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Article Dans Une Revue Nuclear Engineering and Design Année : 2021

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

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Andrea Bersano
Nicola Pedroni
Cristina Bertani
Fulvio Mascari
  • Fonction : Auteur

Résumé

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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Dates et versions

hal-03479086 , version 1 (14-12-2021)

Identifiants

Citer

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, 2021, 379, pp.111230. ⟨10.1016/j.nucengdes.2021.111230⟩. ⟨hal-03479086⟩
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