Markov analysis for time dependent success criteria of passive decay heat removal system

Hassija, Varun; Kumar, Chandan; Velusamy, K.

doi:10.1016/j.anucene.2014.05.029

Cited by 1 publication

(1 citation statement)

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“…On the other hand, the most innovative reactor concepts, such as those of Generation IV, aim at relying almost entirely on passive safety systems, thus becoming as much independent as possible from any kind of external input, especially energy [1] . Moreover, the simplicity of the design of these systems is such that mechanical failures become very unlikely to happen, in contrast to active systems [2][3][4][5][6][7] .…”

Section: Introductionmentioning

confidence: 99%

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

Pedroni

Zio

2017

Applied Mathematical Modelling

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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.

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