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A Bayesian framework of inverse uncertainty quantification with principal component analysis and Kriging for the reliability analysis of passive safety systems

Abstract : In this work, we propose an Inverse Uncertainty Quantification (IUQ) approach to assigning Probability Density Functions (PDFs) to uncertain input parameters of Thermal-Hydraulic (T-H) models used to assess the reliability of passive safety systems. The approach uses experimental data within a Bayesian framework. The application to a RELAP5-3D model of the PERSEO (In-Pool Energy Removal System for Emergency Operation) facility located at SIET laboratory (Piacenza, Italy) is demonstrated. Principal Component Analysis (PCA) is applied for output dimensionality reduction and Kriging meta-modeling is used to emulate the reduced set of RELAP5-3D code outputs. This is done to decrease the computational cost of the Markov Chain Monte Carlo (MCMC) posterior sampling of the uncertain input parameters, which requires a large number of model simulations.
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https://hal-mines-paristech.archives-ouvertes.fr/hal-03479086
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Submitted on : Tuesday, December 14, 2021 - 11:06:10 AM
Last modification on : Wednesday, August 24, 2022 - 2:45:54 PM

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Giovanni Roma, Francesco Di Maio, Andrea Bersano, Nicola Pedroni, Cristina Bertani, et al.. A Bayesian framework of inverse uncertainty quantification with principal component analysis and Kriging for the reliability analysis of passive safety systems. Nuclear Engineering and Design, Elsevier, 2021, 379, pp.111230. ⟨10.1016/j.nucengdes.2021.111230⟩. ⟨hal-03479086⟩

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