monitoring their pipelines. Computer model-based leak detection methods that detect leaks by analyzing the pipeline hydraulic state have been widely employed in the industry, but their effectiveness in practical applications is often challenged by real-world uncertainties. This study quantitatively assessed the effects of uncertainties on leak detectability of a commonly used real-time transient model-based leak detection system. Uncertainties in fluid properties, field sensors, and the data acquisition system were evaluated. Errors were introduced into the input variables of the leak detection system individually and collectively, and the changes in leak detectability caused by the uncertainties were quantified using simulated leaks. This study provides valuable quantitative results contributing towards a better understanding of how real-world uncertainties affect leak detection. A general ranking of the importance of the uncertainty sources was obtained: from high to low it is time skew, bulk modulus error, viscosity error, and polling time. It was also shown that inertia-dominated pipeline systems were less sensitive to uncertainties compared to friction-dominated systems.
Energy Technology Data Exchange (ETDEWEB)
Petelet, M
2008-07-01
Current approach of most welding modellers is to content themselves with available material data, and to chose a mechanical model that seems to be appropriate. Among inputs, those controlling the material properties are one of the key problems of welding simulation: material data are never characterized over a sufficiently wide temperature range. This way to proceed neglect the influence of the uncertainty of input data on the result given by the computer code. In this case, how to assess the credibility of prediction? This thesis represents a step in the direction of implementing an innovative approach in welding simulation in order to bring answers to this question, with an illustration on some concretes welding cases.The global sensitivity analysis is chosen to determine which material properties are the most sensitive in a numerical welding simulation and in which range of temperature. Using this methodology require some developments to sample and explore the input space covering welding of different steel materials. Finally, input data have been divided in two groups according to their influence on the output of the model (residual stress or distortion). In this work, complete methodology of the global sensitivity analysis has been successfully applied to welding simulation and lead to reduce the input space to the only important variables. Sensitivity analysis has provided answers to what can be considered as one of the probable frequently asked questions regarding welding simulation: for a given material which properties must be measured with a good accuracy and which ones can be simply extrapolated or taken from a similar material? (author)
Energy Technology Data Exchange (ETDEWEB)
Petelet, M
2007-10-15
Current approach of most welding modellers is to content themselves with available material data, and to chose a mechanical model that seems to be appropriate. Among inputs, those controlling the material properties are one of the key problems of welding simulation: material data are never characterized over a sufficiently wide temperature range {exclamation_point} This way to proceed neglect the influence of the uncertainty of input data on the result given by the computer code. In this case, how to assess the credibility of prediction? This thesis represents a step in the direction of implementing an innovative approach in welding simulation in order to bring answers to this question, with an illustration on some concretes welding cases. The global sensitivity analysis is chosen to determine which material properties are the most sensitive in a numerical welding simulation and in which range of temperature. Using this methodology require some developments to sample and explore the input space covering welding of different steel materials. Finally, input data have been divided in two groups according to their influence on the output of the model (residual stress or distortion). In this work, complete methodology of the global sensitivity analysis has been successfully applied to welding simulation and lead to reduce the input space to the only important variables. Sensitivity analysis has provided answers to what can be considered as one of the probable frequently asked questions regarding welding simulation: for a given material which properties must be measured with a good accuracy and which ones can be simply extrapolated or taken from a similar material? (author)
Propagation of uncertainty and sensitivity analysis in an integral oil-gas plume model
Wang, Shitao
2016-05-27
Polynomial Chaos expansions are used to analyze uncertainties in an integral oil-gas plume model simulating the Deepwater Horizon oil spill. The study focuses on six uncertain input parameters—two entrainment parameters, the gas to oil ratio, two parameters associated with the droplet-size distribution, and the flow rate—that impact the model\\'s estimates of the plume\\'s trap and peel heights, and of its various gas fluxes. The ranges of the uncertain inputs were determined by experimental data. Ensemble calculations were performed to construct polynomial chaos-based surrogates that describe the variations in the outputs due to variations in the uncertain inputs. The surrogates were then used to estimate reliably the statistics of the model outputs, and to perform an analysis of variance. Two experiments were performed to study the impacts of high and low flow rate uncertainties. The analysis shows that in the former case the flow rate is the largest contributor to output uncertainties, whereas in the latter case, with the uncertainty range constrained by aposteriori analyses, the flow rate\\'s contribution becomes negligible. The trap and peel heights uncertainties are then mainly due to uncertainties in the 95% percentile of the droplet size and in the entrainment parameters.
Probabilistic sensitivity analysis of biochemical reaction systems.
Zhang, Hong-Xuan; Dempsey, William P; Goutsias, John
2009-09-07
Sensitivity analysis is an indispensable tool for studying the robustness and fragility properties of biochemical reaction systems as well as for designing optimal approaches for selective perturbation and intervention. Deterministic sensitivity analysis techniques, using derivatives of the system response, have been extensively used in the literature. However, these techniques suffer from several drawbacks, which must be carefully considered before using them in problems of systems biology. We develop here a probabilistic approach to sensitivity analysis of biochemical reaction systems. The proposed technique employs a biophysically derived model for parameter fluctuations and, by using a recently suggested variance-based approach to sensitivity analysis [Saltelli et al., Chem. Rev. (Washington, D.C.) 105, 2811 (2005)], it leads to a powerful sensitivity analysis methodology for biochemical reaction systems. The approach presented in this paper addresses many problems associated with derivative-based sensitivity analysis techniques. Most importantly, it produces thermodynamically consistent sensitivity analysis results, can easily accommodate appreciable parameter variations, and allows for systematic investigation of high-order interaction effects. By employing a computational model of the mitogen-activated protein kinase signaling cascade, we demonstrate that our approach is well suited for sensitivity analysis of biochemical reaction systems and can produce a wealth of information about the sensitivity properties of such systems. The price to be paid, however, is a substantial increase in computational complexity over derivative-based techniques, which must be effectively addressed in order to make the proposed approach to sensitivity analysis more practical.
Directory of Open Access Journals (Sweden)
Silvério Rosa
2018-02-01
Full Text Available A state wide Human Respiratory Syncytial Virus (HRSV surveillance system was implemented in Florida in 1999 to support clinical decision-making for prophylaxis of premature infants. The research presented in this paper addresses the problem of fitting real data collected by the Florida HRSV surveillance system by using a periodic SEIRS mathematical model. A sensitivity and cost-effectiveness analysis of the model is done and an optimal control problem is formulated and solved with treatment as the control variable.
Propagation of uncertainty and sensitivity analysis in an integral oil-gas plume model
Wang, Shitao; Iskandarani, Mohamed; Srinivasan, Ashwanth; Thacker, W. Carlisle; Winokur, Justin; Knio, Omar
2016-01-01
Polynomial Chaos expansions are used to analyze uncertainties in an integral oil-gas plume model simulating the Deepwater Horizon oil spill. The study focuses on six uncertain input parameters—two entrainment parameters, the gas to oil ratio, two parameters associated with the droplet-size distribution, and the flow rate—that impact the model's estimates of the plume's trap and peel heights, and of its various gas fluxes. The ranges of the uncertain inputs were determined by experimental data. Ensemble calculations were performed to construct polynomial chaos-based surrogates that describe the variations in the outputs due to variations in the uncertain inputs. The surrogates were then used to estimate reliably the statistics of the model outputs, and to perform an analysis of variance. Two experiments were performed to study the impacts of high and low flow rate uncertainties. The analysis shows that in the former case the flow rate is the largest contributor to output uncertainties, whereas in the latter case, with the uncertainty range constrained by aposteriori analyses, the flow rate's contribution becomes negligible. The trap and peel heights uncertainties are then mainly due to uncertainties in the 95% percentile of the droplet size and in the entrainment parameters.
Gazzarri, J. I.; Kesler, O.
In the first part of this two-paper series, we presented a numerical model of the impedance behaviour of a solid oxide fuel cell (SOFC) aimed at simulating the change in the impedance spectrum induced by contact degradation at the interconnect-electrode, and at the electrode-electrolyte interfaces. The purpose of that investigation was to develop a non-invasive diagnostic technique to identify degradation modes in situ. In the present paper, we appraise the predictive capabilities of the proposed method in terms of its robustness to uncertainties in the input parameters, many of which are very difficult to measure independently. We applied this technique to the degradation modes simulated in Part I, in addition to anode sulfur poisoning. Electrode delamination showed the highest robustness to input parameter variations, followed by interconnect oxidation and interconnect detachment. The most sensitive degradation mode was sulfur poisoning, due to strong parameter interactions. In addition, we simulate several simultaneous two-degradation-mode scenarios, assessing the method's capabilities and limitations for the prediction of electrochemical behaviour of SOFC's undergoing multiple simultaneous degradation modes.
Energy Technology Data Exchange (ETDEWEB)
Gazzarri, J.I. [Department of Mechanical Engineering, University of British Columbia, 2054-6250 Applied Science Lane, Vancouver, BC V6T 1Z4 (Canada); Kesler, O. [Department of Mechanical and Industrial Engineering, University of Toronto, 5 King' s College Road, Toronto, ON M5S 3G8 (Canada)
2008-01-21
In the first part of this two-paper series, we presented a numerical model of the impedance behaviour of a solid oxide fuel cell (SOFC) aimed at simulating the change in the impedance spectrum induced by contact degradation at the interconnect-electrode, and at the electrode-electrolyte interfaces. The purpose of that investigation was to develop a non-invasive diagnostic technique to identify degradation modes in situ. In the present paper, we appraise the predictive capabilities of the proposed method in terms of its robustness to uncertainties in the input parameters, many of which are very difficult to measure independently. We applied this technique to the degradation modes simulated in Part I, in addition to anode sulfur poisoning. Electrode delamination showed the highest robustness to input parameter variations, followed by interconnect oxidation and interconnect detachment. The most sensitive degradation mode was sulfur poisoning, due to strong parameter interactions. In addition, we simulate several simultaneous two-degradation-mode scenarios, assessing the method's capabilities and limitations for the prediction of electrochemical behaviour of SOFC's undergoing multiple simultaneous degradation modes. (author)
Sensitivity and uncertainty in flood inundation modelling – concept of an analysis framework
Directory of Open Access Journals (Sweden)
T. Weichel
2007-01-01
Full Text Available After the extreme flood event of the Elbe in 2002 the definition of flood risk areas by law and their simulation became more important in Germany. This paper describes a concept of an analysis framework to improve the localisation and duration of validity of flood inundation maps. The two-dimensional finite difference model TrimR2D is used and linked to a Monte-Carlo routine for parameter sampling as well as to selected performance measures. The purpose is the investigation of the impact of different spatial resolutions and the influence of changing land uses in the simulation of flood inundation areas. The technical assembling of the framework is realised and beside the model calibration, first tests with different parameter ranges were done. Preliminary results show good correlations with observed data, but the investigation of shifting land uses reflects only poor changes in the flood extension.
Directory of Open Access Journals (Sweden)
Robert B. Gramacy
2010-02-01
Full Text Available This document describes the new features in version 2.x of the tgp package for R, implementing treed Gaussian process (GP models. The topics covered include methods for dealing with categorical inputs and excluding inputs from the tree or GP part of the model; fully Bayesian sensitivity analysis for inputs/covariates; sequential optimization of black-box functions; and a new Monte Carlo method for inference in multi-modal posterior distributions that combines simulated tempering and importance sampling. These additions extend the functionality of tgp across all models in the hierarchy: from Bayesian linear models, to classification and regression trees (CART, to treed Gaussian processes with jumps to the limiting linear model. It is assumed that the reader is familiar with the baseline functionality of the package, outlined in the first vignette (Gramacy 2007.
Directory of Open Access Journals (Sweden)
Yan Gao
2014-01-01
Full Text Available The increasing marine activities in Arctic area have brought growing interest in ship-iceberg collision study. The purpose of this paper is to study the iceberg geometry shape effect on the collision process. In order to estimate the sensitivity parameter, five different geometry iceberg models and two iceberg material models are adopted in the analysis. The FEM numerical simulation is used to predict the scenario and the related responses. The simulation results including energy dissipation and impact force are investigated and compared. It is shown that the collision process and energy dissipation are more sensitive to iceberg local shape than other factors when the elastic-plastic iceberg material model is applied. The blunt iceberg models act rigidly while the sharp ones crush easily during the simulation process. With respect to the crushable foam iceberg material model, the iceberg geometry has relatively small influence on the collision process. The spherical iceberg model shows the most rigidity for both iceberg material models and should be paid the most attention for ice-resist design for ships.
Sensitivity analysis of a PWR pressurizer
International Nuclear Information System (INIS)
Bruel, Renata Nunes
1997-01-01
A sensitivity analysis relative to the parameters and modelling of the physical process in a PWR pressurizer has been performed. The sensitivity analysis was developed by implementing the key parameters and theoretical model lings which generated a comprehensive matrix of influences of each changes analysed. The major influences that have been observed were the flashing phenomenon and the steam condensation on the spray drops. The present analysis is also applicable to the several theoretical and experimental areas. (author)
Structural development and web service based sensitivity analysis of the Biome-BGC MuSo model
Hidy, Dóra; Balogh, János; Churkina, Galina; Haszpra, László; Horváth, Ferenc; Ittzés, Péter; Ittzés, Dóra; Ma, Shaoxiu; Nagy, Zoltán; Pintér, Krisztina; Barcza, Zoltán
2014-05-01
-BGC with multi-soil layer). Within the frame of the BioVeL project (http://www.biovel.eu) an open source and domain independent scientific workflow management system (http://www.taverna.org.uk) are used to support 'in silico' experimentation and easy applicability of different models including Biome-BGC MuSo. Workflows can be built upon functionally linked sets of web services like retrieval of meteorological dataset and other parameters; preparation of single run or spatial run model simulation; desk top grid technology based Monte Carlo experiment with parallel processing; model sensitivity analysis, etc. The newly developed, Monte Carlo experiment based sensitivity analysis is described in this study and results are presented about differences in the sensitivity of the original and the developed Biome-BGC model.
Directory of Open Access Journals (Sweden)
Daniele Cavalli
2016-09-01
Full Text Available Two features distinguishing soil organic matter simulation models are the type of kinetics used to calculate pool decomposition rates, and the algorithm used to handle the effects of nitrogen (N shortage on carbon (C decomposition. Compared to widely used first-order kinetics, Monod kinetics more realistically represent organic matter decomposition, because they relate decomposition to both substrate and decomposer size. Most models impose a fixed C to N ratio for microbial biomass. When N required by microbial biomass to decompose a given amount of substrate-C is larger than soil available N, carbon decomposition rates are limited proportionally to N deficit (N inhibition hypothesis. Alternatively, C-overflow was proposed as a way of getting rid of excess C, by allocating it to a storage pool of polysaccharides. We built six models to compare the combinations of three decomposition kinetics (first-order, Monod, and reverse Monod, and two ways to simulate the effect of N shortage on C decomposition (N inhibition and C-overflow. We conducted sensitivity analysis to identify model parameters that mostly affected CO2 emissions and soil mineral N during a simulated 189-day laboratory incubation assuming constant water content and temperature. We evaluated model outputs sensitivity at different stages of organic matter decomposition in a soil amended with three inputs of increasing C to N ratio: liquid manure, solid manure, and low-N crop residue. Only few model parameters and their interactions were responsible for consistent variations of CO2 and soil mineral N. These parameters were mostly related to microbial biomass and to the partitioning of applied C among input pools, as well as their decomposition constants. In addition, in models with Monod kinetics, CO2 was also sensitive to a variation of the half-saturation constants. C-overflow enhanced pool decomposition compared to N inhibition hypothesis when N shortage occurred. Accumulated C in the
Chen, Jiajia; Pitchai, Krishnamoorthy; Birla, Sohan; Negahban, Mehrdad; Jones, David; Subbiah, Jeyamkondan
2014-10-01
A 3-dimensional finite-element model coupling electromagnetics and heat and mass transfer was developed to understand the interactions between the microwaves and fresh mashed potato in a 500 mL tray. The model was validated by performing heating of mashed potato from 25 °C on a rotating turntable in a microwave oven, rated at 1200 W, for 3 min. The simulated spatial temperature profiles on the top and bottom layer of the mashed potato showed similar hot and cold spots when compared to the thermal images acquired by an infrared camera. Transient temperature profiles at 6 locations collected by fiber-optic sensors showed good agreement with predicted results, with the root mean square error ranging from 1.6 to 11.7 °C. The predicted total moisture loss matched well with the observed result. Several input parameters, such as the evaporation rate constant, the intrinsic permeability of water and gas, and the diffusion coefficient of water and gas, are not readily available for mashed potato, and they cannot be easily measured experimentally. Reported values for raw potato were used as baseline values. A sensitivity analysis of these input parameters on the temperature profiles and the total moisture loss was evaluated by changing the baseline values to their 10% and 1000%. The sensitivity analysis showed that the gas diffusion coefficient, intrinsic water permeability, and the evaporation rate constant greatly influenced the predicted temperature and total moisture loss, while the intrinsic gas permeability and the water diffusion coefficient had little influence. This model can be used by the food product developers to understand microwave heating of food products spatially and temporally. This tool will allow food product developers to design food package systems that would heat more uniformly in various microwave ovens. The sensitivity analysis of this study will help us determine the most significant parameters that need to be measured accurately for reliable
Zhang, Yuanhui; Wu, Haipeng; Denton, Brian T; Wilson, James R; Lobo, Jennifer M
2017-10-27
Markov models are commonly used for decision-making studies in many application domains; however, there are no widely adopted methods for performing sensitivity analysis on such models with uncertain transition probability matrices (TPMs). This article describes two simulation-based approaches for conducting probabilistic sensitivity analysis on a given discrete-time, finite-horizon, finite-state Markov model using TPMs that are sampled over a specified uncertainty set according to a relevant probability distribution. The first approach assumes no prior knowledge of the probability distribution, and each row of a TPM is independently sampled from the uniform distribution on the row's uncertainty set. The second approach involves random sampling from the (truncated) multivariate normal distribution of the TPM's maximum likelihood estimators for its rows subject to the condition that each row has nonnegative elements and sums to one. The two sampling methods are easily implemented and have reasonable computation times. A case study illustrates the application of these methods to a medical decision-making problem involving the evaluation of treatment guidelines for glycemic control of patients with type 2 diabetes, where natural variation in a patient's glycated hemoglobin (HbA1c) is modeled as a Markov chain, and the associated TPMs are subject to uncertainty.
