Failure identification in a nuclear passive safety system by Monte Carlo simulation with adaptive Kriging

Passive Safety Systems (PSSs) are increasingly employed in advanced Nuclear Power Plants (NPPs). Their safety performance is evaluated through computationally expensive Thermal-Hydraulic (T-H) simulations models and the identification of the operational conditions which lead to unsafe conditions (the so-called Critical failure Regions, CRs) may be challenging. In the present paper, a computational framework is proposed to identify the CRs of a generic passive Decay Heat Removal (DHR) system of a NPP. A time-demanding Best-Estimate Thermal-Hydraulic (BE-TH) model of the system is used to train a fast-running metamodel embedded within an adaptive sampling technique of literature, namely Adaptive Kriging Monte Carlo Sampling (AK-MCS), so as to provide increased accuracy in proximity of the failure threshold and identify which input values lead the PSS to failure. To the best authors’ knowledge this is the first time that the metamodel-based AK-MCS technique is applied for the identification of the CRs of a PSS of an NPP.