Quantification of the uncertainty of the physical models in the system thermal-hydraulic codes – PREMIUM benchmark

Abstract PREMIUM (Post BEMUSE Reflood Models Input Uncertainty Methods) was an activity launched with the aim of pushing forward the methods of quantification of physical model uncertainties in thermal-hydraulic codes. The benchmark PREMIUM was addressed to all who apply uncertainty evaluation methods based on input uncertainties quantification and propagation. The benchmark was based on a selected case of uncertainty analysis application to the simulation of quench front propagation in an experimental test facility. Applied to an experiment, enabled evaluation and confirmation of the quantified probability distribution functions on the basis of experimental data. The scope of the benchmark comprised a review of the existing methods, selection of potentially important uncertain input parameters, quantification of the ranges and distributions of the identified parameters using experimental results of tests performed on the FEBA test facility, verification of the performed quantification on the basis of tests performed at the FEBA test facility and validation on the basis of blind calculations of the Reflood 2-D PERICLES experiment. The benchmark has shown dependency of the results on the applied methodology and a strong user effect. The conclusion was that a systematic approach for the quantification of model uncertainties is necessary.

[1]  Francesco Saverio D'Auria,et al.  Development and assessment of a method for evaluating uncertainty of input parameters , 2017 .

[2]  S. S. Wilks Determination of Sample Sizes for Setting Tolerance Limits , 1941 .

[3]  A. de Crécy,et al.  Quantification of the uncertainty of physical models integrated into system thermohydraulic codes , 2017 .

[4]  Francesc Reventos,et al.  Testing methodologies for quantifying physical models uncertainties. A comparative exercise using CIRCE and IPREM (FFTBM) , 2016 .

[5]  Sébastien Destercke,et al.  Methods for the evaluation and synthesis of multiple sources of information applied to nuclear computer codes , 2008 .

[6]  Pierre Probst,et al.  DIPE: Determination of Input Parameters Uncertainties Methodology Applied to CATHARE V2.5_1 , 2008 .

[7]  François M. Hemez,et al.  Improved best estimate plus uncertainty methodology, including advanced validation concepts, to license evolving nuclear reactors , 2011 .

[8]  Jinzhao Zhang,et al.  Development of a Pragmatic Approach to Model Input Uncertainty Quantification for BEPU Applications , 2018, Nuclear Technology.

[9]  Kyung Doo Kim,et al.  IMPLEMENTATION OF DATA ASSIMILATION METHODOLOGY FOR PHYSICAL MODEL UNCERTAINTY EVALUATION USING POST - CHF EXPERIMENTAL DATA , 2014 .

[10]  Dan G. Cacuci,et al.  Best-Estimate Model Calibration and Prediction through Experimental Data Assimilation—I: Mathematical Framework , 2010 .

[11]  Francesc Reventos,et al.  SAPIUM a systematic approach for input uncertainty quantification , 2018 .