Directory of Open Access Journals (Sweden)
Mahmoudi Hoda
2014-09-01
Full Text Available These instructions give you guidelines for preparing papers for IFAC conferences. A reverse supply chain is configured by a sequence of elements forming a continuous process to treat return-products until they are properly recovered or disposed. The activities in a reverse supply chain include collection, cleaning, disassembly, test and sorting, storage, transport, and recovery operations. This paper presents a mathematical programming model with the objective of minimizing the total costs of reverse supply chain including transportation, fixed opening, operation, maintenance and remanufacturing costs of centers. The proposed model considers the design of a multi-layer, multi-product reverse supply chain that consists of returning, disassembly, processing, recycling, remanufacturing, materials and distribution centers. This integer linear programming model is solved by using Lingo 9 software and the results are reported. Finally, a sensitivity analysis of the proposed model is also presented.
Directory of Open Access Journals (Sweden)
S. TATTARI
2008-12-01
Full Text Available Modeling tools are needed to assess (i the amounts of loading from agricultural sources to water bodies as well as (ii the alternative management options in varying climatic conditions. These days, the implementation of Water Framework Directive (WFD has put totally new requirements also for modeling approaches. The physically based models are commonly not operational and thus the usability of these models is restricted for a few selected catchments. But the rewarding feature of these process-based models is an option to study the effect of protection measures on a catchment scale and, up to a certain point, a possibility to upscale the results. In this study, the parameterization of the SWAT model was developed in terms of discharge dynamics and nutrient loads, and a sensitivity analysis regarding discharge and sediment concentration was made. The SWAT modeling exercise was carried out for a 2nd order catchment (Yläneenjoki, 233 km2 of the Eurajoki river basin in southwestern Finland. The Yläneenjoki catchment has been intensively monitored during the last 14 years. Hence, there was enough background information available for both parameter setup and calibration. In addition to load estimates, SWAT also offers possibility to assess the effects of various agricultural management actions like fertilization, tillage practices, choice of cultivated plants, buffer strips, sedimentation ponds and constructed wetlands (CWs on loading. Moreover, information on local agricultural practices and the implemented and planned protective measures were readily available thanks to aware farmers and active authorities. Here, we studied how CWs can reduce the nutrient load at the outlet of the Yläneenjoki river basin. The results suggested that sensitivity analysis and autocalibration tools incorporated in the model are useful by pointing out the most influential parameters, and that flow dynamics and annual loading values can be modeled with reasonable
Louka, Panagiota; Petropoulos, George; Papanikolaou, Ioannis
2015-04-01
The ability to map the spatiotemporal distribution of extreme climatic conditions, such as frost, is a significant tool in successful agricultural management and decision making. Nowadays, with the development of Earth Observation (EO) technology, it is possible to obtain accurately, timely and in a cost-effective way information on the spatiotemporal distribution of frost conditions, particularly over large and otherwise inaccessible areas. The present study aimed at developing and evaluating a frost risk prediction model, exploiting primarily EO data from MODIS and ASTER sensors and ancillary ground observation data. For the evaluation of our model, a region in north-western Greece was selected as test site and a detailed sensitivity analysis was implemented. The agreement between the model predictions and the observed (remotely sensed) frost frequency obtained by MODIS sensor was evaluated thoroughly. Also, detailed comparisons of the model predictions were performed against reference frost ground observations acquired from the Greek Agricultural Insurance Organization (ELGA) over a period of 10-years (2000-2010). Overall, results evidenced the ability of the model to produce reasonably well the frost conditions, following largely explainable patterns in respect to the study site and local weather conditions characteristics. Implementation of our proposed frost risk model is based primarily on satellite imagery analysis provided nowadays globally at no cost. It is also straightforward and computationally inexpensive, requiring much less effort in comparison for example to field surveying. Finally, the method is adjustable to be potentially integrated with other high resolution data available from both commercial and non-commercial vendors. Keywords: Sensitivity analysis, frost risk mapping, GIS, remote sensing, MODIS, Greece
Chen, Zhuowei; Shi, Liangsheng; Ye, Ming; Zhu, Yan; Yang, Jinzhong
2018-06-01
Nitrogen reactive transport modeling is subject to uncertainty in model parameters, structures, and scenarios. By using a new variance-based global sensitivity analysis method, this paper identifies important parameters for nitrogen reactive transport with simultaneous consideration of these three uncertainties. A combination of three scenarios of soil temperature and two scenarios of soil moisture creates a total of six scenarios. Four alternative models describing the effect of soil temperature and moisture content are used to evaluate the reduction functions used for calculating actual reaction rates. The results show that for nitrogen reactive transport problem, parameter importance varies substantially among different models and scenarios. Denitrification and nitrification process is sensitive to soil moisture content status rather than to the moisture function parameter. Nitrification process becomes more important at low moisture content and low temperature. However, the changing importance of nitrification activity with respect to temperature change highly relies on the selected model. Model-averaging is suggested to assess the nitrification (or denitrification) contribution by reducing the possible model error. Despite the introduction of biochemical heterogeneity or not, fairly consistent parameter importance rank is obtained in this study: optimal denitrification rate (Kden) is the most important parameter; reference temperature (Tr) is more important than temperature coefficient (Q10); empirical constant in moisture response function (m) is the least important one. Vertical distribution of soil moisture but not temperature plays predominant role controlling nitrogen reaction. This study provides insight into the nitrogen reactive transport modeling and demonstrates an effective strategy of selecting the important parameters when future temperature and soil moisture carry uncertainties or when modelers face with multiple ways of establishing nitrogen
Heusinkveld, Maarten H G; Quicken, Sjeng; Holtackers, Robert J; Huberts, Wouter; Reesink, Koen D; Delhaas, Tammo; Spronck, Bart
2018-02-01
Quantification of the uncertainty in constitutive model predictions describing arterial wall mechanics is vital towards non-invasive assessment of vascular drug therapies. Therefore, we perform uncertainty quantification to determine uncertainty in mechanical characteristics describing the vessel wall response upon loading. Furthermore, a global variance-based sensitivity analysis is performed to pinpoint measurements that are most rewarding to be measured more precisely. We used previously published carotid diameter-pressure and intima-media thickness (IMT) data (measured in triplicate), and Holzapfel-Gasser-Ogden models. A virtual data set containing 5000 diastolic and systolic diameter-pressure points, and IMT values was generated by adding measurement error to the average of the measured data. The model was fitted to single-exponential curves calculated from the data, obtaining distributions of constitutive parameters and constituent load bearing parameters. Additionally, we (1) simulated vascular drug treatment to assess the relevance of model uncertainty and (2) evaluated how increasing the number of measurement repetitions influences model uncertainty. We found substantial uncertainty in constitutive parameters. Simulating vascular drug treatment predicted a 6% point reduction in collagen load bearing ([Formula: see text]), approximately 50% of its uncertainty. Sensitivity analysis indicated that the uncertainty in [Formula: see text] was primarily caused by noise in distension and IMT measurements. Spread in [Formula: see text] could be decreased by 50% when increasing the number of measurement repetitions from 3 to 10. Model uncertainty, notably that in [Formula: see text], could conceal effects of vascular drug therapy. However, this uncertainty could be reduced by increasing the number of measurement repetitions of distension and wall thickness measurements used for model parameterisation.
TEMAC, Top Event Sensitivity Analysis
International Nuclear Information System (INIS)
Iman, R.L.; Shortencarier, M.J.
1988-01-01
1 - Description of program or function: TEMAC is designed to permit the user to easily estimate risk and to perform sensitivity and uncertainty analyses with a Boolean expression such as produced by the SETS computer program. SETS produces a mathematical representation of a fault tree used to model system unavailability. In the terminology of the TEMAC program, such a mathematical representation is referred to as a top event. The analysis of risk involves the estimation of the magnitude of risk, the sensitivity of risk estimates to base event probabilities and initiating event frequencies, and the quantification of the uncertainty in the risk estimates. 2 - Method of solution: Sensitivity and uncertainty analyses associated with top events involve mathematical operations on the corresponding Boolean expression for the top event, as well as repeated evaluations of the top event in a Monte Carlo fashion. TEMAC employs a general matrix approach which provides a convenient general form for Boolean expressions, is computationally efficient, and allows large problems to be analyzed. 3 - Restrictions on the complexity of the problem - Maxima of: 4000 cut sets, 500 events, 500 values in a Monte Carlo sample, 16 characters in an event name. These restrictions are implemented through the FORTRAN 77 PARAMATER statement
Sensitivity Analysis of Multidisciplinary Rotorcraft Simulations
Wang, Li; Diskin, Boris; Biedron, Robert T.; Nielsen, Eric J.; Bauchau, Olivier A.
2017-01-01
A multidisciplinary sensitivity analysis of rotorcraft simulations involving tightly coupled high-fidelity computational fluid dynamics and comprehensive analysis solvers is presented and evaluated. An unstructured sensitivity-enabled Navier-Stokes solver, FUN3D, and a nonlinear flexible multibody dynamics solver, DYMORE, are coupled to predict the aerodynamic loads and structural responses of helicopter rotor blades. A discretely-consistent adjoint-based sensitivity analysis available in FUN3D provides sensitivities arising from unsteady turbulent flows and unstructured dynamic overset meshes, while a complex-variable approach is used to compute DYMORE structural sensitivities with respect to aerodynamic loads. The multidisciplinary sensitivity analysis is conducted through integrating the sensitivity components from each discipline of the coupled system. Numerical results verify accuracy of the FUN3D/DYMORE system by conducting simulations for a benchmark rotorcraft test model and comparing solutions with established analyses and experimental data. Complex-variable implementation of sensitivity analysis of DYMORE and the coupled FUN3D/DYMORE system is verified by comparing with real-valued analysis and sensitivities. Correctness of adjoint formulations for FUN3D/DYMORE interfaces is verified by comparing adjoint-based and complex-variable sensitivities. Finally, sensitivities of the lift and drag functions obtained by complex-variable FUN3D/DYMORE simulations are compared with sensitivities computed by the multidisciplinary sensitivity analysis, which couples adjoint-based flow and grid sensitivities of FUN3D and FUN3D/DYMORE interfaces with complex-variable sensitivities of DYMORE structural responses.
Kalra, Tarandeep S.; Aretxabaleta, Alfredo; Seshadri, Pranay; Ganju, Neil K.; Beudin, Alexis
2017-12-01
Coastal hydrodynamics can be greatly affected by the presence of submerged aquatic vegetation. The effect of vegetation has been incorporated into the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling system. The vegetation implementation includes the plant-induced three-dimensional drag, in-canopy wave-induced streaming, and the production of turbulent kinetic energy by the presence of vegetation. In this study, we evaluate the sensitivity of the flow and wave dynamics to vegetation parameters using Sobol' indices and a least squares polynomial approach referred to as the Effective Quadratures method. This method reduces the number of simulations needed for evaluating Sobol' indices and provides a robust, practical, and efficient approach for the parameter sensitivity analysis. The evaluation of Sobol' indices shows that kinetic energy, turbulent kinetic energy, and water level changes are affected by plant stem density, height, and, to a lesser degree, diameter. Wave dissipation is mostly dependent on the variation in plant stem density. Performing sensitivity analyses for the vegetation module in COAWST provides guidance to optimize efforts and reduce exploration of parameter space for future observational and modeling work.
Directory of Open Access Journals (Sweden)
T. S. Kalra
2017-12-01
Full Text Available Coastal hydrodynamics can be greatly affected by the presence of submerged aquatic vegetation. The effect of vegetation has been incorporated into the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST modeling system. The vegetation implementation includes the plant-induced three-dimensional drag, in-canopy wave-induced streaming, and the production of turbulent kinetic energy by the presence of vegetation. In this study, we evaluate the sensitivity of the flow and wave dynamics to vegetation parameters using Sobol' indices and a least squares polynomial approach referred to as the Effective Quadratures method. This method reduces the number of simulations needed for evaluating Sobol' indices and provides a robust, practical, and efficient approach for the parameter sensitivity analysis. The evaluation of Sobol' indices shows that kinetic energy, turbulent kinetic energy, and water level changes are affected by plant stem density, height, and, to a lesser degree, diameter. Wave dissipation is mostly dependent on the variation in plant stem density. Performing sensitivity analyses for the vegetation module in COAWST provides guidance to optimize efforts and reduce exploration of parameter space for future observational and modeling work.
Wind Farm Group Efficiency - A Sensitivity Analysis with a Mesoscale Model
DEFF Research Database (Denmark)
Volker, Patrick; Badger, Jake; Hahmann, Andrea N.
2014-01-01
In the North Sea the total installed capacity was in 2012 5GW, and it estimated that it will grow to 40GW by 2020 (EWEA). This will lead to an increasing wind farm density in regions with the most favourable conditions. In this study, we investigate the sensitivity of power density losses to wind...
Sensitivity and uncertainty analysis for a field-scale P loss model
Models are often used to predict phosphorus (P) loss from agricultural fields. While it is commonly recognized that there are inherent uncertainties with model predictions, limited studies have addressed model prediction uncertainty. In this study we assess the effect of model input error on predict...
Casadebaig, Pierre; Zheng, Bangyou; Chapman, Scott; Huth, Neil; Faivre, Robert; Chenu, Karine
2016-01-01
A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites × 125 years), management practices (3 sowing dates × 3 nitrogen fertilization levels) and CO2 (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait × environment × management landscape (∼ 82 million individual simulations in total). The patterns of parameter × environment × management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference cultivar. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identification of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement. PMID:26799483
Directory of Open Access Journals (Sweden)
Pierre Casadebaig
Full Text Available A crop can be viewed as a complex system with outputs (e.g. yield that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background. The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90 was evaluated in a wide target population of environments (4 sites × 125 years, management practices (3 sowing dates × 3 nitrogen fertilization levels and CO2 (2 levels. The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait × environment × management landscape (∼ 82 million individual simulations in total. The patterns of parameter × environment × management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference cultivar. Main (i.e. linear and interaction (i.e. non-linear and interaction sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identification of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model improvement.
Casadebaig, Pierre; Zheng, Bangyou; Chapman, Scott; Huth, Neil; Faivre, Robert; Chenu, Karine
2016-01-01
A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites × 125 years), management practices (3 sowing dates × 3 nitrogen fertilization levels) and CO2 (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait × environment × management landscape (∼ 82 million individual simulations in total). The patterns of parameter × environment × management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference cultivar. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identification of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement.
2015-10-28
techniques such as regression analysis, correlation, and multicollinearity assessment to identify the change and error on the input to the model...between many of the independent or predictor variables, the issue of multicollinearity may arise [18]. VII. SUMMARY Accurate decisions concerning
International Nuclear Information System (INIS)
Clifton, P.M.
1985-03-01
This study examines the sensitivity of the travel time distribution predicted by a reference case model to (1) scale of representation of the model parameters, (2) size of the model domain, (3) correlation range of log-transmissivity, and (4) cross correlations between transmissivity and effective thickness. The basis for the reference model is the preliminary stochastic travel time model previously documented by the Basalt Waste Isolation Project. Results of this study show the following. The variability of the predicted travel times can be adequately represented when the ratio between the size of the zones used to represent the model parameters and the log-transmissivity correlation range is less than about one-fifth. The size of the model domain and the types of boundary conditions can have a strong impact on the distribution of travel times. Longer log-transmissivity correlation ranges cause larger variability in the predicted travel times. Positive cross correlation between transmissivity and effective thickness causes a decrease in the travel time variability. These results demonstrate the need for a sound conceptual model prior to conducting a stochastic travel time analysis
Land Sensitivity Analysis of Degradation using MEDALUS model: Case Study of Deliblato Sands, Serbia
Directory of Open Access Journals (Sweden)
Kadović Ratko
2016-12-01
Full Text Available This paper studies the assessment of sensitivity to land degradation of Deliblato sands (the northern part of Serbia, as a special nature reserve. Sandy soils of Deliblato sands are highly sensitive to degradation (given their fragility, while the system of land use is regulated according to the law, consisting of three zones under protection. Based on the MEDALUS approach and the characteristics of the study area, four main factors were considered for evaluation: soil, climate, vegetation and management. Several indicators affecting the quality of each factor were identified. Each indicator was quantified according to its quality and given a weighting of between 1.0 and 2.0. ArcGIS 9 was utilized to analyze and prepare the layers of quality maps, using the geometric mean to integrate the individual indicator map. In turn, the geometric mean of all four quality indices was used to generate sensitivity of land degradation status map. Results showed that 56.26% of the area is classified as critical; 43.18% as fragile; 0.55% as potentially affected and 0.01% as not affected by degradation. The values of vegetation quality index, expressed as coverage, diversity of vegetation functions and management policy during the protection regime are clearly represented through correlation coefficient (0.87 and 0.47.
Maternal sensitivity: a concept analysis.
Shin, Hyunjeong; Park, Young-Joo; Ryu, Hosihn; Seomun, Gyeong-Ae
2008-11-01
The aim of this paper is to report a concept analysis of maternal sensitivity. Maternal sensitivity is a broad concept encompassing a variety of interrelated affective and behavioural caregiving attributes. It is used interchangeably with the terms maternal responsiveness or maternal competency, with no consistency of use. There is a need to clarify the concept of maternal sensitivity for research and practice. A search was performed on the CINAHL and Ovid MEDLINE databases using 'maternal sensitivity', 'maternal responsiveness' and 'sensitive mothering' as key words. The searches yielded 54 records for the years 1981-2007. Rodgers' method of evolutionary concept analysis was used to analyse the material. Four critical attributes of maternal sensitivity were identified: (a) dynamic process involving maternal abilities; (b) reciprocal give-and-take with the infant; (c) contingency on the infant's behaviour and (d) quality of maternal behaviours. Maternal identity and infant's needs and cues are antecedents for these attributes. The consequences are infant's comfort, mother-infant attachment and infant development. In addition, three positive affecting factors (social support, maternal-foetal attachment and high self-esteem) and three negative affecting factors (maternal depression, maternal stress and maternal anxiety) were identified. A clear understanding of the concept of maternal sensitivity could be useful for developing ways to enhance maternal sensitivity and to maximize the developmental potential of infants. Knowledge of the attributes of maternal sensitivity identified in this concept analysis may be helpful for constructing measuring items or dimensions.
