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.

被动安全系统 (PSS) 越来越多地用于先进核电站 (NPP)。它们的安全性能是通过计算成本高昂的热液压 (TH) 模拟模型进行评估的，识别导致不安全条件的操作条件（所谓的关键故障区域，CRs）可能具有挑战性。

在本文中，提出了一种计算框架来识别核电厂的通用被动衰变热去除 (DHR) 系统的 CR。该系统的耗时最佳估计热工水力 (BE-TH) 模型用于训练嵌入在文献自适应采样技术中的快速运行元模型，即自适应克里金蒙特卡罗采样 (AK-MCS)，因此以在接近故障阈值时提供更高的准确性，并确定哪些输入值导致 PSS 出现故障。据作者所知，这是首次将基于元模型的 AK-MCS 技术应用于识别核电厂 PSS 的 CR。