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.
A Bayesian framework of inverse uncertainty quantification with principal component analysis and Kriging for the reliability analysis of passive safety systems / Roma, G.; Di Maio, F.; Bersano, A.; Pedroni, N.; Bertani, C.; Mascari, F.; Zio, E.. - In: NUCLEAR ENGINEERING AND DESIGN. - ISSN 0029-5493. - ELETTRONICO. - 379:(2021), p. 111230. [10.1016/j.nucengdes.2021.111230]
A Bayesian framework of inverse uncertainty quantification with principal component analysis and Kriging for the reliability analysis of passive safety systems
Bersano A.;Pedroni N.;Bertani C.;Zio E.
2021
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.File | Dimensione | Formato | |
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NED_A Bayesian framework of inverse uncertainty quantification with PCA.pdf
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R1_Inverse uncertainty quantification.pdf
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https://hdl.handle.net/11583/2915686