Contreras-Reyes, Eduardo; Garay, Jeremías
2018-01-01
The outer rise is a topographic bulge seaward of the trench at a subduction zone that is caused by bending and flexure of the oceanic lithosphere as subduction commences. The classic model of the flexure of oceanic lithosphere w (x) is a hydrostatic restoring force acting upon an elastic plate at the trench axis. The governing parameters are elastic thickness Te, shear force V0, and bending moment M0. V0 and M0 are unknown variables that are typically replaced by other quantities such as the height of the fore-bulge, wb, and the half-width of the fore-bulge, (xb - xo). However, this method is difficult to implement with the presence of excessive topographic noise around the bulge of the outer rise. Here, we present an alternative method to the classic model, in which lithospheric flexure w (x) is a function of the flexure at the trench axis w0, the initial dip angle of subduction β0, and the elastic thickness Te. In this investigation, we apply a sensitivity analysis to both methods in order to determine the impact of the differing parameters on the solution, w (x). The parametric sensitivity analysis suggests that stable solutions for the alternative approach requires relatively low β0 values (rise bulge. The alternative method is a more suitable approach, assuming that accurate geometric information at the trench axis (i.e., w0 and β0) is available.
Srivastava, S. K., Sr.; Sharma, D. A.; Sachdeva, K.
2017-12-01
Indo-Gangetic plains of India experience severe fog conditions during the peak winter months of December and January every year. In this paper an attempt has been to analyze the spatial and temporal variability of winter fog over Indo-Gangetic plains. Further, an attempt has also been made to configure an efficient meso-scale numerical weather prediction model using different parameterization schemes and develop a forecasting tool for prediction of fog during winter months over Indo-Gangetic plains. The study revealed that an alarming increasing positive trend of fog frequency prevails over many locations of IGP. Hot spot and cluster analysis were conducted to identify the high fog prone zones using GIS and inferential statistical tools respectively. Hot spots on an average experiences fog on 68.27% days, it is followed by moderate and cold spots with 48.03% and 21.79% respectively. The study proposes a new FASP (Fog Analysis, sensitivity and prediction) Model for overall analysis and prediction of fog at a particular location and period over IGP. In the first phase of this model long term climatological fog data of a location is analyzed to determine its characteristics and prevailing trend using various advanced statistical techniques. During a second phase a sensitivity test is conducted with different combination of parameterization schemes to determine the most suitable combination for fog simulation over a particular location and period and in the third and final phase, first ARIMA model is used to predict the number of fog days in future . Thereafter, Numerical model is used to predict the various meteorological parameters favourable for fog forecast. Finally, Hybrid model is used for fog forecast over the study location. The results of the FASP model are validated with actual ground based fog data using statistical tools. Forecast Fog-gram generated using hybrid model during Jan 2017 shows highly encouraging results for fog occurrence/Non occurrence between
Wu, Y.; Liu, S.
2012-01-01
Parameter optimization and uncertainty issues are a great challenge for the application of large environmental models like the Soil and Water Assessment Tool (SWAT), which is a physically-based hydrological model for simulating water and nutrient cycles at the watershed scale. In this study, we present a comprehensive modeling environment for SWAT, including automated calibration, and sensitivity and uncertainty analysis capabilities through integration with the R package Flexible Modeling Environment (FME). To address challenges (e.g., calling the model in R and transferring variables between Fortran and R) in developing such a two-language coupling framework, 1) we converted the Fortran-based SWAT model to an R function (R-SWAT) using the RFortran platform, and alternatively 2) we compiled SWAT as a Dynamic Link Library (DLL). We then wrapped SWAT (via R-SWAT) with FME to perform complex applications including parameter identifiability, inverse modeling, and sensitivity and uncertainty analysis in the R environment. The final R-SWAT-FME framework has the following key functionalities: automatic initialization of R, running Fortran-based SWAT and R commands in parallel, transferring parameters and model output between SWAT and R, and inverse modeling with visualization. To examine this framework and demonstrate how it works, a case study simulating streamflow in the Cedar River Basin in Iowa in the United Sates was used, and we compared it with the built-in auto-calibration tool of SWAT in parameter optimization. Results indicate that both methods performed well and similarly in searching a set of optimal parameters. Nonetheless, the R-SWAT-FME is more attractive due to its instant visualization, and potential to take advantage of other R packages (e.g., inverse modeling and statistical graphics). The methods presented in the paper are readily adaptable to other model applications that require capability for automated calibration, and sensitivity and uncertainty
Todd, Martin; Cavazos, Carolina; Wang, Yi
2013-04-01
The Saharan atmospheric boundary layer (SABL) during summer is one of the deepest on Earth, and is crucial in controlling the vertical redistribution and long-range transport of dust in the Sahara. The SABL is typically made up of an actively growing convective layer driven by high sensible heating at the surface, with a deep, near-neutrally stratified Saharan residual layer (SRL) above it, which is mostly well mixed in humidity and temperature and reaches a height of ˜5-6km. These two layers are usually separated by a weak (≤1K) temperature inversion. Model representation of the SPBL structure and evolution is important for accurate weather/climate and aerosol prediction. In this work, we evaluate model performance of the Weather Research and Forecasting (WRF) to represent key multi-scale processes in the SABL during summer 2011, including depiction of the diurnal cycle. For this purpose, a sensitivity analysis is performed to examine the performance of seven PBL schemes (YSU, MYJ, QNSE, MYNN, ACM, Boulac and MRF) and two land-surface model (Noah and RUC) schemes. In addition, the sensitivity to the choice of lateral boundary conditions (ERA-Interim and NCEP) and land use classification maps (USGS and MODIS-based) is tested. Model outputs were confronted upper-air and surface observations from the Fennec super-site at Bordj Moktar and automatic weather station (AWS) in Southern Algeria Vertical profiles of wind speed, potential temperature and water vapour mixing ratio were examined to diagnose differences in PBL heights and model efficacy to reproduce the diurnal cycle of the SABL. We find that the structure of the model SABL is most sensitive the choice of land surface model and lateral boundary conditions and relatively insensitive to the PBL scheme. Overall the model represents well the diurnal cycle in the structure of the SABL. Consistent model biases include (i) a moist (1-2 gkg-1) and slightly cool (~1K) bias in the daytime convective boundary layer (ii
Multiple predictor smoothing methods for sensitivity analysis
International Nuclear Information System (INIS)
Helton, Jon Craig; Storlie, Curtis B.
2006-01-01
The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (1) locally weighted regression (LOESS), (2) additive models, (3) projection pursuit regression, and (4) recursive partitioning regression. The indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present
Multiple predictor smoothing methods for sensitivity analysis.
Energy Technology Data Exchange (ETDEWEB)
Helton, Jon Craig; Storlie, Curtis B.
2006-08-01
The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (1) locally weighted regression (LOESS), (2) additive models, (3) projection pursuit regression, and (4) recursive partitioning regression. The indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present.
International Nuclear Information System (INIS)
Clifton, P.M.
1984-12-01
The deep basalt formations beneath the Hanford Site are being investigated for the Department of Energy (DOE) to assess their suitability as a host medium for a high level nuclear waste repository. Predicted performance of the proposed repository is an important part of the investigation. One of the performance measures being used to gauge the suitability of the host medium is pre-waste-emplacement groundwater travel times to the accessible environment. Many deterministic analyses of groundwater travel times have been completed by Rockwell and other independent organizations. Recently, Rockwell has completed a preliminary stochastic analysis of groundwater travel times. This document presents analyses that show the sensitivity of the results from the previous stochastic travel time study to: (1) scale of representation of model parameters, (2) size of the model domain, (3) correlation range of log-transmissivity, and (4) cross-correlation between transmissivity and effective thickness. 40 refs., 29 figs., 6 tabs
International Nuclear Information System (INIS)
Tang, Kunkun; Congedo, Pietro M.; Abgrall, Rémi
2016-01-01
The Polynomial Dimensional Decomposition (PDD) is employed in this work for the global sensitivity analysis and uncertainty quantification (UQ) of stochastic systems subject to a moderate to large number of input random variables. Due to the intimate connection between the PDD and the Analysis of Variance (ANOVA) approaches, PDD is able to provide a simpler and more direct evaluation of the Sobol' sensitivity indices, when compared to the Polynomial Chaos expansion (PC). Unfortunately, the number of PDD terms grows exponentially with respect to the size of the input random vector, which makes the computational cost of standard methods unaffordable for real engineering applications. In order to address the problem of the curse of dimensionality, this work proposes essentially variance-based adaptive strategies aiming to build a cheap meta-model (i.e. surrogate model) by employing the sparse PDD approach with its coefficients computed by regression. Three levels of adaptivity are carried out in this paper: 1) the truncated dimensionality for ANOVA component functions, 2) the active dimension technique especially for second- and higher-order parameter interactions, and 3) the stepwise regression approach designed to retain only the most influential polynomials in the PDD expansion. During this adaptive procedure featuring stepwise regressions, the surrogate model representation keeps containing few terms, so that the cost to resolve repeatedly the linear systems of the least-squares regression problem is negligible. The size of the finally obtained sparse PDD representation is much smaller than the one of the full expansion, since only significant terms are eventually retained. Consequently, a much smaller number of calls to the deterministic model is required to compute the final PDD coefficients.
Energy Technology Data Exchange (ETDEWEB)
Tang, Kunkun, E-mail: ktg@illinois.edu [The Center for Exascale Simulation of Plasma-Coupled Combustion (XPACC), University of Illinois at Urbana–Champaign, 1308 W Main St, Urbana, IL 61801 (United States); Inria Bordeaux – Sud-Ouest, Team Cardamom, 200 avenue de la Vieille Tour, 33405 Talence (France); Congedo, Pietro M. [Inria Bordeaux – Sud-Ouest, Team Cardamom, 200 avenue de la Vieille Tour, 33405 Talence (France); Abgrall, Rémi [Institut für Mathematik, Universität Zürich, Winterthurerstrasse 190, CH-8057 Zürich (Switzerland)
2016-06-01
The Polynomial Dimensional Decomposition (PDD) is employed in this work for the global sensitivity analysis and uncertainty quantification (UQ) of stochastic systems subject to a moderate to large number of input random variables. Due to the intimate connection between the PDD and the Analysis of Variance (ANOVA) approaches, PDD is able to provide a simpler and more direct evaluation of the Sobol' sensitivity indices, when compared to the Polynomial Chaos expansion (PC). Unfortunately, the number of PDD terms grows exponentially with respect to the size of the input random vector, which makes the computational cost of standard methods unaffordable for real engineering applications. In order to address the problem of the curse of dimensionality, this work proposes essentially variance-based adaptive strategies aiming to build a cheap meta-model (i.e. surrogate model) by employing the sparse PDD approach with its coefficients computed by regression. Three levels of adaptivity are carried out in this paper: 1) the truncated dimensionality for ANOVA component functions, 2) the active dimension technique especially for second- and higher-order parameter interactions, and 3) the stepwise regression approach designed to retain only the most influential polynomials in the PDD expansion. During this adaptive procedure featuring stepwise regressions, the surrogate model representation keeps containing few terms, so that the cost to resolve repeatedly the linear systems of the least-squares regression problem is negligible. The size of the finally obtained sparse PDD representation is much smaller than the one of the full expansion, since only significant terms are eventually retained. Consequently, a much smaller number of calls to the deterministic model is required to compute the final PDD coefficients.
Price, D. C.; Angus, D. A.; Garcia, A.; Fisher, Q. J.; Parsons, S.; Kato, J.
2018-03-01
Time-lapse seismic attributes are used extensively in the history matching of production simulator models. However, although proven to contain information regarding production induced stress change, it is typically only loosely (i.e. qualitatively) used to calibrate geomechanical models. In this study we conduct a multimethod Global Sensitivity Analysis (GSA) to assess the feasibility and aid the quantitative calibration of geomechanical models via near-offset time-lapse seismic data. Specifically, the calibration of mechanical properties of the overburden. Via the GSA, we analyse the near-offset overburden seismic traveltimes from over 4000 perturbations of a Finite Element (FE) geomechanical model of a typical High Pressure High Temperature (HPHT) reservoir in the North Sea. We find that, out of an initially large set of material properties, the near-offset overburden traveltimes are primarily affected by Young's modulus and the effective stress (i.e. Biot) coefficient. The unexpected significance of the Biot coefficient highlights the importance of modelling fluid flow and pore pressure outside of the reservoir. The FE model is complex and highly nonlinear. Multiple combinations of model parameters can yield equally possible model realizations. Consequently, numerical calibration via a large number of random model perturbations is unfeasible. However, the significant differences in traveltime results suggest that more sophisticated calibration methods could potentially be feasible for finding numerous suitable solutions. The results of the time-varying GSA demonstrate how acquiring multiple vintages of time-lapse seismic data can be advantageous. However, they also suggest that significant overburden near-offset seismic time-shifts, useful for model calibration, may take up to 3 yrs after the start of production to manifest. Due to the nonlinearity of the model behaviour, similar uncertainty in the reservoir mechanical properties appears to influence overburden
Sensitivity analysis of an explanatory crop model at the regional scale using geospatial data
Explanatory, or process-based, crop models are computational tools that have been developed for a wide range of applications, such as optimizing crop production and simulating the effects of climate change. Crop models rely on a diverse set of input variables for predicting outcomes such as crop yie...
Hydrologic models are used to simulate the responses of agricultural systems to different inputs and management strategies to identify alternative management practices to cope up with future climate and/or geophysical changes. The Agricultural Policy/Environmental eXtender (APEX) is a model develope...
Statistical emulation of a tsunami model for sensitivity analysis and uncertainty quantification
Directory of Open Access Journals (Sweden)
A. Sarri
2012-06-01
Full Text Available Due to the catastrophic consequences of tsunamis, early warnings need to be issued quickly in order to mitigate the hazard. Additionally, there is a need to represent the uncertainty in the predictions of tsunami characteristics corresponding to the uncertain trigger features (e.g. either position, shape and speed of a landslide, or sea floor deformation associated with an earthquake. Unfortunately, computer models are expensive to run. This leads to significant delays in predictions and makes the uncertainty quantification impractical. Statistical emulators run almost instantaneously and may represent well the outputs of the computer model. In this paper, we use the outer product emulator to build a fast statistical surrogate of a landslide-generated tsunami computer model. This Bayesian framework enables us to build the emulator by combining prior knowledge of the computer model properties with a few carefully chosen model evaluations. The good performance of the emulator is validated using the leave-one-out method.
Directory of Open Access Journals (Sweden)
S. Wang
2012-12-01
Full Text Available Model calibration is essential for hydrologic modeling of large watersheds in a heterogeneous mountain environment. Little guidance is available for model calibration protocols for distributed models that aim at capturing the spatial variability of hydrologic processes. This study used the physically-based distributed hydrologic model, MIKE SHE, to contrast a lumped calibration protocol that used streamflow measured at one single watershed outlet to a multi-site calibration method which employed streamflow measurements at three stations within the large Chaohe River basin in northern China. Simulation results showed that the single-site calibrated model was able to sufficiently simulate the hydrographs for two of the three stations (Nash-Sutcliffe coefficient of 0.65–0.75, and correlation coefficient 0.81–0.87 during the testing period, but the model performed poorly for the third station (Nash-Sutcliffe coefficient only 0.44. Sensitivity analysis suggested that streamflow of upstream area of the watershed was dominated by slow groundwater, whilst streamflow of middle- and down- stream areas by relatively quick interflow. Therefore, a multi-site calibration protocol was deemed necessary. Due to the potential errors and uncertainties with respect to the representation of spatial variability, performance measures from the multi-site calibration protocol slightly decreased for two of the three stations, whereas it was improved greatly for the third station. We concluded that multi-site calibration protocol reached a compromise in term of model performance for the three stations, reasonably representing the hydrographs of all three stations with Nash-Sutcliffe coefficient ranging from 0.59–072. The multi-site calibration protocol applied in the analysis generally has advantages to the single site calibration protocol.
Schenkl, Sebastian; Muggenthaler, Holger; Hubig, Michael; Erdmann, Bodo; Weiser, Martin; Zachow, Stefan; Heinrich, Andreas; Güttler, Felix Victor; Teichgräber, Ulf; Mall, Gita
2017-05-01
Temperature-based death time estimation is based either on simple phenomenological models of corpse cooling or on detailed physical heat transfer models. The latter are much more complex but allow a higher accuracy of death time estimation, as in principle, all relevant cooling mechanisms can be taken into account.Here, a complete workflow for finite element-based cooling simulation is presented. The following steps are demonstrated on a CT phantom: Computer tomography (CT) scan Segmentation of the CT images for thermodynamically relevant features of individual geometries and compilation in a geometric computer-aided design (CAD) model Conversion of the segmentation result into a finite element (FE) simulation model Computation of the model cooling curve (MOD) Calculation of the cooling time (CTE) For the first time in FE-based cooling time estimation, the steps from the CT image over segmentation to FE model generation are performed semi-automatically. The cooling time calculation results are compared to cooling measurements performed on the phantoms under controlled conditions. In this context, the method is validated using a CT phantom. Some of the phantoms' thermodynamic material parameters had to be determined via independent experiments.Moreover, the impact of geometry and material parameter uncertainties on the estimated cooling time is investigated by a sensitivity analysis.
