A Framework based on Finite Mixture Models and Adaptive Kriging for Characterizing Non-Smooth and Multimodal Failure Regions in a Nuclear Passive Safety System

L. Puppo, N. Pedroni, F. Di Maio, A. Bersano, C. Bertani and E. Zio

Reliability Engineering and System Safety, 2021, vol. 216, issue C

Abstract: In the safety analyses of passive systems for nuclear energy applications, computationally demanding models can be substituted by fast-running surrogate models coupled with adaptive sampling techniques; for speeding up the exploration of the components and system state-space and the characterization of the conditions leading to failure (i.e., the system Critical failure Regions, CRs). However, in some cases of non-smoothness and multimodality of the state-space, the existing approaches do not suffice. In this paper, we propose a novel methodological framework, based on Finite Mixture Models (FMMs) and Adaptive Kriging (AK-MCS) for CRs characterization in case of non-smoothness and/or multimodality of the output. The framework contains three main steps: 1) dimensionality reduction through FMMs to tackle the output non-smoothness and multimodality, while focusing on its clusters defining the system failure; 2) adaptive training (AK-MCS) of the metamodel on the reduced space to mimic the time-demanding model and, finally, 3) use of the trained metamodel provide the output for new input combinations and retrieve information about the CRs.

Keywords: Critical Failure Region characterization; Dimensionality Reduction; Sensitivity Analysis; Finite Mixture Models (FMMs); Kriging; Adaptive Sampling; Adaptive-Kriging Monte Carlo Sampling (AK-MCS); Passive Safety System; Decay Heat Removal (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:216:y:2021:i:c:s0951832021004749

DOI: 10.1016/j.ress.2021.107963

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