Efficient functional reliability estimation for a passive residual heat removal system with subset simulation based on importance sampling

Abstract

An innovative reliability analysis approach known as “Subset Simulation based on Importance Sampling” is developed for the efficient estimation of the small functional failure probability of a passive safety system. This approach is based on the idea that a small failure probability can be expressed as a product of larger conditional failure probabilities by introducing a proper choice of intermediate failure events. Importance sampling simulation is carried out to generate conditional samples for each intermediate failure region. This application is illustrated for the functional reliability analysis of a passive residual heat removal system due to epistemic uncertainty parameters. The numerical results demonstrate the high level of computational efficiency and excellent computational accuracy by comparison with direct Monte Carlo simulation, Importance Sampling simulation and Subset Simulation based on Markov Chain Monte Carlo. The sensitivity, defined as the partial derivative of the failure probability with respect to the distribution parameter is also discussed, which can help to identify the contribution of each parameter and guide the optimization model.