International Nuclear Information System (INIS)
Jonsson, Karin; Elert, Mark
2006-08-01
In this report, further investigations of the model concept for radionuclide transport in stream, developed in the SKB report TR-05-03 is presented. Especially three issues have been the focus of the model investigations. The first issue was to investigate the influence of assumed channel geometry on the simulation results. The second issue was to reconsider the applicability of the equation for the bed-load transport in the stream model, and finally the last issue was to investigate how the model discretisation will influence the simulation results. The simulations showed that there were relatively small differences in results when applying different cross-sections in the model. The inclusion of the exact shape of the cross-section in the model is therefore not crucial, however, if cross-sectional data exist, the overall shape of the cross-section should be used in the model formulation. This could e.g. be accomplished by using measured values of the stream width and depth in the middle of the stream and by assuming a triangular shape. The bed-load transport was in this study determined for different sediment characteristics which can be used as an order of magnitude estimation if no exact determinations of the bed-load are available. The difference in the calculated bed-load transport for the different materials was, however, found to be limited. The investigation of model discretisation showed that a fine model discretisation to account for numerical effects is probably not important for the performed simulations. However, it can be necessary for being able to account for different conditions along a stream. For example, the application of mean slopes instead of individual values in the different stream reaches can result in very different predicted concentrations
Radomyski, Artur; Giubilato, Elisa; Ciffroy, Philippe; Critto, Andrea; Brochot, Céline; Marcomini, Antonio
2016-11-01
The study is focused on applying uncertainty and sensitivity analysis to support the application and evaluation of large exposure models where a significant number of parameters and complex exposure scenarios might be involved. The recently developed MERLIN-Expo exposure modelling tool was applied to probabilistically assess the ecological and human exposure to PCB 126 and 2,3,7,8-TCDD in the Venice lagoon (Italy). The 'Phytoplankton', 'Aquatic Invertebrate', 'Fish', 'Human intake' and PBPK models available in MERLIN-Expo library were integrated to create a specific food web to dynamically simulate bioaccumulation in various aquatic species and in the human body over individual lifetimes from 1932 until 1998. MERLIN-Expo is a high tier exposure modelling tool allowing propagation of uncertainty on the model predictions through Monte Carlo simulation. Uncertainty in model output can be further apportioned between parameters by applying built-in sensitivity analysis tools. In this study, uncertainty has been extensively addressed in the distribution functions to describe the data input and the effect on model results by applying sensitivity analysis techniques (screening Morris method, regression analysis, and variance-based method EFAST). In the exposure scenario developed for the Lagoon of Venice, the concentrations of 2,3,7,8-TCDD and PCB 126 in human blood turned out to be mainly influenced by a combination of parameters (half-lives of the chemicals, body weight variability, lipid fraction, food assimilation efficiency), physiological processes (uptake/elimination rates), environmental exposure concentrations (sediment, water, food) and eating behaviours (amount of food eaten). In conclusion, this case study demonstrated feasibility of MERLIN-Expo to be successfully employed in integrated, high tier exposure assessment. Copyright © 2016 Elsevier B.V. All rights reserved.
Szwedowski, T D; Fialkov, J; Whyne, C M
2011-01-01
Developing a more complete understanding of the mechanical response of the craniofacial skeleton (CFS) to physiological loads is fundamental to improving treatment for traumatic injuries, reconstruction due to neoplasia, and deformities. Characterization of the biomechanics of the CFS is challenging due to its highly complex structure and heterogeneity, motivating the utilization of experimentally validated computational models. As such, the objective of this study was to develop, experimentally validate, and parametrically analyse a patient-specific finite element (FE) model of the CFS to elucidate a better understanding of the factors that are of intrinsic importance to the skeletal structural behaviour of the human CFS. An FE model of a cadaveric craniofacial skeleton was created from subject-specific computed tomography data. The model was validated based on bone strain measurements taken under simulated physiological-like loading through the masseter and temporalis muscles (which are responsible for the majority of craniofacial physiologic loading due to mastication). The baseline subject-specific model using locally defined cortical bone thicknesses produced the strongest correlation to the experimental data (r2 = 0.73). Large effects on strain patterns arising from small parametric changes in cortical thickness suggest that the very thin bony structures present in the CFS are crucial to characterizing the local load distribution in the CFS accurately.
Sensitivity analysis of an operational advanced Gaussian model to different turbulent regimes
International Nuclear Information System (INIS)
Mangia, C.; Rizza, U.; Tirabassi, T.
1998-01-01
A non-reactive air pollution model evaluating ground level concentration is presented. It relies on a new Gaussian formulation (Lupini, R. and Tirabassi, T., J. Appl. Meteor., 20 (1981) 565-570; Tirabassi, T. and Rizza, U., Atmos. Environ., 28 (1994) 611-615) for transport and vertical diffusion in the Atmospheric Boundary Layer (ABL). In this formulation, the source height is replaced by a virtual height expressed by simple functions of meteorological variables. The model accepts a general profile of wind u(z) and eddy diffusivity coefficient K z . The lateral dispersion coefficient is based on Taylor's theory (Taylor, G. I., Proc. London Math. Soc., 20 (1921) 196-204). The turbulence in the ABL is subdivided into various regimes, each characterized by different parameters for length and velocity scales. The model performances under unstable conditions have been tested utilizing two different data sets
Directory of Open Access Journals (Sweden)
Liang Tang
2010-01-01
Full Text Available A mathematical model for M/G/1-type queueing networks with multiple user applications and limited resources is established. The goal is to develop a dynamic distributed algorithm for this model, which supports all data traffic as efficiently as possible and makes optimally fair decisions about how to minimize the network performance cost. An online policy gradient optimization algorithm based on a single sample path is provided to avoid suffering from a “curse of dimensionality”. The asymptotic convergence properties of this algorithm are proved. Numerical examples provide valuable insights for bridging mathematical theory with engineering practice.
Techniques for sensitivity analysis of SYVAC results
International Nuclear Information System (INIS)
Prust, J.O.
1985-05-01
Sensitivity analysis techniques may be required to examine the sensitivity of SYVAC model predictions to the input parameter values, the subjective probability distributions assigned to the input parameters and to the relationship between dose and the probability of fatal cancers plus serious hereditary disease in the first two generations of offspring of a member of the critical group. This report mainly considers techniques for determining the sensitivity of dose and risk to the variable input parameters. The performance of a sensitivity analysis technique may be improved by decomposing the model and data into subsets for analysis, making use of existing information on sensitivity and concentrating sampling in regions the parameter space that generates high doses or risks. A number of sensitivity analysis techniques are reviewed for their application to the SYVAC model including four techniques tested in an earlier study by CAP Scientific for the SYVAC project. This report recommends the development now of a method for evaluating the derivative of dose and parameter value and extending the Kruskal-Wallis technique to test for interactions between parameters. It is also recommended that the sensitivity of the output of each sub-model of SYVAC to input parameter values should be examined. (author)
Global sensitivity analysis of a dynamic agroecosystem model under different irrigation treatments
Savings in consumptive use through limited or deficit irrigation in agriculture has become an increasingly viable source of additional water for places with high population growth such as the Colorado Front Range, USA. Crop models provide a mechanism to evaluate various management methods without pe...
The microbial quality of surface waters warrants attention because of associated food- and waterborne-disease outbreaks, and fecal indicator organisms (FIOs) are commonly used to evaluate levels of microbial pollution. Models that predict the fate and transport of FIOs are required for designing and...
DEFF Research Database (Denmark)
Vezzaro, Luca; Mikkelsen, Peter Steen
2012-01-01
of uncertainty in a conceptual lumped dynamic stormwater runoff quality model that is used in a study catchment to estimate (i) copper loads, (ii) compliance with dissolved Cu concentration limits on stormwater discharge and (iii) the fraction of Cu loads potentially intercepted by a planned treatment facility...
Sensitivity analysis of ground level ozone in India using WRF-CMAQ models
Sharma, Sumit; Chatani, Satoru; Mahtta, Richa; Goel, Anju; Kumar, Atul
2016-01-01
Ground level ozone is emerging as a pollutant of concern in India. Limited surface monitoring data reveals that ozone concentrations are well above the prescribed national standards. This study aims to simulate the regional and urban scale ozone concentrations in India using WRF-CMAQ models.
van Santen, R.B.; de Swart, H.E.; van Dijk, T.A.G.P.
2011-01-01
An integrated field data-modelling approach is employed to investigate relationships between the wavelength of tidal sand waves and four environmental parameters: tidal current amplitude, water depth, tidal ellipticity and median grain size. From echo sounder data at 23 locations on the Dutch
DEFF Research Database (Denmark)
Malaguerra, Flavio; Chambon, Julie Claire Claudia; Albrechtsen, Hans-Jørgen
2010-01-01
and natural degradation of chlorinated solvents frequently occurs in the subsurface through sequential reductive dechlorination. However, the occurrence and the performance of natural sequential reductive dechlorination strongly depends on environmental factor such as redox conditions, presence of fermenting...... organic matter / electron donors, presence of specific biomass, etc. Here we develop a new fully-kinetic biogeochemical reactive model able to simulate chlorinated solvents degradation as well as production and consumption of molecular hydrogen. The model is validated using batch experiment data......Chlorinated hydrocarbons originating from point sources are amongst the most prevalent contaminants of ground water and often represent a serious threat to groundwater-based drinking water resources. Natural attenuation of contaminant plumes can play a major role in contaminated site management...
Abdallah, Wael; Zhao, Weishu; Gmira, Ahmed; Negara, Ardiansyah; Buiting, Jan
2011-01-01
Interfacial tension (IFT) measurements of Dodecane/brine systems at different concentrations and Dodecane/deionized water subject to different Dodecane purification cycles were taken over extended durations at room temperature and pressure to investigate the impact of aging. When a fresh droplet was formed, a sharp drop in IFT was observed assumed to be a result of intrinsic impurity adsorption at the interface. The subsequent measurements exhibited a prolonged equilibration period consistent with diffusion from the bulk phase to the interface. Our results indicate that minute amounts of impurities present in experimental chemical fluids "used as received" have a drastic impact on the properties of the interface. Initial and equilibrium IFT are shown to be dramatically different, therefore it is important to be cautious of utilizing IFT values in numerical models. The study demonstrates the impact these variations in IFT have on relative permeability relationships by adopting a simple pore network model simulation.
Abdallah, Wael
2011-05-18
Interfacial tension (IFT) measurements of Dodecane/brine systems at different concentrations and Dodecane/deionized water subject to different Dodecane purification cycles were taken over extended durations at room temperature and pressure to investigate the impact of aging. When a fresh droplet was formed, a sharp drop in IFT was observed assumed to be a result of intrinsic impurity adsorption at the interface. The subsequent measurements exhibited a prolonged equilibration period consistent with diffusion from the bulk phase to the interface. Our results indicate that minute amounts of impurities present in experimental chemical fluids "used as received" have a drastic impact on the properties of the interface. Initial and equilibrium IFT are shown to be dramatically different, therefore it is important to be cautious of utilizing IFT values in numerical models. The study demonstrates the impact these variations in IFT have on relative permeability relationships by adopting a simple pore network model simulation.
A Sensitivity Analysis of a Computer Model-Based Leak Detection System for Oil Pipelines
Zhe Lu; Yuntong She; Mark Loewen
2017-01-01
Improving leak detection capability to eliminate undetected releases is an area of focus for the energy pipeline industry, and the pipeline companies are working to improve existing methods for monitoring their pipelines. Computer model-based leak detection methods that detect leaks by analyzing the pipeline hydraulic state have been widely employed in the industry, but their effectiveness in practical applications is often challenged by real-world uncertainties. This study quantitatively ass...
A hybrid approach for global sensitivity analysis
International Nuclear Information System (INIS)
Chakraborty, Souvik; Chowdhury, Rajib
2017-01-01
Distribution based sensitivity analysis (DSA) computes sensitivity of the input random variables with respect to the change in distribution of output response. Although DSA is widely appreciated as the best tool for sensitivity analysis, the computational issue associated with this method prohibits its use for complex structures involving costly finite element analysis. For addressing this issue, this paper presents a method that couples polynomial correlated function expansion (PCFE) with DSA. PCFE is a fully equivalent operational model which integrates the concepts of analysis of variance decomposition, extended bases and homotopy algorithm. By integrating PCFE into DSA, it is possible to considerably alleviate the computational burden. Three examples are presented to demonstrate the performance of the proposed approach for sensitivity analysis. For all the problems, proposed approach yields excellent results with significantly reduced computational effort. The results obtained, to some extent, indicate that proposed approach can be utilized for sensitivity analysis of large scale structures. - Highlights: • A hybrid approach for global sensitivity analysis is proposed. • Proposed approach integrates PCFE within distribution based sensitivity analysis. • Proposed approach is highly efficient.
DEFF Research Database (Denmark)
Folens, Karel; Mortier, Séverine Thérèse F C; Baeten, Janis
2016-01-01
Platinum (Pt) based antineoplastics are important in cancer therapy. To date the Pt which is urinary excreted by the patients ends up in wastewater. This is disadvantageous from both an economic as from an ecological point of view because Pt is a valuable material and the excretion products...... are toxic for aquatic organisms. Therefore, efforts should be made to recover the Pt. The urinary excretion of Pt from two antineoplastics are taken under consideration, i.e. cisplatin and carboplatin. Using these reference compounds, a scenario analysis based on administration statistics from Ghent...
Global modelling of secondary organic aerosol in the troposphere: a sensitivity analysis
Directory of Open Access Journals (Sweden)
K. Tsigaridis
2003-01-01
Full Text Available A global 3-dimensional chemistry/transport model able to describe O3, NOx, Volatile Organic Compounds (VOC, sulphur and NH3 chemistry has been extended to simulate the temporal and spatial distribution of primary and secondary carbonaceous aerosols in the troposphere focusing on Secondary Organic Aerosol (SOA formation. A number of global simulations have been performed to determine a possible range of annual global SOA production and investigate uncertainties associated with the model results. The studied uncertainties in the SOA budget have been evaluated to be in decreasing importance: the potentially irreversible sticking of the semi-volatile compounds on aerosols, the enthalpy of vaporization of these compounds, the partitioning of SOA on non-carbonaceous aerosols, the conversion of aerosols from hydrophobic to hydrophilic, the emissions of primary carbonaceous aerosols, the chemical fate of the first generation products and finally the activity coefficient of the condensable species. The large uncertainties associated with the emissions of VOC and the adopted simplification of chemistry have not been investigated in this study. Although not all sources of uncertainties have been investigated, according to our calculations, the above factors within the experimental range of variations could result to an overall uncertainty of about a factor of 20 in the global SOA budget. The global annual SOA production from biogenic VOC might range from 2.5 to 44.5 Tg of organic matter per year, whereas that from anthropogenic VOC ranges from 0.05 to 2.62 Tg of organic matter per year. These estimates can be considered as a lower limit, since partitioning on coarse particles like nitrate, dust or sea-salt, together with the partitioning and the dissociation of the semi-volatile products in aerosol water has been neglected. Comparison of model results to observations, where available, shows a better agreement for the upper budget estimates than for the
Sensitivity analysis of brain morphometry based on MRI-derived surface models
Klein, Gregory J.; Teng, Xia; Schoenemann, P. T.; Budinger, Thomas F.
1998-07-01
Quantification of brain structure is important for evaluating changes in brain size with growth and aging and for characterizing neurodegeneration disorders. Previous quantification efforts using ex vivo techniques suffered considerable error due to shrinkage of the cerebrum after extraction from the skull, deformation of slices during sectioning, and numerous other factors. In vivo imaging studies of brain anatomy avoid these problems and allow repetitive studies following progression of brain structure changes due to disease or natural processes. We have developed a methodology for obtaining triangular mesh models of the cortical surface from MRI brain datasets. The cortex is segmented from nonbrain tissue using a 2D region-growing technique combined with occasional manual edits. Once segmented, thresholding and image morphological operations (erosions and openings) are used to expose the regions between adjacent surfaces in deep cortical folds. A 2D region- following procedure is then used to find a set of contours outlining the cortical boundary on each slice. The contours on all slices are tiled together to form a closed triangular mesh model approximating the cortical surface. This model can be used for calculation of cortical surface area and volume, as well as other parameters of interest. Except for the initial segmentation of the cortex from the skull, the technique is automatic and requires only modest computation time on modern workstations. Though the use of image data avoids many of the pitfalls of ex vivo and sectioning techniques, our MRI-based technique is still vulnerable to errors that may impact the accuracy of estimated brain structure parameters. Potential inaccuracies include segmentation errors due to incorrect thresholding, missed deep sulcal surfaces, falsely segmented holes due to image noise and surface tiling artifacts. The focus of this paper is the characterization of these errors and how they affect measurements of cortical surface
Sensitivity analysis of the meteorological model applied in the German risk study (DRS)
International Nuclear Information System (INIS)
Vogt, S.
1982-01-01
In the first part of this paper it will be shown how the influence of uncertainties in estimation on risk statements is determined using methods of the probability theory. In particular the parameters contained in the dispersion model are studied more thoroughly. In the second part, based on the knowledge gathered in the previous investigations, new and more realistic best estimate values will be proposed for four selected parameters to be used in future work. The modifications in the risk statements by these new parameter values will be commented upon
GLOBAL RANDOM WALK SIMULATIONS FOR SENSITIVITY AND UNCERTAINTY ANALYSIS OF PASSIVE TRANSPORT MODELS
Directory of Open Access Journals (Sweden)
Nicolae Suciu
2011-07-01
Full Text Available The Global Random Walk algorithm (GRW performs a simultaneoustracking on a fixed grid of huge numbers of particles at costscomparable to those of a single-trajectory simulation by the traditional Particle Tracking (PT approach. Statistical ensembles of GRW simulations of a typical advection-dispersion process in groundwater systems with randomly distributed spatial parameters are used to obtain reliable estimations of the input parameters for the upscaled transport model and of their correlations, input-output correlations, as well as full probability distributions of the input and output parameters.
