scholarly journals Nonlinear Uncertainty Quantification, Sensitivity Analysis, and Uncertainty Propagation of a Dynamic Electrical Circuit

2013 ◽  
Author(s):  
Austin Doty ◽  
John Doty ◽  
Jose Camberos ◽  
Kirk Yerkes
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Quantification ◽  
Uncertainty Propagation ◽  
Electrical Circuit

scholarly journals Basic Framework and Main Methods of Uncertainty Quantification

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Juan Zhang ◽  
Junping Yin ◽  
Ruili Wang
Keyword(s):  
Sensitivity Analysis ◽  
Bayesian Inference ◽  
Uncertainty Quantification ◽  
Model Uncertainty ◽  
Model Calibration ◽  
Uncertainty Propagation ◽  
Propagation Model ◽  
Practical Significance ◽  
The Core ◽  
Basic Framework

Since 2000, the research of uncertainty quantification (UQ) has been successfully applied in many fields and has been highly valued and strongly supported by academia and industry. This review firstly discusses the sources and the types of uncertainties and gives an overall discussion on the goal, practical significance, and basic framework of the research of UQ. Then, the core ideas and typical methods of several important UQ processes are introduced, including sensitivity analysis, uncertainty propagation, model calibration, Bayesian inference, experimental design, surrogate model, and model uncertainty analysis.

Sobol′ Sensitivity Analysis Using a Neural Network Model of a LB-LOCA in the ZION Nuclear Power Plant With CATHARE-2 V2.5 Code

2013 ◽  
Author(s):  
Fabrice Fouet ◽  
Pierre Probst
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Nuclear Power ◽  
Uncertainty Propagation ◽  
Correlation Coefficients ◽  
Computer Code ◽  
Numerical Application ◽  
Loss Of Coolant Accident ◽  
Input Parameters ◽  
Artificial Neural Network Ann

In nuclear safety, the Best-Estimate (BE) codes may be used in safety demonstration and licensing, provided that uncertainties are added to the relevant output parameters before comparing them with the acceptance criteria. The uncertainty of output parameters, which comes mainly from the lack of knowledge of the input parameters, is evaluated by estimating the 95% percentile with a high degree of confidence. IRSN, technical support of the French Safety Authority, developed a method of uncertainty propagation. This method has been tested with the BE code used is CATHARE-2 V2.5 in order to evaluate the Peak Cladding Temperature (PCT) of the fuel during a Large Break Loss Of Coolant Accident (LB-LOCA) event, starting from a large number of input parameters. A sensitivity analysis is needed in order to limit the number of input parameters and to quantify the influence of each one on the response variability of the numerical model. Generally, the Global Sensitivity Analysis (GSA) is done with linear correlation coefficients. This paper presents a new approach to perform a more accurate GSA to determine and to classify the main uncertain parameters: the Sobol′ methodology. The GSA requires simulating many sets of parameters to propagate uncertainties correctly, which makes of it a time-consuming approach. Therefore, it is natural to replace the complex computer code by an approximate mathematical model, called response surface or surrogate model. We have tested Artificial Neural Network (ANN) methodology for its construction and the Sobol′ methodology for the GSA. The paper presents a numerical application of the previously described methodology on the ZION reactor, a Westinghouse 4-loop PWR, which has been retained for the BEMUSE international problem [8]. The output is the first maximum PCT of the fuel which depends on 54 input parameters. This application outlined that the methodology could be applied to high-dimensional complex problems.

06-02 Methods for Uncertainty Quantification, Sensitivity Analysis, and Prediction Sessions

2021 ◽  
Author(s):  
Michael Prime ◽  
Gavin Jones ◽  
Vicente Romero ◽  
Justin Winokur ◽  
Benjamin Schroeder
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Quantification

scholarly journals UNCERTAINTY PROPAGATION AND SENSITIVITY ANALYSIS IN RAY-TRACING SIMULATIONS

2011 ◽  
Vol 21 ◽  
pp. 149-161 ◽  
Author(s):  
Antoine Haarscher ◽  
P. De Doncker ◽  
David Lautru
Keyword(s):  
Sensitivity Analysis ◽  
Ray Tracing ◽  
Uncertainty Propagation
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