Implementation of condition-dependent probabilistic risk assessment using surveillance data on passive components

Abstract

A great deal of surveillance data are collected for a nuclear power plant that reflect the changing condition of the plant as it ages. Although surveillance data are used to determine failure probabilities of active components for the plant’s probabilistic risk assessment (PRA) and to indicate the need for maintenance activities, they are not used in a structured manner to characterize the evolving risk of the plant. The present study explores the feasibility of using a condition-dependent PRA framework that takes a first principles approach to modeling the progression of degradation mechanisms to characterize evolving risk, periodically adapting the model to account for surveillance results. A case study is described involving a potential containment bypass accident sequence due to the progression of flow-accelerated corrosion in secondary system piping and stress corrosion cracking of steam generator tubes. In this sequence, a steam line break accompanied by failure to close of a main steam isolation valve results in depressurization of the steam generator and induces the rupture of one or more faulted steam generator tubes. The case study indicates that a condition-dependent PRA framework might be capable of providing early identification of degradation mechanisms important to plant risk.