An Adaptive Metamodel-Based Subset Importance Sampling (AM-SIS) approach, previously developed by the authors, is here employed to assess the (small) functional failure probability of a thermal-hydraulic (T-H) nuclear passive safety system. The approach relies on an iterative Importance Sampling (IS) scheme that efficiently couples the powerful characteristics of Subset Simulation (SS) and fast-running Artificial Neural Networks (ANNs). In particular, SS and ANNs are intelligently employed to build and progressively refine a fully nonparametric estimator of the ideal, zero-variance Importance Sampling Density (ISD), in order to: (i) increase the robustness of the failure probability estimates and (ii) decrease the number of expensive T-H simulations needed (together with the related computational burden). The performance of the approach is thoroughly compared to that of other efficient Monte Carlo (MC) techniques on a case study involving an advanced nuclear reactor cooled by helium.

An Adaptive Metamodel-Based Subset Importance Sampling approach for the assessment of the functional failure probability of a thermal-hydraulic passive system

Pedroni, Nicola;Zio, Enrico
2017-01-01

Abstract

An Adaptive Metamodel-Based Subset Importance Sampling (AM-SIS) approach, previously developed by the authors, is here employed to assess the (small) functional failure probability of a thermal-hydraulic (T-H) nuclear passive safety system. The approach relies on an iterative Importance Sampling (IS) scheme that efficiently couples the powerful characteristics of Subset Simulation (SS) and fast-running Artificial Neural Networks (ANNs). In particular, SS and ANNs are intelligently employed to build and progressively refine a fully nonparametric estimator of the ideal, zero-variance Importance Sampling Density (ISD), in order to: (i) increase the robustness of the failure probability estimates and (ii) decrease the number of expensive T-H simulations needed (together with the related computational burden). The performance of the approach is thoroughly compared to that of other efficient Monte Carlo (MC) techniques on a case study involving an advanced nuclear reactor cooled by helium.
Adaptive metamodeling; Efficient Monte Carlo; Non-parametric importance sampling; Nuclear passive safety system; Small failure probability; Subset Simulation; Modeling and Simulation; Applied Mathematics
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1053214
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