Directory of Open Access Journals (Sweden)
Nan-Hung Hsieh
2018-06-01
Full Text Available Traditionally, the solution to reduce parameter dimensionality in a physiologically-based pharmacokinetic (PBPK model is through expert judgment. However, this approach may lead to bias in parameter estimates and model predictions if important parameters are fixed at uncertain or inappropriate values. The purpose of this study was to explore the application of global sensitivity analysis (GSA to ascertain which parameters in the PBPK model are non-influential, and therefore can be assigned fixed values in Bayesian parameter estimation with minimal bias. We compared the elementary effect-based Morris method and three variance-based Sobol indices in their ability to distinguish “influential” parameters to be estimated and “non-influential” parameters to be fixed. We illustrated this approach using a published human PBPK model for acetaminophen (APAP and its two primary metabolites APAP-glucuronide and APAP-sulfate. We first applied GSA to the original published model, comparing Bayesian model calibration results using all the 21 originally calibrated model parameters (OMP, determined by “expert judgment”-based approach vs. the subset of original influential parameters (OIP, determined by GSA from the OMP. We then applied GSA to all the PBPK parameters, including those fixed in the published model, comparing the model calibration results using this full set of 58 model parameters (FMP vs. the full set influential parameters (FIP, determined by GSA from FMP. We also examined the impact of different cut-off points to distinguish the influential and non-influential parameters. We found that Sobol indices calculated by eFAST provided the best combination of reliability (consistency with other variance-based methods and efficiency (lowest computational cost to achieve convergence in identifying influential parameters. We identified several originally calibrated parameters that were not influential, and could be fixed to improve computational
Directory of Open Access Journals (Sweden)
Y. Y. Yu
2013-01-01
Full Text Available To accurately estimate past terrestrial carbon pools is the key to understanding the global carbon cycle and its relationship with the climate system. SoilGen2 is a useful tool to obtain aspects of soil properties (including carbon content by simulating soil formation processes; thus it offers an opportunity for both past soil carbon pool reconstruction and future carbon pool prediction. In order to apply it to various environmental conditions, parameters related to carbon cycle process in SoilGen2 are calibrated based on six soil pedons from two typical loess deposition regions (Belgium and China. Sensitivity analysis using the Morris method shows that decomposition rate of humus (k_{HUM}, fraction of incoming plant material as leaf litter (fr_{ecto} and decomposition rate of resistant plant material (k_{RPM} are the three most sensitive parameters that would cause the greatest uncertainty in simulated change of soil organic carbon in both regions. According to the principle of minimizing the difference between simulated and measured organic carbon by comparing quality indices, the suited values of k_{HUM}, (fr_{ecto} and k_{RPM} in the model are deduced step by step and validated for independent soil pedons. The difference of calibrated parameters between Belgium and China may be attributed to their different vegetation types and climate conditions. This calibrated model allows more accurate simulation of carbon change in the whole pedon and has potential for future modeling of carbon cycle over long timescales.
Data fusion qualitative sensitivity analysis
International Nuclear Information System (INIS)
Clayton, E.A.; Lewis, R.E.
1995-09-01
Pacific Northwest Laboratory was tasked with testing, debugging, and refining the Hanford Site data fusion workstation (DFW), with the assistance of Coleman Research Corporation (CRC), before delivering the DFW to the environmental restoration client at the Hanford Site. Data fusion is the mathematical combination (or fusion) of disparate data sets into a single interpretation. The data fusion software used in this study was developed by CRC. The data fusion software developed by CRC was initially demonstrated on a data set collected at the Hanford Site where three types of data were combined. These data were (1) seismic reflection, (2) seismic refraction, and (3) depth to geologic horizons. The fused results included a contour map of the top of a low-permeability horizon. This report discusses the results of a sensitivity analysis of data fusion software to variations in its input parameters. The data fusion software developed by CRC has a large number of input parameters that can be varied by the user and that influence the results of data fusion. Many of these parameters are defined as part of the earth model. The earth model is a series of 3-dimensional polynomials with horizontal spatial coordinates as the independent variables and either subsurface layer depth or values of various properties within these layers (e.g., compression wave velocity, resistivity) as the dependent variables
Zhang, Wei; Cho, Chongdu; Piao, Changhao; Choi, Hojoon
2016-01-01
This paper presents a novel method for identifying the main parameters affecting the stress distribution of the components used in assembly modeling of proton exchange membrane fuel cell (PEMFC) stack. This method is a combination of an approximation model and Sobol's method, which allows a fast global sensitivity analysis for a set of uncertain parameters using only a limited number of calculations. Seven major parameters, i.e., Young's modulus of the end plate and the membrane electrode assembly (MEA), the contact stiffness between the MEA and bipolar plate (BPP), the X and Y positions of the bolts, the pressure of each bolt, and the thickness of the end plate, are investigated regarding their effect on four metrics, i.e., the maximum stresses of the MEA, BPP, and end plate, and the stress distribution percentage of the MEA. The analysis reveals the individual effects of each parameter and its interactions with the other parameters. The results show that the X position of a bolt has a major influence on the maximum stresses of the BPP and end plate, whereas the thickness of the end plate has the strongest effect on both the maximum stress and the stress distribution percentage of the MEA.
Tonini, R.; Maesano, F. E.; Tiberti, M. M.; Romano, F.; Scala, A.; Lorito, S.; Volpe, M.; Basili, R.
2017-12-01
The geometry of seismogenic sources could be one of the most important factors concurring to control the generation and the propagation of earthquake-generated tsunamis and their effects on the coasts. Since the majority of potentially tsunamigenic earthquakes occur offshore, the corresponding faults are generally poorly constrained and, consequently, their geometry is often oversimplified as a planar fault. The rupture area of mega-thrust earthquakes in subduction zones, where most of the greatest tsunamis have occurred, extends for tens to hundreds of kilometers both down dip and along strike, and generally deviates from the planar geometry. Therefore, the larger the earthquake size is, the weaker the planar fault assumption become. In this work, we present a sensitivity analysis aimed to explore the effects on modeled tsunamis generated by seismic sources with different degrees of geometric complexities. We focused on the Calabrian subduction zone, located in the Mediterranean Sea, which is characterized by the convergence between the African and European plates, with rates of up to 5 mm/yr. This subduction zone has been considered to have generated some past large earthquakes and tsunamis, despite it shows only in-slab significant seismic activity below 40 km depth and no relevant seismicity in the shallower portion of the interface. Our analysis is performed by defining and modeling an exhaustive set of tsunami scenarios located in the Calabrian subduction and using different models of the subduction interface with increasing geometrical complexity, from a planar surface to a highly detailed 3D surface. The latter was obtained from the interpretation of a dense network of seismic reflection profiles coupled with the analysis of the seismicity distribution. The more relevant effects due to the inclusion of 3D complexities in the seismic source geometry are finally highlighted in terms of the resulting tsunami impact.
Energy Technology Data Exchange (ETDEWEB)
Rankinen, K.; Granlund, K. [Finnish Environmental Inst., Helsinki (Finland); Futter, M. N. [Swedish Univ. of Agricultural Sciences, Uppsala (Sweden)
2013-11-01
The semi-distributed, dynamic INCA-N model was used to simulate the behaviour of dissolved inorganic nitrogen (DIN) in two Finnish research catchments. Parameter sensitivity and model structural uncertainty were analysed using generalized sensitivity analysis. The Mustajoki catchment is a forested upstream catchment, while the Savijoki catchment represents intensively cultivated lowlands. In general, there were more influential parameters in Savijoki than Mustajoki. Model results were sensitive to N-transformation rates, vegetation dynamics, and soil and river hydrology. Values of the sensitive parameters were based on long-term measurements covering both warm and cold years. The highest measured DIN concentrations fell between minimum and maximum values estimated during the uncertainty analysis. The lowest measured concentrations fell outside these bounds, suggesting that some retention processes may be missing from the current model structure. The lowest concentrations occurred mainly during low flow periods; so effects on total loads were small. (orig.)
Berberich, Julian; Dietrich, Johannes W; Hoermann, Rudolf; Müller, Matthias A
2018-01-01
Despite significant progress in assay technology, diagnosis of functional thyroid disorders may still be a challenge, as illustrated by the vague upper limit of the reference range for serum thyrotropin ( TSH ). Diagnostical problems also apply to subjects affected by syndrome T, i.e., those 10% of hypothyroid patients who continue to suffer from poor quality of life despite normal TSH concentrations under substitution therapy with levothyroxine ( L - T 4 ). In this paper, we extend a mathematical model of the pituitary-thyroid feedback loop in order to improve the understanding of thyroid hormone homeostasis. In particular, we incorporate a TSH - T 3 -shunt inside the thyroid, whose existence has recently been demonstrated in several clinical studies. The resulting extended model shows good accordance with various clinical observations, such as a circadian rhythm in free peripheral triiodothyronine ( FT 3 ). Furthermore, we perform a sensitivity analysis of the derived model, revealing the dependence of TSH and hormone concentrations on different system parameters. The results have implications for clinical interpretation of thyroid tests, e.g., in the differential diagnosis of subclinical hypothyroidism.
Directory of Open Access Journals (Sweden)
Julian Berberich
2018-03-01
Full Text Available Despite significant progress in assay technology, diagnosis of functional thyroid disorders may still be a challenge, as illustrated by the vague upper limit of the reference range for serum thyrotropin (TSH. Diagnostical problems also apply to subjects affected by syndrome T, i.e., those 10% of hypothyroid patients who continue to suffer from poor quality of life despite normal TSH concentrations under substitution therapy with levothyroxine (L-T4. In this paper, we extend a mathematical model of the pituitary–thyroid feedback loop in order to improve the understanding of thyroid hormone homeostasis. In particular, we incorporate a TSH-T3-shunt inside the thyroid, whose existence has recently been demonstrated in several clinical studies. The resulting extended model shows good accordance with various clinical observations, such as a circadian rhythm in free peripheral triiodothyronine (FT3. Furthermore, we perform a sensitivity analysis of the derived model, revealing the dependence of TSH and hormone concentrations on different system parameters. The results have implications for clinical interpretation of thyroid tests, e.g., in the differential diagnosis of subclinical hypothyroidism.
Berberich, Julian; Dietrich, Johannes W.; Hoermann, Rudolf; Müller, Matthias A.
2018-01-01
Despite significant progress in assay technology, diagnosis of functional thyroid disorders may still be a challenge, as illustrated by the vague upper limit of the reference range for serum thyrotropin (TSH). Diagnostical problems also apply to subjects affected by syndrome T, i.e., those 10% of hypothyroid patients who continue to suffer from poor quality of life despite normal TSH concentrations under substitution therapy with levothyroxine (L-T4). In this paper, we extend a mathematical model of the pituitary–thyroid feedback loop in order to improve the understanding of thyroid hormone homeostasis. In particular, we incorporate a TSH-T3-shunt inside the thyroid, whose existence has recently been demonstrated in several clinical studies. The resulting extended model shows good accordance with various clinical observations, such as a circadian rhythm in free peripheral triiodothyronine (FT3). Furthermore, we perform a sensitivity analysis of the derived model, revealing the dependence of TSH and hormone concentrations on different system parameters. The results have implications for clinical interpretation of thyroid tests, e.g., in the differential diagnosis of subclinical hypothyroidism. PMID:29619006
Aleksankina, Ksenia; Heal, Mathew R.; Dore, Anthony J.; Van Oijen, Marcel; Reis, Stefan
2018-04-01
Atmospheric chemistry transport models (ACTMs) are widely used to underpin policy decisions associated with the impact of potential changes in emissions on future pollutant concentrations and deposition. It is therefore essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input pollutant emissions. ACTMs incorporate complex and non-linear descriptions of chemical and physical processes which means that interactions and non-linearities in input-output relationships may not be revealed through the local one-at-a-time sensitivity analysis typically used. The aim of this work is to demonstrate a global sensitivity and uncertainty analysis approach for an ACTM, using as an example the FRAME model, which is extensively employed in the UK to generate source-receptor matrices for the UK Integrated Assessment Model and to estimate critical load exceedances. An optimised Latin hypercube sampling design was used to construct model runs within ±40 % variation range for the UK emissions of SO2, NOx, and NH3, from which regression coefficients for each input-output combination and each model grid ( > 10 000 across the UK) were calculated. Surface concentrations of SO2, NOx, and NH3 (and of deposition of S and N) were found to be predominantly sensitive to the emissions of the respective pollutant, while sensitivities of secondary species such as HNO3 and particulate SO42-, NO3-, and NH4+ to pollutant emissions were more complex and geographically variable. The uncertainties in model output variables were propagated from the uncertainty ranges reported by the UK National Atmospheric Emissions Inventory for the emissions of SO2, NOx, and NH3 (±4, ±10, and ±20 % respectively). The uncertainties in the surface concentrations of NH3 and NOx and the depositions of NHx and NOy were dominated by the uncertainties in emissions of NH3, and NOx respectively, whilst concentrations of SO2 and deposition of SOy were affected
High order depletion sensitivity analysis
International Nuclear Information System (INIS)
Naguib, K.; Adib, M.; Morcos, H.N.
2002-01-01
A high order depletion sensitivity method was applied to calculate the sensitivities of build-up of actinides in the irradiated fuel due to cross-section uncertainties. An iteration method based on Taylor series expansion was applied to construct stationary principle, from which all orders of perturbations were calculated. The irradiated EK-10 and MTR-20 fuels at their maximum burn-up of 25% and 65% respectively were considered for sensitivity analysis. The results of calculation show that, in case of EK-10 fuel (low burn-up), the first order sensitivity was found to be enough to perform an accuracy of 1%. While in case of MTR-20 (high burn-up) the fifth order was found to provide 3% accuracy. A computer code SENS was developed to provide the required calculations
Fate and transport of mercury in soil systems : a numerical model in HP1 and sensitivity analysis
Leterme, Bertrand; Jacques, Diederik
2013-04-01
demethylation was not implemented, because it could be neglected in an oxidising environment. However, if the model is to be tested in more reducing conditions (e.g. shallow groundwater table), methyl- and dimethylmercury formation can be non negligible. Using 50 year time series of daily weather observations in Dessel (Belgium) and a typical sandy soil with deep groundwater (free drainage, oxic conditions), a sensitivity analysis was performed to assess the relative importance of processes and parameters within the model. We used the elementary effects method (Morris, 1991; Campolongo et al., 2007), which draws trajectories across the parameter space to derive information on the global sensitivity of the selected input parameters. The impact of different initial contamination phases (solid, NAPL, aqueous and combinations of these) was also tested. Simulation results are presented in terms of (i) Hg volatilized to the atmosphere; (ii) Hg leached out of the soil profile; (iii) Hg still present in the soil horizon originally polluted; and (iv) Hg still present in the soil profile but below the original contaminated horizon. Processes and parameters identified as critical based on the sensitivity analysis differ from one scenario to the other ; depending on pollution type (cinnabar, NAPL, aqueous Hg), on the indicator assessed and on time (after 5, 25 or 50 years). However, in general DOM in soil water was the most critical parameter. Other important parameters were those related to Hg sorption on SOM (thiols, and humic and fulvic acids), and to Hg complexation with DOM. Initial Hg concentration was also often identified as a sensitive parameter. Interactions between factors and non linear effects as measured by the elementary effect method were generally important, but also dependent on the type of contamination and on time. No model calibration was performed until now. The numerical tool could greatly benefit from partial model calibration and/or validation. Ideally, detailed
Applications of advances in nonlinear sensitivity analysis
Energy Technology Data Exchange (ETDEWEB)
Werbos, P J
1982-01-01
The following paper summarizes the major properties and applications of a collection of algorithms involving differentiation and optimization at minimum cost. The areas of application include the sensitivity analysis of models, new work in statistical or econometric estimation, optimization, artificial intelligence and neuron modelling.
Jiménez-Murcia, Susana; Fernández-Aranda, Fernando; Mestre-Bach, Gemma; Granero, Roser; Tárrega, Salomé; Torrubia, Rafael; Aymamí, Neus; Gómez-Peña, Mónica; Soriano-Mas, Carles; Steward, Trevor; Moragas, Laura; Baño, Marta; Del Pino-Gutiérrez, Amparo; Menchón, José M
2017-06-01
Most individuals will gamble during their lifetime, yet only a select few will develop gambling disorder. Gray's Reinforcement Sensitivity Theory holds promise for providing insight into gambling disorder etiology and symptomatology as it ascertains that neurobiological differences in reward and punishment sensitivity play a crucial role in determining an individual's affect and motives. The aim of the study was to assess a mediational pathway, which included patients' sex, personality traits, reward and punishment sensitivity, and gambling-severity variables. The Sensitivity to Punishment and Sensitivity to Reward Questionnaire, the South Oaks Gambling Screen, the Symptom Checklist-Revised, and the Temperament and Character Inventory-Revised were administered to a sample of gambling disorder outpatients (N = 831), diagnosed according to DSM-5 criteria, attending a specialized outpatient unit. Sociodemographic variables were also recorded. A structural equation model found that both reward and punishment sensitivity were positively and directly associated with increased gambling severity, sociodemographic variables, and certain personality traits while also revealing a complex mediational role for these dimensions. To this end, our findings suggest that the Sensitivity to Punishment and Sensitivity to Reward Questionnaire could be a useful tool for gaining a better understanding of different gambling disorder phenotypes and developing tailored interventions.
Ethical sensitivity in professional practice: concept analysis.
Weaver, Kathryn; Morse, Janice; Mitcham, Carl
2008-06-01
This paper is a report of a concept analysis of ethical sensitivity. Ethical sensitivity enables nurses and other professionals to respond morally to the suffering and vulnerability of those receiving professional care and services. Because of its significance to nursing and other professional practices, ethical sensitivity deserves more focused analysis. A criteria-based method oriented toward pragmatic utility guided the analysis of 200 papers and books from the fields of nursing, medicine, psychology, dentistry, clinical ethics, theology, education, law, accounting or business, journalism, philosophy, political and social sciences and women's studies. This literature spanned 1970 to 2006 and was sorted by discipline and concept dimensions and examined for concept structure and use across various contexts. The analysis was completed in September 2007. Ethical sensitivity in professional practice develops in contexts of uncertainty, client suffering and vulnerability, and through relationships characterized by receptivity, responsiveness and courage on the part of professionals. Essential attributes of ethical sensitivity are identified as moral perception, affectivity and dividing loyalties. Outcomes include integrity preserving decision-making, comfort and well-being, learning and professional transcendence. Our findings promote ethical sensitivity as a type of practical wisdom that pursues client comfort and professional satisfaction with care delivery. The analysis and resulting model offers an inclusive view of ethical sensitivity that addresses some of the limitations with prior conceptualizations.
