Monte Carlo Simulation-based Sensitivity Analysis of the model of a Thermal-Hydraulic Passive SystemReportar como inadecuado

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* Corresponding author 1 Chaire Sciences des Systèmes et Défis Energétiques EDF-ECP-Supélec SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF-ECP-Supélec, Dipartimento di Energia 2 Dipartimento di Energia

Abstract : Thermal-Hydraulic T-H passive safety systems are potentially more reliable than active systems, and for this reason are expected to improve the safety of nuclear power plants. However, uncertainties are present in the operation and modeling of a T-H passive system and the system may find itself unable to accomplish its function. For the analysis of the system functional failures, a mechanistic code is used and the probability of failure is estimated based on a Monte Carlo MC sample of code runs which propagate the uncertainties in the model and numerical values of its parameters-variables. Within this framework, sensitivity analysis aims at determining the contribution of the individual uncertain parameters i.e., the inputs to the mechanistic code to i the uncertainty in the outputs of the T-H model code and ii the probability of functional failure of the passive system. The analysis requires multiple e.g., many hundreds or thousands evaluations of the code for different combinations of system inputs: this makes the associated computational effort prohibitive in those practical cases in which the computer code requires several hours to run a single simulation. To tackle the computational issue, in this work the use of the Subset Simulation SS and Line Sampling LS methods is investigated. The methods are tested on two case studies: the first one is based on the well-known Ishigami function 1; the second one involves the natural convection cooling in a Gas-cooled Fast Reactor GFR after a Loss of Coolant Accident LOCA 2.

Keywords : Sobol indices Nuclear passive system Functional failure probability Reliability sensitivity analysis Subset Simulation Line Sampling Sobol indices.

Autor: Enrico Zio - Nicola Pedroni -



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