Conventional probabilistic risk assessments (PRAs) are not well-suited to addressing long-term reactor operations. Since passive structures, systems and components are among those for which refurbishment or replacement can be least practical, they might be expected to contribute increasingly to risk in an aging plant. Yet, passives receive limited treatment in PRAs. Furthermore, PRAs produce only snapshots of risk based on the assumption of time-independent component failure rates. This assumption is unlikely to be valid in aging systems. The treatment of aging passive components in PRA does present challenges. First, service data required to quantify component reliability models are sparse, and this problem is exacerbated by the greater data demands of age-dependent reliability models. A compounding factor is that there can be numerous potential degradation mechanisms associated with the materials, design, and operating environment of a given component. This deepens the data problem since the risk-informed management of materials degradation and component aging will demand an understanding of the long-term risk significance of individual degradation mechanisms. In this paper we describe a Bayesian methodology that integrates the metrics of materials degradation susceptibility being developed under the Nuclear Regulatory Commission’s Proactive Materials Degradation Assessment Program with available plant service data to estimate age-dependent passive component reliabilities. Integration of these models into conventional PRA will provide a basis for materials degradation management informed by the predicted long-term operational risk.

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