Schwarz, Massimiliano; Cohen, Denis
2017-04-01
Morphology and extent of hydrological pathways, in combination with the spatio-temporal variability of rainfall events and the heterogeneities of hydro-mechanical properties of soils, has a major impact on the hydrological conditions that locally determine the triggering of shallow landslides. The coupling of these processes at different spatial scales is an enormous challenge for slope stability modeling at the catchment scale. In this work we present a sensitivity analysis of a new dual-porosity hydrological model implemented in the hydro-mechanical model SOSlope for the modeling of shallow landslides on vegetated hillslopes. The proposed model links the calculation of the saturation dynamic of preferential flow-paths based on hydrological and topographical characteristics of the landscape to the hydro-mechanical behavior of the soil along a potential failure surface due to the changes of soil matrix saturation. Furthermore, the hydro-mechanical changes of soil conditions are linked to the local stress-strain properties of the (rooted-)soil that ultimately determine the force redistribution and related deformations at the hillslope scale. The model considers forces to be redistributed through three types of solicitations: tension, compression, and shearing. The present analysis shows how the conditions of deformation due to the passive earth pressure mobilized at the toe of the landslide are particularly important in defining the timing and extension of shallow landslides. The model also shows that, in densely rooted hillslopes, lateral force redistribution under tension through the root-network may substantially contribute to stabilizing slopes, avoiding crack formation and large deformations. The results of the sensitivity analysis are discussed in the context of protection forest management and bioengineering techniques.
International Nuclear Information System (INIS)
Marrel, A.
2008-01-01
In the studies of environmental transfer and risk assessment, numerical models are used to simulate, understand and predict the transfer of pollutant. These computer codes can depend on a high number of uncertain input parameters (geophysical variables, chemical parameters, etc.) and can be often too computer time expensive. To conduct uncertainty propagation studies and to measure the importance of each input on the response variability, the computer code has to be approximated by a meta model which is build on an acceptable number of simulations of the code and requires a negligible calculation time. We focused our research work on the use of Gaussian process meta model to make the sensitivity analysis of the code. We proposed a methodology with estimation and input selection procedures in order to build the meta model in the case of a high number of inputs and with few simulations available. Then, we compared two approaches to compute the sensitivity indices with the meta model and proposed an algorithm to build prediction intervals for these indices. Afterwards, we were interested in the choice of the code simulations. We studied the influence of different sampling strategies on the predictiveness of the Gaussian process meta model. Finally, we extended our statistical tools to a functional output of a computer code. We combined a decomposition on a wavelet basis with the Gaussian process modelling before computing the functional sensitivity indices. All the tools and statistical methodologies that we developed were applied to the real case of a complex hydrogeological computer code, simulating radionuclide transport in groundwater. (author) [fr
Fremier, A. K.; Estrada Carmona, N.; Harper, E.; DeClerck, F.
2011-12-01
Appropriate application of complex models to estimate system behavior requires understanding the influence of model structure and parameter estimates on model output. To date, most researchers perform local sensitivity analyses, rather than global, because of computational time and quantity of data produced. Local sensitivity analyses are limited in quantifying the higher order interactions among parameters, which could lead to incomplete analysis of model behavior. To address this concern, we performed a GSA on a commonly applied equation for soil loss - the Revised Universal Soil Loss Equation. USLE is an empirical model built on plot-scale data from the USA and the Revised version (RUSLE) includes improved equations for wider conditions, with 25 parameters grouped into six factors to estimate long-term plot and watershed scale soil loss. Despite RUSLE's widespread application, a complete sensitivity analysis has yet to be performed. In this research, we applied a GSA to plot and watershed scale data from the US and Costa Rica to parameterize the RUSLE in an effort to understand the relative importance of model factors and parameters across wide environmental space. We analyzed the GSA results using Random Forest, a statistical approach to evaluate parameter importance accounting for the higher order interactions, and used Classification and Regression Trees to show the dominant trends in complex interactions. In all GSA calculations the management of cover crops (C factor) ranks the highest among factors (compared to rain-runoff erosivity, topography, support practices, and soil erodibility). This is counter to previous sensitivity analyses where the topographic factor was determined to be the most important. The GSA finding is consistent across multiple model runs, including data from the US, Costa Rica, and a synthetic dataset of the widest theoretical space. The three most important parameters were: Mass density of live and dead roots found in the upper inch
Huberts, W; de Jonge, C; van der Linden, W P M; Inda, M A; Passera, K; Tordoir, J H M; van de Vosse, F N; Bosboom, E M H
2013-06-01
Decision-making in vascular access surgery for hemodialysis can be supported by a pulse wave propagation model that is able to simulate pressure and flow changes induced by the creation of a vascular access. To personalize such a model, patient-specific input parameters should be chosen. However, the number of input parameters that can be measured in clinical routine is limited. Besides, patient data are compromised with uncertainty. Incomplete and uncertain input data will result in uncertainties in model predictions. In part A, we analyzed how the measurement uncertainty in the input propagates to the model output by means of a sensitivity analysis. Of all 73 input parameters, 16 parameters were identified to be worthwhile to measure more accurately and 51 could be fixed within their measurement uncertainty range, but these latter parameters still needed to be measured. Here, we present a methodology for assessing the model input parameters that can be taken constant and therefore do not need to be measured. In addition, a method to determine the value of this parameter is presented. For the pulse wave propagation model applied to vascular access surgery, six patient-specific datasets were analyzed and it was found that 47 out of 73 parameters can be fixed on a generic value. These model parameters are not important for personalization of the wave propagation model. Furthermore, we were able to determine a generic value for 37 of the 47 fixable model parameters. Copyright © 2012 IPEM. Published by Elsevier Ltd. All rights reserved.
A predictive mathematical model was developed to simulate heat transfer in a tomato undergoing double sided infrared (IR) heating in a dry-peeling process. The aims of this study were to validate the developed model using experimental data and to investigate different engineering parameters that mos...
Directory of Open Access Journals (Sweden)
George P. Petropoulos
2015-05-01
Full Text Available In today’s changing climate, the development of robust, accurate and globally applicable models is imperative for a wider understanding of Earth’s terrestrial biosphere. Moreover, an understanding of the representation, sensitivity and coherence of such models are vital for the operationalisation of any physically based model. A Global Sensitivity Analysis (GSA was conducted on the SimSphere land biosphere model in which a meta-modelling method adopting Bayesian theory was implemented. Initially, effects of assuming uniform probability distribution functions (PDFs for the model inputs, when examining sensitivity of key quantities simulated by SimSphere at different output times, were examined. The development of topographic model input parameters (e.g., slope, aspect, and elevation were derived within a Geographic Information System (GIS before implementation within the model. The effect of time of the simulation on the sensitivity of previously examined outputs was also analysed. Results showed that simulated outputs were significantly influenced by changes in topographic input parameters, fractional vegetation cover, vegetation height and surface moisture availability in agreement with previous studies. Time of model output simulation had a significant influence on the absolute values of the output variance decomposition, but it did not seem to change the relative importance of each input parameter. Sensitivity Analysis (SA results of the newly modelled outputs allowed identification of the most responsive model inputs and interactions. Our study presents an important step forward in SimSphere verification given the increasing interest in its use both as an independent modelling and educational tool. Furthermore, this study is very timely given on-going efforts towards the development of operational products based on the synergy of SimSphere with Earth Observation (EO data. In this context, results also provide additional support for the
Contributions to sensitivity analysis and generalized discriminant analysis
International Nuclear Information System (INIS)
Jacques, J.
2005-12-01
Two topics are studied in this thesis: sensitivity analysis and generalized discriminant analysis. Global sensitivity analysis of a mathematical model studies how the output variables of this last react to variations of its inputs. The methods based on the study of the variance quantify the part of variance of the response of the model due to each input variable and each subset of input variables. The first subject of this thesis is the impact of a model uncertainty on results of a sensitivity analysis. Two particular forms of uncertainty are studied: that due to a change of the model of reference, and that due to the use of a simplified model with the place of the model of reference. A second problem was studied during this thesis, that of models with correlated inputs. Indeed, classical sensitivity indices not having significance (from an interpretation point of view) in the presence of correlation of the inputs, we propose a multidimensional approach consisting in expressing the sensitivity of the output of the model to groups of correlated variables. Applications in the field of nuclear engineering illustrate this work. Generalized discriminant analysis consists in classifying the individuals of a test sample in groups, by using information contained in a training sample, when these two samples do not come from the same population. This work extends existing methods in a Gaussian context to the case of binary data. An application in public health illustrates the utility of generalized discrimination models thus defined. (author)
Directory of Open Access Journals (Sweden)
W. Zhang
2012-03-01
Full Text Available The high-order decoupled direct method in three dimensions for particulate matter (HDDM-3D/PM has been implemented in the Community Multiscale Air Quality (CMAQ model to enable advanced sensitivity analysis. The major effort of this work is to develop high-order DDM sensitivity analysis of ISORROPIA, the inorganic aerosol module of CMAQ. A case-specific approach has been applied, and the sensitivities of activity coefficients and water content are explicitly computed. Stand-alone tests are performed for ISORROPIA by comparing the sensitivities (first- and second-order computed by HDDM and the brute force (BF approximations. Similar comparison has also been carried out for CMAQ sensitivities simulated using a week-long winter episode for a continental US domain. Second-order sensitivities of aerosol species (e.g., sulfate, nitrate, and ammonium with respect to domain-wide SO_{2}, NO_{x}, and NH_{3} emissions show agreement with BF results, yet exhibit less noise in locations where BF results are demonstrably inaccurate. Second-order sensitivity analysis elucidates poorly understood nonlinear responses of secondary inorganic aerosols to their precursors and competing species. Adding second-order sensitivity terms to the Taylor series projection of the nitrate concentrations with a 50% reduction in domain-wide NO_{x} or SO_{2} emissions rates improves the prediction with statistical significance.
International Nuclear Information System (INIS)
Deman, G.; Konakli, K.; Sudret, B.; Kerrou, J.; Perrochet, P.; Benabderrahmane, H.
2016-01-01
The study makes use of polynomial chaos expansions to compute Sobol' indices within the frame of a global sensitivity analysis of hydro-dispersive parameters in a simplified vertical cross-section of a segment of the subsurface of the Paris Basin. Applying conservative ranges, the uncertainty in 78 input variables is propagated upon the mean lifetime expectancy of water molecules departing from a specific location within a highly confining layer situated in the middle of the model domain. Lifetime expectancy is a hydrogeological performance measure pertinent to safety analysis with respect to subsurface contaminants, such as radionuclides. The sensitivity analysis indicates that the variability in the mean lifetime expectancy can be sufficiently explained by the uncertainty in the petrofacies, i.e. the sets of porosity and hydraulic conductivity, of only a few layers of the model. The obtained results provide guidance regarding the uncertainty modeling in future investigations employing detailed numerical models of the subsurface of the Paris Basin. Moreover, the study demonstrates the high efficiency of sparse polynomial chaos expansions in computing Sobol' indices for high-dimensional models. - Highlights: • Global sensitivity analysis of a 2D 15-layer groundwater flow model is conducted. • A high-dimensional random input comprising 78 parameters is considered. • The variability in the mean lifetime expectancy for the central layer is examined. • Sparse polynomial chaos expansions are used to compute Sobol' sensitivity indices. • The petrofacies of a few layers can sufficiently explain the response variance.
Huberts, W; de Jonge, C; van der Linden, W P M; Inda, M A; Tordoir, J H M; van de Vosse, F N; Bosboom, E M H
2013-06-01
Previously, a pulse wave propagation model was developed that has potential in supporting decision-making in arteriovenous fistula (AVF) surgery for hemodialysis. To adapt the wave propagation model to personalized conditions, patient-specific input parameters should be available. In clinics, the number of measurable input parameters is limited which results in sparse datasets. In addition, patient data are compromised with uncertainty. These uncertain and incomplete input datasets will result in model output uncertainties. By means of a sensitivity analysis the propagation of input uncertainties into output uncertainty can be studied which can give directions for input measurement improvement. In this study, a computational framework has been developed to perform such a sensitivity analysis with a variance-based method and Monte Carlo simulations. The framework was used to determine the influential parameters of our pulse wave propagation model applied to AVF surgery, with respect to parameter prioritization and parameter fixing. With this we were able to determine the model parameters that have the largest influence on the predicted mean brachial flow and systolic radial artery pressure after AVF surgery. Of all 73 parameters 51 could be fixed within their measurement uncertainty interval without significantly influencing the output, while 16 parameters importantly influence the output uncertainty. Measurement accuracy improvement should thus focus on these 16 influential parameters. The most rewarding are measurement improvements of the following parameters: the mean aortic flow, the aortic windkessel resistance, the parameters associated with the smallest arterial or venous diameters of the AVF in- and outflow tract and the radial artery windkessel compliance. Copyright © 2012 IPEM. Published by Elsevier Ltd. All rights reserved.
Fujimura, Kazumasa; Iseri, Yoshihiko; Kanae, Shinjiro; Murakami, Masahiro
2014-05-01
Accurate estimation of low flow can contribute to better water resources management and also lead to more reliable evaluation of climate change impacts on water resources. In the early study, the nonlinearity of low flow related to the storage in the basin was suggested by Horton (1937) as the exponential function of Q=KSN, where Q is the discharge, S is the storage, K is a constant and N is the exponent value. In the recent study by Ding (2011) showed the general storage-discharge equation of Q = KNSN. Since the constant K is defined as the fractional recession constant and symbolized as Au by Ando et al. (1983), in this study, we rewrite this equation as Qg=AuNSgN, where Qg is the groundwater runoff and Sg is the groundwater storage. Although this equation was applied to a short-term runoff event of less than 14 hours using the unit hydrograph method by Ding, it was not yet applied for a long-term runoff event including low flow more than 10 years. This study performed a sensitive analysis of two parameters of the constant Au and exponent value N by using the hourly hydrological model for two mountainous basins in Japan. The hourly hydrological model used in this study was presented by Fujimura et al. (2012), which comprise the Diskin-Nazimov infiltration model, groundwater recharge and groundwater runoff calculations, and a direct runoff component. The study basins are the Sameura Dam basin (SAME basin) (472 km2) located in the western Japan which has variability of rainfall, and the Shirakawa Dam basin (SIRA basin) (205km2) located in a region of heavy snowfall in the eastern Japan, that are different conditions of climate and geology. The period of available hourly data for the SAME basin is 20 years from 1 January 1991 to 31 December 2010, and for the SIRA basin is 10 years from 1 October 2003 to 30 September 2013. In the sensitive analysis, we prepared 19900 sets of the two parameters of Au and N, the Au value ranges from 0.0001 to 0.0100 in steps of 0
Harshan, Suraj
The main objective of the present thesis is the improvement of the TEB/ISBA (SURFEX) urban land surface model (ULSM) through comprehensive evaluation, sensitivity analysis, and optimization experiments using energy balance and radiative and air temperature data observed during 11 months at a tropical sub-urban site in Singapore. Overall the performance of the model is satisfactory, with a small underestimation of net radiation and an overestimation of sensible heat flux. Weaknesses in predicting the latent heat flux are apparent with smaller model values during daytime and the model also significantly underpredicts both the daytime peak and nighttime storage heat. Surface temperatures of all facets are generally overpredicted. Significant variation exists in the model behaviour between dry and wet seasons. The vegetation parametrization used in the model is inadequate to represent the moisture dynamics, producing unrealistically low latent heat fluxes during a particularly dry period. The comprehensive evaluation of the USLM shows the need for accurate estimation of input parameter values for present site. Since obtaining many of these parameters through empirical methods is not feasible, the present study employed a two step approach aimed at providing information about the most sensitive parameters and an optimized parameter set from model calibration. Two well established sensitivity analysis methods (global: Sobol and local: Morris) and a state-of-the-art multiobjective evolutionary algorithm (Borg) were employed for sensitivity analysis and parameter estimation. Experiments were carried out for three different weather periods. The analysis indicates that roof related parameters are the most important ones in controlling the behaviour of the sensible heat flux and net radiation flux, with roof and road albedo as the most influential parameters. Soil moisture initialization parameters are important in controlling the latent heat flux. The built (town) fraction
UMTS Common Channel Sensitivity Analysis
DEFF Research Database (Denmark)
Pratas, Nuno; Rodrigues, António; Santos, Frederico
2006-01-01
and as such it is necessary that both channels be available across the cell radius. This requirement makes the choice of the transmission parameters a fundamental one. This paper presents a sensitivity analysis regarding the transmission parameters of two UMTS common channels: RACH and FACH. Optimization of these channels...... is performed and values for the key transmission parameters in both common channels are obtained. On RACH these parameters are the message to preamble offset, the initial SIR target and the preamble power step while on FACH it is the transmission power offset....
USE OF SENSITIVITY ANALYSIS ON A PHYSIOLOGICALLY BASED PHARMACOKINETIC (PBPK) MODEL FOR CHLOROFORM IN RATS TO DETERMINE AGE-RELATED TOXICITY.CR Eklund, MV Evans, and JE Simmons. US EPA, ORD, NHEERL, ETD,PKB, Research Triangle Park, NC. Chloroform (CHCl3) is a disinfec...
