Ensemble-based sensitivity analysis of a Best Estimate Thermal Hydraulics model: Application to a Passive Containment Cooling System of an AP1000 Nuclear Power Plant

Abstract

The reliability analysis of a Nuclear Power Plant (NPP) passive safety system requires to quantify its functional failure probability, using a Best Estimate (BE) Thermal Hydraulic (TH) code to predict the behavior in normal and accidental conditions. To effectively do this, sensitivity analysis (SA) is performed to identify the relevant, important input variables whose variations affect the system functional response (calculated as output of the BE-TH code). In this paper, we propose a novel framework for the sensitivity analysis (SA) of a BE-TH code and apply it to the analysis of the Passive Containment Cooling System (PCCS) of an Advanced Pressurized reactor AP1000 during a Loss Of Coolant Accident (LOCA). We build Finite Mixture Models (FMMs) for approximating the probability density function (pdf) of the pressure reached by the PCCS during the LOCA, on the basis of a limited number of simulations. Then, an ensemble of three alternative SA methods is innovatively set up for identifying the input variables that most affect the TH code output: input saliency, Hellinger distance and Kullback–Leibler divergence. The capability of the ensemble is shown on a numerical case study.