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

•Uncertain inputs of a T-H passive system model are calibrated.•Experimental data is used within a Bayesian framework.•Principal Component Analysis is applied for output dimensionality reduction.•Kriging is used to emulate quickly the long-running system model code.•The successful application to a m...

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Bibliographic Details
Published in:Nuclear engineering and design 2021-08, Vol.379, p.111230, Article 111230
Main Authors: Roma, Giovanni, Di Maio, Francesco, Bersano, Andrea, Pedroni, Nicola, Bertani, Cristina, Mascari, Fulvio, Zio, Enrico
Format: Article
Language:English
Subjects:
Bayesian analysis
Component reliability
Computer applications
Computer Science
Emergency procedures
Engineering Sciences
Inverse uncertainty quantification
Kriging
Markov chains
Mathematical models
Occupational safety
Parameter uncertainty
Passive safety systems
Principal component analysis
Principal components analysis
Probability density functions
Reliability analysis
Safety
Safety systems
Surrogate modeling
Three dimensional models
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Summary:•Uncertain inputs of a T-H passive system model are calibrated.•Experimental data is used within a Bayesian framework.•Principal Component Analysis is applied for output dimensionality reduction.•Kriging is used to emulate quickly the long-running system model code.•The successful application to a model of the PERSEO facility is demonstrated. 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.
ISSN:0029-5493
1872-759X
DOI:10.1016/j.nucengdes.2021.111230