Reliability assessment of equal height difference passive containment cooling system based on the adaptive metamodel-based subset importance sampling method in inverse uncertainty quantification framework

To enhance computational efficiency in estimating the posterior failure probability of passive systems, this study proposes an innovative framework integrating inverse uncertainty quantification (IUQ) with adaptive metamodel-based subset importance sampling (AM-SIS). This approach leverages updated input parameter distributions for precise posterior failure probability assessment. Within the IUQ framework, Bayesian neural networks is employed to assist established Markov Chain Monte Carlo methods for posterior sampling, significantly reducing computational costs. The coupled IUQ-AM-SIS framework is validated on polynomial benchmark cases and the equal height difference passive containment cooling system (EHDPCCS), examining scenarios with single and double calibration parameters. Results demonstrate that integrating the AM-SIS method significantly improves computational efficiency for posterior failure probability estimation, particularly in low failure probability regimes.