Biasing transition rate method based on direct MC simulation for probabilistic safety assessment

Abstract

Direct Monte Carlo (MC) simulation is a powerful probabilistic safety assessment method for accounting dynamics of the system. But it is not efficient at simulating rare events. A biasing transition rate method based on direct MC simulation is proposed to solve the problem in this paper. This method biases transition rates of the components by adding virtual components to them in series to increase the occurrence probability of the rare event, hence the decrease in the variance of MC estimator. Several cases are used to benchmark this method. The results show that the method is effective at modeling system failure and is more efficient at collecting evidence of rare events than the direct MC simulation. The performance is greatly improved by the biasing transition rate method.