RELIABILITY MODELS OF AGING PASSIVE COMPONENTS INFORMED BY MATERIALS DEGRADATION METRICS TO SUPPORT LONG-TERM REACTOR OPERATIONS
Paper describes a methodology for the synthesis of nuclear power plant service data with expert-elicited materials degradation information to estimate the future failure rates of passive components. This method should be an important resource to long-term plant operations and reactor life extension. Conventional probabilistic risk assessments (PRAs) are not well suited to addressing long-term reactor operations. Since passive structures and components are among those for which 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 presents challenges. Service data to quantify component reliability models are sparse, and this is exacerbated by the greater data demands of age-dependent reliability models. Another factor is that there can be numerous potential degradation mechanisms associated with the materials and operating environment of a given component. This deepens the data problem since risk-informed management of 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 metrics of materials degradation susceptibility 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 predicted long-term operational risk.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1040676
- Report Number(s):
- PNNL-SA-78802; NSENAO; TRN: US1202505
- Journal Information:
- Nuclear Science and Engineering, Vol. 171, Issue 1; ISSN 0029-5639
- Country of Publication:
- United States
- Language:
- English
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