Directory of Open Access Journals (Sweden)
John D. Hedley
2017-11-01
Full Text Available The capability for mapping two species of seagrass, Thalassia testudinium and Syringodium filiforme, by remote sensing using a physics based model inversion method was investigated. The model was based on a three-dimensional canopy model combined with a model for the overlying water column. The model included uncertainty propagation based on variation in leaf reflectances, canopy structure, water column properties, and the air-water interface. The uncertainty propagation enabled both a-priori predictive sensitivity analysis of potential capability and the generation of per-pixel error bars when applied to imagery. A primary aim of the work was to compare the sensitivity analysis to results achieved in a practical application using airborne hyperspectral data, to gain insight on the validity of sensitivity analyses in general. Results showed that while the sensitivity analysis predicted a weak but positive discrimination capability for species, in a practical application the relevant spectral differences were extremely small compared to discrepancies in the radiometric alignment of the model with the imagery—even though this alignment was very good. Complex interactions between spectral matching and uncertainty propagation also introduced biases. Ability to discriminate LAI was good, and comparable to previously published methods using different approaches. The main limitation in this respect was spatial alignment with the imagery with in situ data, which was heterogeneous on scales of a few meters. The results provide insight on the limitations of physics based inversion methods and seagrass mapping in general. Complex models can degrade unpredictably when radiometric alignment of the model and imagery is not perfect and incorporating uncertainties can have non-intuitive impacts on method performance. Sensitivity analyses are upper bounds to practical capability, incorporating a term for potential systematic errors in radiometric alignment may
Xia, Zhiye; Xu, Lisheng; Chen, Hongbin; Wang, Yongqian; Liu, Jinbao; Feng, Wenlan
2017-06-01
Extended range forecasting of 10-30 days, which lies between medium-term and climate prediction in terms of timescale, plays a significant role in decision-making processes for the prevention and mitigation of disastrous meteorological events. The sensitivity of initial error, model parameter error, and random error in a nonlinear crossprediction error (NCPE) model, and their stability in the prediction validity period in 10-30-day extended range forecasting, are analyzed quantitatively. The associated sensitivity of precipitable water, temperature, and geopotential height during cases of heavy rain and hurricane is also discussed. The results are summarized as follows. First, the initial error and random error interact. When the ratio of random error to initial error is small (10-6-10-2), minor variation in random error cannot significantly change the dynamic features of a chaotic system, and therefore random error has minimal effect on the prediction. When the ratio is in the range of 10-1-2 (i.e., random error dominates), attention should be paid to the random error instead of only the initial error. When the ratio is around 10-2-10-1, both influences must be considered. Their mutual effects may bring considerable uncertainty to extended range forecasting, and de-noising is therefore necessary. Second, in terms of model parameter error, the embedding dimension m should be determined by the factual nonlinear time series. The dynamic features of a chaotic system cannot be depicted because of the incomplete structure of the attractor when m is small. When m is large, prediction indicators can vanish because of the scarcity of phase points in phase space. A method for overcoming the cut-off effect ( m > 4) is proposed. Third, for heavy rains, precipitable water is more sensitive to the prediction validity period than temperature or geopotential height; however, for hurricanes, geopotential height is most sensitive, followed by precipitable water.
Sensitivity analysis in a structural reliability context
International Nuclear Information System (INIS)
Lemaitre, Paul
2014-01-01
This thesis' subject is sensitivity analysis in a structural reliability context. The general framework is the study of a deterministic numerical model that allows to reproduce a complex physical phenomenon. The aim of a reliability study is to estimate the failure probability of the system from the numerical model and the uncertainties of the inputs. In this context, the quantification of the impact of the uncertainty of each input parameter on the output might be of interest. This step is called sensitivity analysis. Many scientific works deal with this topic but not in the reliability scope. This thesis' aim is to test existing sensitivity analysis methods, and to propose more efficient original methods. A bibliographical step on sensitivity analysis on one hand and on the estimation of small failure probabilities on the other hand is first proposed. This step raises the need to develop appropriate techniques. Two variables ranking methods are then explored. The first one proposes to make use of binary classifiers (random forests). The second one measures the departure, at each step of a subset method, between each input original density and the density given the subset reached. A more general and original methodology reflecting the impact of the input density modification on the failure probability is then explored. The proposed methods are then applied on the CWNR case, which motivates this thesis. (author)
Lauvernet, Claire; Noll, Dorothea; Muñoz-Carpena, Rafael; Carluer, Nadia
2014-05-01
agricultural field and the VFS characteristics. These scenarios are based on: 2 types of climates (North and South-west of France), different rainfall intensities and durations, different lengths and slopes of hillslope, different humidity conditions, 4 soil types (silt loam, sandy loam, clay loam, sandy clay loam), 2 crops (wheat and corn) for the contributive area, 2 water table depths (1m and 2.5m) and 4 soil types for the VFS. The sizing method was applied for all these scenarios, and a sensitivity analysis of the VFS optimal length was performed for all the input parameters in order to understand their influence, and to identify for which a special care has to be given. Based on that sensitivity analysis, a metamodel has been developed. The idea is to simplify the whole toolchain and to make it possible to perform the buffer sizing by using a unique tool and a smaller set of parameters, given the available information from the end users. We first compared several mathematical methods to compute the metamodel, and then validated them on an agricultural watershed with real data in the North-West of France.
DEFF Research Database (Denmark)
Svensson, Elin; Borraccino, Antoine; Meyer Forsting, Alexander Raul
The sensitivity of nacelle lidar wind speed measurements to wind-induction models and lidar range configurations is studied using experimental data from the Nørrekær Enge (NKE) measurement campaign and simulated lidar data from Reynold-Averaged Navier Stokes (RANS) aerodynamic computational fluid...... the ZDM was configured to measure at five distances. From the configured distances, a large number of range configurations were created and systematically tested to determine the sensitivity of the reconstructed wind speeds to the number of ranges, minimum range and maximum range in the range......) of the fitting residuals. The results demonstrate that it is not possible to use RANS CFD simulated lidar data to determine optimal range configurations for real-time nacelle lidars due to their perfect (unrealistic) representation of the simulated flow field. The recommended range configurations are therefore...
Sensitivity analysis of the Gupta and Park chemical models on the heat flux by DSMC and CFD codes
Morsa, Luigi; Festa, Giandomenico; Zuppardi, Gennaro
2012-11-01
The present study is the logical continuation of a former paper by the first author in which the influence of the chemical models by Gupta and by Park on the computation of heat flux on the Orion and EXPERT capsules was evaluated. Tests were carried out by the direct simulation Monte Carlo code DS2V and by the computational fluiddynamic (CFD) code H3NS. DS2V implements the Gupta model, while H3NS implements the Park model. In order to compare the effects of the chemical models, the Park model was implemented also in DS2V. The results showed that DS2V and H3NS compute a different composition both in the flow field and on the surface, even using the same chemical model (Park). Furthermore DS2V computes, by the two chemical models, different compositions in the flow field but the same composition on the surface, therefore the same heat flux. In the present study, in order to evaluate the influence of these chemical models also in a CFD code, the Gupta and the Park models have been implemented in FLUENT. Tests by DS2V and by FLUENT, have been carried out for the EXPERT capsule at the altitude of 70 km and with velocity of 5000 m/s. The capsule experiences a hypersonic, continuum low density regime. Due to the energy level of the flow, the vibration equation, lacking in the original version of FLUENT, has been implemented. The results of the heat flux computation verify that FLUENT is quite sensitive to the Gupta and to the Park chemical models. In fact, at the stagnation point, the percentage difference between the models is about 13%. On the opposite the DS2V results by the two models are practically equivalent.
Li, Yi Zhe; Zhang, Ting Long; Liu, Qiu Yu; Li, Ying
2018-01-01
The ecological process models are powerful tools for studying terrestrial ecosystem water and carbon cycle at present. However, there are many parameters for these models, and weather the reasonable values of these parameters were taken, have important impact on the models simulation results. In the past, the sensitivity and the optimization of model parameters were analyzed and discussed in many researches. But the temporal and spatial heterogeneity of the optimal parameters is less concerned. In this paper, the BIOME-BGC model was used as an example. In the evergreen broad-leaved forest, deciduous broad-leaved forest and C3 grassland, the sensitive parameters of the model were selected by constructing the sensitivity judgment index with two experimental sites selected under each vegetation type. The objective function was constructed by using the simulated annealing algorithm combined with the flux data to obtain the monthly optimal values of the sensitive parameters at each site. Then we constructed the temporal heterogeneity judgment index, the spatial heterogeneity judgment index and the temporal and spatial heterogeneity judgment index to quantitatively analyze the temporal and spatial heterogeneity of the optimal values of the model sensitive parameters. The results showed that the sensitivity of BIOME-BGC model parameters was different under different vegetation types, but the selected sensitive parameters were mostly consistent. The optimal values of the sensitive parameters of BIOME-BGC model mostly presented time-space heterogeneity to different degrees which varied with vegetation types. The sensitive parameters related to vegetation physiology and ecology had relatively little temporal and spatial heterogeneity while those related to environment and phenology had generally larger temporal and spatial heterogeneity. In addition, the temporal heterogeneity of the optimal values of the model sensitive parameters showed a significant linear correlation
Rahman, Tanzina; Millwater, Harry; Shipley, Heather J
2014-11-15
Aluminum oxide nanoparticles have been widely used in various consumer products and there are growing concerns regarding their exposure in the environment. This study deals with the modeling, sensitivity analysis and uncertainty quantification of one-dimensional transport of nano-sized (~82 nm) aluminum oxide particles in saturated sand. The transport of aluminum oxide nanoparticles was modeled using a two-kinetic-site model with a blocking function. The modeling was done at different ionic strengths, flow rates, and nanoparticle concentrations. The two sites representing fast and slow attachments along with a blocking term yielded good agreement with the experimental results from the column studies of aluminum oxide nanoparticles. The same model was used to simulate breakthrough curves under different conditions using experimental data and calculated 95% confidence bounds of the generated breakthroughs. The sensitivity analysis results showed that slow attachment was the most sensitive parameter for high influent concentrations (e.g. 150 mg/L Al2O3) and the maximum solid phase retention capacity (related to blocking function) was the most sensitive parameter for low concentrations (e.g. 50 mg/L Al2O3). Copyright © 2014 Elsevier B.V. All rights reserved.
Singh, Jyotsna; Singh, Phool; Malik, Vikas
2017-01-01
Parkinson disease alters the information patterns in movement related pathways in brain. Experimental results performed on rats show that the activity patterns changes from single spike activity to mixed burst mode in Parkinson disease. However the cause of this change in activity pattern is not yet completely understood. Subthalamic nucleus is one of the main nuclei involved in the origin of motor dysfunction in Parkinson disease. In this paper, a single compartment conductance based model is considered which focuses on subthalamic nucleus and synaptic input from globus pallidus (external). This model shows highly nonlinear behavior with respect to various intrinsic parameters. Behavior of model has been presented with the help of activity patterns generated in healthy and Parkinson condition. These patterns have been compared by calculating their correlation coefficient for different values of intrinsic parameters. Results display that the activity patterns are very sensitive to various intrinsic parameters and calcium shows some promising results which provide insights into the motor dysfunction.
Bashkirtseva, Irina; Ryashko, Lev; Ryazanova, Tatyana
2018-01-01
A problem of mathematical modeling of complex stochastic processes in macroeconomics is discussed. For the description of dynamics of income and capital stock, the well-known Kaldor model of business cycles is used as a basic example. The aim of the paper is to give an overview of the variety of stochastic phenomena which occur in Kaldor model forced by additive and parametric random noise. We study a generation of small- and large-amplitude stochastic oscillations, and their mixed-mode intermittency. To analyze these phenomena, we suggest a constructive approach combining the study of the peculiarities of deterministic phase portrait, and stochastic sensitivity of attractors. We show how parametric noise can stabilize the unstable equilibrium and transform dynamics of Kaldor system from order to chaos.
Directory of Open Access Journals (Sweden)
Li Wang
2017-02-01
Full Text Available The ability to obtain appropriate parameters for an advanced pressurized water reactor (PWR unit model is of great significance for power system analysis. The attributes of that ability include the following: nonlinear relationships, long transition time, intercoupled parameters and difficult obtainment from practical test, posed complexity and difficult parameter identification. In this paper, a model and a parameter identification method for the PWR primary loop system were investigated. A parameter identification process was proposed, using a particle swarm optimization (PSO algorithm that is based on random perturbation (RP-PSO. The identification process included model variable initialization based on the differential equations of each sub-module and program setting method, parameter obtainment through sub-module identification in the Matlab/Simulink Software (Math Works Inc., Natick, MA, USA as well as adaptation analysis for an integrated model. A lot of parameter identification work was carried out, the results of which verified the effectiveness of the method. It was found that the change of some parameters, like the fuel temperature and coolant temperature feedback coefficients, changed the model gain, of which the trajectory sensitivities were not zero. Thus, obtaining their appropriate values had significant effects on the simulation results. The trajectory sensitivities of some parameters in the core neutron dynamic module were interrelated, causing the parameters to be difficult to identify. The model parameter sensitivity could be different, which would be influenced by the model input conditions, reflecting the parameter identifiability difficulty degree for various input conditions.
Hemmati, Reza; Saboori, Hedayat
2016-05-01
Energy storage systems (ESSs) have experienced a very rapid growth in recent years and are expected to be a promising tool in order to improving power system reliability and being economically efficient. The ESSs possess many potential benefits in various areas in the electric power systems. One of the main benefits of an ESS, especially a bulk unit, relies on smoothing the load pattern by decreasing on-peak and increasing off-peak loads, known as load leveling. These devices require new methods and tools in order to model and optimize their effects in the power system studies. In this respect, this paper will model bulk ESSs based on the several technical characteristics, introduce the proposed model in the thermal unit commitment (UC) problem, and analyze it with respect to the various sensitive parameters. The technical limitations of the thermal units and transmission network constraints are also considered in the model. The proposed model is a Mixed Integer Linear Programming (MILP) which can be easily solved by strong commercial solvers (for instance CPLEX) and it is appropriate to be used in the practical large scale networks. The results of implementing the proposed model on a test system reveal that proper load leveling through optimum storage scheduling leads to considerable operation cost reduction with respect to the storage system characteristics.
Hemmati, Reza; Saboori, Hedayat
2016-01-01
Energy storage systems (ESSs) have experienced a very rapid growth in recent years and are expected to be a promising tool in order to improving power system reliability and being economically efficient. The ESSs possess many potential benefits in various areas in the electric power systems. One of the main benefits of an ESS, especially a bulk unit, relies on smoothing the load pattern by decreasing on-peak and increasing off-peak loads, known as load leveling. These devices require new methods and tools in order to model and optimize their effects in the power system studies. In this respect, this paper will model bulk ESSs based on the several technical characteristics, introduce the proposed model in the thermal unit commitment (UC) problem, and analyze it with respect to the various sensitive parameters. The technical limitations of the thermal units and transmission network constraints are also considered in the model. The proposed model is a Mixed Integer Linear Programming (MILP) which can be easily solved by strong commercial solvers (for instance CPLEX) and it is appropriate to be used in the practical large scale networks. The results of implementing the proposed model on a test system reveal that proper load leveling through optimum storage scheduling leads to considerable operation cost reduction with respect to the storage system characteristics. PMID:27222741
Arcella, D; Soggiu, M E; Leclercq, C
2003-10-01
For the assessment of exposure to food-borne chemicals, the most commonly used methods in the European Union follow a deterministic approach based on conservative assumptions. Over the past few years, to get a more realistic view of exposure to food chemicals, risk managers are getting more interested in the probabilistic approach. Within the EU-funded 'Monte Carlo' project, a stochastic model of exposure to chemical substances from the diet and a computer software program were developed. The aim of this paper was to validate the model with respect to the intake of saccharin from table-top sweeteners and cyclamate from soft drinks by Italian teenagers with the use of the software and to evaluate the impact of the inclusion/exclusion of indicators on market share and brand loyalty through a sensitivity analysis. Data on food consumption and the concentration of sweeteners were collected. A food frequency questionnaire aimed at identifying females who were high consumers of sugar-free soft drinks and/or of table top sweeteners was filled in by 3982 teenagers living in the District of Rome. Moreover, 362 subjects participated in a detailed food survey by recording, at brand level, all foods and beverages ingested over 12 days. Producers were asked to provide the intense sweeteners' concentration of sugar-free products. Results showed that consumer behaviour with respect to brands has an impact on exposure assessments. Only probabilistic models that took into account indicators of market share and brand loyalty met the validation criteria.
Sensitivity Analysis in Two-Stage DEA
Directory of Open Access Journals (Sweden)
Athena Forghani
2015-07-01
Full Text Available Data envelopment analysis (DEA is a method for measuring the efficiency of peer decision making units (DMUs which uses a set of inputs to produce a set of outputs. In some cases, DMUs have a two-stage structure, in which the first stage utilizes inputs to produce outputs used as the inputs of the second stage to produce final outputs. One important issue in two-stage DEA is the sensitivity of the results of an analysis to perturbations in the data. The current paper looks into combined model for two-stage DEA and applies the sensitivity analysis to DMUs on the entire frontier. In fact, necessary and sufficient conditions for preserving a DMU's efficiency classiffication are developed when various data changes are applied to all DMUs.
Sensitivity Analysis in Two-Stage DEA
Directory of Open Access Journals (Sweden)
Athena Forghani
2015-12-01
Full Text Available Data envelopment analysis (DEA is a method for measuring the efficiency of peer decision making units (DMUs which uses a set of inputs to produce a set of outputs. In some cases, DMUs have a two-stage structure, in which the first stage utilizes inputs to produce outputs used as the inputs of the second stage to produce final outputs. One important issue in two-stage DEA is the sensitivity of the results of an analysis to perturbations in the data. The current paper looks into combined model for two-stage DEA and applies the sensitivity analysis to DMUs on the entire frontier. In fact, necessary and sufficient conditions for preserving a DMU's efficiency classiffication are developed when various data changes are applied to all DMUs.
DEFF Research Database (Denmark)
Avolio, E.; Federico, S.; Miglietta, M.
