Development of the DQFM method to consider the effect of correlation of component failures in seismic PSA of nuclear power plant

Abstract

Abstract This paper presents a new calculation method for considering the effect of correlation of component failures in seismic probabilistic safety assessment (PSA) of nuclear power plants (NPPs) by direct quantification of Fault Tree (FT) using the Monte Carlo simulation (DQFM) and discusses the effect of correlation on core damage frequency (CDF). In the DQFM method, occurrence probability of a top event is calculated as follows: (1) Response and capacity of each component are generated according to their probability distribution. In this step, the response and capacity can be made correlated according to a set of arbitrarily given correlation data. (2) For each component whether the component is failed or not is judged by comparing the response and the capacity. (3) The status of each component, failure or success, is assigned as either TRUE or FALSE in a Truth Table, which represents the logical structure of the FT to judge the occurrence of the top event. After this trial is iterated sufficient times, the occurrence probability of the top event is obtained as the ratio of the occurrence number of the top event to the number of total iterations. The DQFM method has the following features compared with the minimal cut set (MCS) method used in the well known Seismic Safety Margins Research Program (SSMRP). While the MCS method gives the upper bound approximation for occurrence probability of an union of MCSs, the DQFM method gives more exact results than the upper bound approximation. Further, the DQFM method considers the effect of correlation on the union and intersection of component failures while the MCS method considers only the effect on the latter. The importance of these features in seismic PSA of NPPs are demonstrated by an example calculation and a calculation of CDF in a seismic PSA. The effect of correlation on CDF was evaluated by the DQFM method and was compared with that evaluated in the application study of the SSMRP methodology. In the application study, Bohn et al. showed that correlation had a significant effect on CDF and may vary it by up to an order. However, in the results calculated by the DQFM method correlation varied CDF by at most 2 or 3 times compared with CDF for a case where no correlation was assumed. Although some factors should further be examined, this implied that the MCS method may have overestimated the effect of correlation on CDF and the effect of correlation on CDF may not be so significant as that evaluated in the SSMRP.