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Baccou, J.*; Glantz, T.*; Ghione, A.*; Sargentini, L.*; Fillion, P.*; Damblin, G.*; Sueur, R.*; Iooss, B.*; Fang, J.*; Liu, J.*; et al.
Nuclear Engineering and Design, 421, p.113035_1 - 113035_16, 2024/05
被引用回数：0In the Best-Estimate Plus Uncertainty (BEPU) framework, the use of best-estimate code requires to go through a Verification, Validation and Uncertainty Quantification process (VVUQ). The relevance of the experimental data in relation to the physical phenomena of interest in the VVUQ process is crucial. Adequacy analysis of selected experimental databases addresses this problem. The outcomes of the analysis can be used to select a subset of relevant experimental data, to encourage designing new experiments or to drop some experiments from a database because of their substantial lack of adequacy. The development of a specific transparent and reproducible approach to analyze the relevance of experimental data for VVUQ still remains open and is the topic of this contribution. In this paper, the concept of adequacy initially introduced in the OECD/NEA SAPIUM (Systematic APproach for model Input Uncertainty quantification Methodology) activity is formalized. It is defined through two key properties, called representativeness and completeness, that allows considering the multifactorial dimension of the adequacy problem. A new systematic approach is then proposed to analyze the adequacy of a set of experimental databases. It relies on the introduction of two sets of criteria to characterize representativeness and completeness and on the use of multi-criteria decision analysis method to perform the analysis. Finally, the approach is applied in the framework of the new OECD/NEA ATRIUM activity which includes a set of practical IUQ exercises in thermal-hydraulics to test the SAPIUM guideline in determining input uncertainties and forward propagating them on an application case. It allows evaluating the adequacy of eight experimental databases coming from the Super Moby-dick, Sozzi-Sutherland and Marviken experiments and identifying the most adequate ones.
Chevalier-Jabet, K.*; Zheng, X.; Mabrouk, A.*; 丸山 結; Baccou, J.*
Proceedings of 2015 International Congress on Advances in Nuclear Power Plants (ICAPP 2015) (CD-ROM), 13 Pages, 2015/05
Severe accident phenomenology in light water nuclear power plants is complex. For the past decades, extensive experimental programs have been conducted to gain knowledge and computational tools have been built to predict accident progressions and consequences. Nevertheless remained uncertainties directly affect the predictability of severe accidents consequences. Monte-Carlo techniques are widely used in previous uncertainty analysis and the shortcomings are addressed by JAEA and IRSN. The first part of the article deals with uncertainty propagation. Possibilist formalisms are presented with an example. In the second part, JAEA has developed a method for source term assessment using a Dirichlet process. The implementation of the method is described from the Bayesian nonparametric model to the cross-validation process. As results, corresponding computational cost and importance measure of inputs are addressed. The third part describes the current research at IRSN. The combination of Bayesian formalism and graph theory is applied to modeling severe accident uncertainties. The method allows the information to propagate in any direction of the graph, making inference easy to perform. Bayesian networks allow the representation of a complex model in an integrated environment.