2017-01-01
the surface, where the model uncertainties are, usually, smaller than at the surface. A general anticlockwise rotation of the simulated flow with height is found at all levels. The mixing height is overestimated by all schemes and a possible role of the simulated sensible heat fluxes for this mismatching......The sensitivity of boundary layer variables to five (two non-local and three local) planetary boundary-layer (PBL) parameterization schemes, available in the Weather Research and Forecasting (WRF) mesoscale meteorological model, is evaluated in an experimental site in Calabria region (southern...... is investigated. On a single-case basis, significantly better results are obtained when the atmospheric conditions near the measurement site are dominated by synoptic forcing rather than by local circulations. From this study, it follows that the two first order non-local schemes, ACM2 and YSU, are the schemes...
Directory of Open Access Journals (Sweden)
Jinyang Du
2015-07-01
Full Text Available Freeze-thaw (FT and moisture dynamics within the soil active layer are critical elements of boreal, arctic and alpine ecosystems, and environmental change assessments. We evaluated the potential for detecting dielectric changes within different soil layers using combined L- and P-band radar remote sensing as a prerequisite for detecting FT and moisture profile changes within the soil active layer. A two-layer scattering model was developed and validated for simulating radar responses from vertically inhomogeneous soil. The model simulations indicated that inhomogeneity in the soil dielectric profile contributes to both L- and P-band backscatter, but with greater P-band sensitivity at depth. The difference in L- and P-band responses to soil dielectric profile inhomogeneity appears suitable for detecting associated changes in soil active layer conditions. Additional evaluation using collocated airborne radar (AIRSAR observations and in situ soil moisture measurements over alpine tundra indicates that combined L- and P-band SAR observations are sensitive to soil dielectric profile heterogeneity associated with variations in soil moisture and FT conditions.
Precipitates/Salts Model Sensitivity Calculation
International Nuclear Information System (INIS)
Mariner, P.
2001-01-01
The objective and scope of this calculation is to assist Performance Assessment Operations and the Engineered Barrier System (EBS) Department in modeling the geochemical effects of evaporation on potential seepage waters within a potential repository drift. This work is developed and documented using procedure AP-3.12Q, ''Calculations'', in support of ''Technical Work Plan For Engineered Barrier System Department Modeling and Testing FY 02 Work Activities'' (BSC 2001a). The specific objective of this calculation is to examine the sensitivity and uncertainties of the Precipitates/Salts model. The Precipitates/Salts model is documented in an Analysis/Model Report (AMR), ''In-Drift Precipitates/Salts Analysis'' (BSC 2001b). The calculation in the current document examines the effects of starting water composition, mineral suppressions, and the fugacity of carbon dioxide (CO 2 ) on the chemical evolution of water in the drift
Schuff, M M; Gore, J P; Nauman, E A
2013-12-01
The treatment of cancerous tumors is dependent upon the delivery of therapeutics through the blood by means of the microcirculation. Differences in the vasculature of normal and malignant tissues have been recognized, but it is not fully understood how these differences affect transport and the applicability of existing mathematical models has been questioned at the microscale due to the complex rheology of blood and fluid exchange with the tissue. In addition to determining an appropriate set of governing equations it is necessary to specify appropriate model parameters based on physiological data. To this end, a two stage sensitivity analysis is described which makes it possible to determine the set of parameters most important to the model's calibration. In the first stage, the fluid flow equations are examined and a sensitivity analysis is used to evaluate the importance of 11 different model parameters. Of these, only four substantially influence the intravascular axial flow providing a tractable set that could be calibrated using red blood cell velocity data from the literature. The second stage also utilizes a sensitivity analysis to evaluate the importance of 14 model parameters on extravascular flux. Of these, six exhibit high sensitivity and are integrated into the model calibration using a response surface methodology and experimental intra- and extravascular accumulation data from the literature (Dreher et al. in J Natl Cancer Inst 98(5):335-344, 2006). The model exhibits good agreement with the experimental results for both the mean extravascular concentration and the penetration depth as a function of time for inert dextran over a wide range of molecular weights.
Chen, Mingjie; Abriola, Linda M.; Amos, Benjamin K.; Suchomel, Eric J.; Pennell, Kurt D.; Löffler, Frank E.; Christ, John A.
2013-08-01
Reductive dechlorination catalyzed by organohalide-respiring bacteria is often considered for remediation of non-aqueous phase liquid (NAPL) source zones due to cost savings, ease of implementation, regulatory acceptance, and sustainability. Despite knowledge of the key dechlorinators, an understanding of the processes and factors that control NAPL dissolution rates and detoxification (i.e., ethene formation) is lacking. A recent column study demonstrated a 5-fold cumulative enhancement in tetrachloroethene (PCE) dissolution and ethene formation (Amos et al., 2009). Spatial and temporal monitoring of key geochemical and microbial (i.e., Geobacter lovleyi and Dehalococcoides mccartyi strains) parameters in the column generated a data set used herein as the basis for refinement and testing of a multiphase, compositional transport model. The refined model is capable of simulating the reactive transport of multiple chemical constituents produced and consumed by organohalide-respiring bacteria and accounts for substrate limitations and competitive inhibition. Parameter estimation techniques were used to optimize the values of sensitive microbial kinetic parameters, including maximum utilization rates, biomass yield coefficients, and endogenous decay rates. Comparison and calibration of model simulations with the experimental data demonstrate that the model is able to accurately reproduce measured effluent concentrations, while delineating trends in dechlorinator growth and reductive dechlorination kinetics along the column. Sensitivity analyses performed on the optimized model parameters indicate that the rates of PCE and cis-1,2-dichloroethene (cis-DCE) transformation and Dehalococcoides growth govern bioenhanced dissolution, as long as electron donor (i.e., hydrogen flux) is not limiting. Dissolution enhancements were shown to be independent of cis-DCE accumulation; however, accumulation of cis-DCE, as well as column length and flow rate (i.e., column residence time
Bertheloot, Jessica; Wu, Qiongli; Cournède, Paul-Henry; Andrieu, Bruno
2011-10-01
Simulating nitrogen economy in crop plants requires formalizing the interactions between soil nitrogen availability, root nitrogen acquisition, distribution between vegetative organs and remobilization towards grains. This study evaluates and analyses the functional-structural and mechanistic model of nitrogen economy, NEMA (Nitrogen Economy Model within plant Architecture), developed for winter wheat (Triticum aestivum) after flowering. NEMA was calibrated for field plants under three nitrogen fertilization treatments at flowering. Model behaviour was investigated and sensitivity to parameter values was analysed. Nitrogen content of all photosynthetic organs and in particular nitrogen vertical distribution along the stem and remobilization patterns in response to fertilization were simulated accurately by the model, from Rubisco turnover modulated by light intercepted by the organ and a mobile nitrogen pool. This pool proved to be a reliable indicator of plant nitrogen status, allowing efficient regulation of nitrogen acquisition by roots, remobilization from vegetative organs and accumulation in grains in response to nitrogen treatments. In our simulations, root capacity to import carbon, rather than carbon availability, limited nitrogen acquisition and ultimately nitrogen accumulation in grains, while Rubisco turnover intensity mostly affected dry matter accumulation in grains. NEMA enabled interpretation of several key patterns usually observed in field conditions and the identification of plausible processes limiting for grain yield, protein content and root nitrogen acquisition that could be targets for plant breeding; however, further understanding requires more mechanistic formalization of carbon metabolism. Its strong physiological basis and its realistic behaviour support its use to gain insights into nitrogen economy after flowering.
A new importance measure for sensitivity analysis
International Nuclear Information System (INIS)
Liu, Qiao; Homma, Toshimitsu
2010-01-01
Uncertainty is an integral part of risk assessment of complex engineering systems, such as nuclear power plants and space crafts. The aim of sensitivity analysis is to identify the contribution of the uncertainty in model inputs to the uncertainty in the model output. In this study, a new importance measure that characterizes the influence of the entire input distribution on the entire output distribution was proposed. It represents the expected deviation of the cumulative distribution function (CDF) of the model output that would be obtained when one input parameter of interest were known. The applicability of this importance measure was tested with two models, a nonlinear nonmonotonic mathematical model and a risk model. In addition, a comparison of this new importance measure with several other importance measures was carried out and the differences between these measures were explained. (author)
Wang, Yuan; Zhang, Na; Yu, Gui-rui
2010-07-01
By using modified carbon-water cycle model EPPML (ecosystem productivity process model for landscape), the carbon absorption and respiration in Qianyanzhou artificial masson pine forest ecosystem in 2003 and 2004 were simulated, and the sensitivity of the model parameters was analyzed. The results showed that EPPML could effectively simulate the carbon cycle process of this ecosystem. The simulated annual values and the seasonal variations of gross primary productivity (GPP), net ecosystem productivity (NEP), and ecosystem respiration (Re) not only fitted well with the measured data, but also reflected the major impacts of extreme weather on carbon flows. The artificial masson pine forest ecosystem in Qianyanzhou was a strong carbon sink in both 2003 and 2004. Due to the coupling of high temperature and severe drought in the growth season in 2003, the carbon absorption in 2003 was lower than that in 2004. The annual NEP in 2003 and 2004 was 481.8 and 516.6 g C x m(-2) x a(-1), respectively. The key climatic factors giving important impacts on the seasonal variations of carbon cycle were solar radiation during early growth season, drought during peak growth season, and precipitation during post-peak growth season. Autotrophic respiration (Ra) and net primary productivity (NPP) had the similar seasonal variations. Soil heterotrophic respiration (Rh) was mainly affected by soil temperature at yearly scale, and by soil water content at monthly scale. During wet growth season, the higher the soil water content, the lower the Rh was; during dry growth season, the higher the precipitation during the earlier two months, the higher the Rh was. The maximum RuBP carboxylation rate at 25 degrees C (Vm25), specific leaf area (SLA), maximum leaf nitrogen content (LNm), average leaf nitrogen content (LN), and conversion coefficient of biomass to carbon (C/B) had the greatest influence on annual NEP. Different carbon cycle process could have different responses to sensitive
The sensitivity of the ESA DELTA model
Martin, C.; Walker, R.; Klinkrad, H.
Long-term debris environment models play a vital role in furthering our understanding of the future debris environment, and in aiding the determination of a strategy to preserve the Earth orbital environment for future use. By their very nature these models have to make certain assumptions to enable informative future projections to be made. Examples of these assumptions include the projection of future traffic, including launch and explosion rates, and the methodology used to simulate break-up events. To ensure a sound basis for future projections, and consequently for assessing the effectiveness of various mitigation measures, it is essential that the sensitivity of these models to variations in key assumptions is examined. The DELTA (Debris Environment Long Term Analysis) model, developed by QinetiQ for the European Space Agency, allows the future projection of the debris environment throughout Earth orbit. Extensive analyses with this model have been performed under the auspices of the ESA Space Debris Mitigation Handbook and following the recent upgrade of the model to DELTA 3.0. This paper draws on these analyses to present the sensitivity of the DELTA model to changes in key model parameters and assumptions. Specifically the paper will address the variation in future traffic rates, including the deployment of satellite constellations, and the variation in the break-up model and criteria used to simulate future explosion and collision events.
Multitarget global sensitivity analysis of n-butanol combustion.
Zhou, Dingyu D Y; Davis, Michael J; Skodje, Rex T
2013-05-02
A model for the combustion of butanol is studied using a recently developed theoretical method for the systematic improvement of the kinetic mechanism. The butanol mechanism includes 1446 reactions, and we demonstrate that it is straightforward and computationally feasible to implement a full global sensitivity analysis incorporating all the reactions. In addition, we extend our previous analysis of ignition-delay targets to include species targets. The combination of species and ignition targets leads to multitarget global sensitivity analysis, which allows for a more complete mechanism validation procedure than we previously implemented. The inclusion of species sensitivity analysis allows for a direct comparison between reaction pathway analysis and global sensitivity analysis.
International Nuclear Information System (INIS)
Fitton, N; Datta, A; Hastings, A; Kuhnert, M; Smith, P; Topp, C F E; Cloy, J M; Rees, R M; Cardenas, L M; Williams, J R; Smith, K; Chadwick, D
2014-01-01
The United Kingdom currently reports nitrous oxide emissions from agriculture using the IPCC default Tier 1 methodology. However Tier 1 estimates have a large degree of uncertainty as they do not account for spatial variations in emissions. Therefore biogeochemical models such as DailyDayCent (DDC) are increasingly being used to provide a spatially disaggregated assessment of annual emissions. Prior to use, an assessment of the ability of the model to predict annual emissions should be undertaken, coupled with an analysis of how model inputs influence model outputs, and whether the modelled estimates are more robust that those derived from the Tier 1 methodology. The aims of the study were (a) to evaluate if the DailyDayCent model can accurately estimate annual N 2 O emissions across nine different experimental sites, (b) to examine its sensitivity to different soil and climate inputs across a number of experimental sites and (c) to examine the influence of uncertainty in the measured inputs on modelled N 2 O emissions. DailyDayCent performed well across the range of cropland and grassland sites, particularly for fertilized fields indicating that it is robust for UK conditions. The sensitivity of the model varied across the sites and also between fertilizer/manure treatments. Overall our results showed that there was a stronger correlation between the sensitivity of N 2 O emissions to changes in soil pH and clay content than the remaining input parameters used in this study. The lower the initial site values for soil pH and clay content, the more sensitive DDC was to changes from their initial value. When we compared modelled estimates with Tier 1 estimates for each site, we found that DailyDayCent provided a more accurate representation of the rate of annual emissions. (paper)
Energy Technology Data Exchange (ETDEWEB)
Hunke, Elizabeth Clare [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Urrego Blanco, Jorge Rolando [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Urban, Nathan Mark [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2018-02-12
Coupled climate models have a large number of input parameters that can affect output uncertainty. We conducted a sensitivity analysis of sea ice proper:es and Arc:c related climate variables to 5 parameters in the HiLAT climate model: air-ocean turbulent exchange parameter (C), conversion of water vapor to clouds (cldfrc_rhminl) and of ice crystals to snow (micro_mg_dcs), snow thermal conduc:vity (ksno), and maximum snow grain size (rsnw_mlt). We used an elementary effect (EE) approach to rank their importance for output uncertainty. EE is an extension of one-at-a-time sensitivity analyses, but it is more efficient in sampling multi-dimensional parameter spaces. We looked for emerging relationships among climate variables across the model ensemble, and used causal discovery algorithms to establish potential pathways for those relationships.
Sensitivity and uncertainty analyses for performance assessment modeling
International Nuclear Information System (INIS)
Doctor, P.G.
1988-08-01
Sensitivity and uncertainty analyses methods for computer models are being applied in performance assessment modeling in the geologic high level radioactive waste repository program. The models used in performance assessment tend to be complex physical/chemical models with large numbers of input variables. There are two basic approaches to sensitivity and uncertainty analyses: deterministic and statistical. The deterministic approach to sensitivity analysis involves numerical calculation or employs the adjoint form of a partial differential equation to compute partial derivatives; the uncertainty analysis is based on Taylor series expansions of the input variables propagated through the model to compute means and variances of the output variable. The statistical approach to sensitivity analysis involves a response surface approximation to the model with the sensitivity coefficients calculated from the response surface parameters; the uncertainty analysis is based on simulation. The methods each have strengths and weaknesses. 44 refs
Energy Technology Data Exchange (ETDEWEB)
Nicoulaud-Gouin, V.; Metivier, J.M.; Gonze, M.A. [Institut de Radioprotection et de Surete Nucleaire-PRP-ENV/SERIS/LM2E (France); Garcia-Sanchez, L. [Institut de Radioprotection et de Surete Nucleaire-PRPENV/SERIS/L2BT (France)
2014-07-01
The increasing spatial and temporal complexity of models demands methods capable of ranking the influence of their large numbers of parameters. This question specifically arises in assessment studies on the consequences of the Fukushima accident. Sensitivity analysis aims at measuring the influence of input variability on the output response. Generally, two main approaches are distinguished (Saltelli, 2001, Iooss, 2011): - Screening approach, less expensive in computation time and allowing to identify non influential parameters; - Measures of importance, introducing finer quantitative indices. In this category, there are regression-based methods, assuming a linear or monotonic response (Pearson coefficient, Spearman coefficient), and variance-based methods, without assumptions on the model but requiring an increasingly prohibitive number of evaluations when the number of parameters increases. These approaches are available in various statistical programs (notably R) but are still poorly integrated in modelling platforms of radioecological risk assessment. This work aimed at illustrating the benefits of sensitivity analysis in the course of radioecological risk assessments This study used two complementary state-of-art global sensitivity analysis methods: - The screening method of Morris (Morris, 1991; Campolongo et al., 2007) based on limited model evaluations with a one-at-a-time (OAT) design; - The variance-based Sobol' sensitivity analysis (Saltelli, 2002) based a large number of model evaluations in the parameter space with a quasi-random sampling (Owen, 2003). Sensitivity analyses were applied on a dynamic Soil-Plant Deposition Model (Gonze et al., submitted to this conference) predicting foliar concentration in weeds after atmospheric radionuclide fallout. The Soil-Plant Deposition Model considers two foliage pools and a root pool, and describes foliar biomass growth with a Verhulst model. The developed semi-analytic formulation of foliar concentration
International Nuclear Information System (INIS)
Van Winkle, W.; Christensen, S.W.; Kauffman, G.
1976-12-01
The description and justification for the compensation function developed and used by Lawler, Matusky and Skelly Engineers (LMS) (under contract to Consolidated Edison Company of New York) in their Hudson River striped bass models are presented. A sensitivity analysis of this compensation function is reported, based on computer runs with a modified version of the LMS completely mixed (spatially homogeneous) model. Two types of sensitivity analysis were performed: a parametric study involving at least five levels for each of the three parameters in the compensation function, and a study of the form of the compensation function itself, involving comparison of the LMS function with functions having no compensation at standing crops either less than or greater than the equilibrium standing crops. For the range of parameter values used in this study, estimates of percent reduction are least sensitive to changes in YS, the equilibrium standing crop, and most sensitive to changes in KXO, the minimum mortality rate coefficient. Eliminating compensation at standing crops either less than or greater than the equilibrium standing crops results in higher estimates of percent reduction. For all values of KXO and for values of YS and KX at and above the baseline values, eliminating compensation at standing crops less than the equilibrium standing crops results in a greater increase in percent reduction than eliminating compensat