Multi-State Physics Models of Aging Passive Components in Probabilistic Risk Assessment
Multi-state Markov modeling has proved to be a promising approach to estimating the reliability of passive components - particularly metallic pipe components - in the context of probabilistic risk assessment (PRA). These models consider the progressive degradation of a component through a series of observable discrete states, such as detectable flaw, leak and rupture. Service data then generally provides the basis for estimating the state transition rates. Research in materials science is producing a growing understanding of the physical phenomena that govern the aging degradation of passive pipe components. As a result, there is an emerging opportunity to incorporate these insights into PRA. This paper describes research conducted under the Risk-Informed Safety Margin Characterization Pathway of the Department of Energy’s Light Water Reactor Sustainability Program. A state transition model is described that addresses aging behavior associated with stress corrosion cracking in ASME Class 1 dissimilar metal welds – a component type relevant to LOCA analysis. The state transition rate estimates are based on physics models of weld degradation rather than service data. The resultant model is found to be non-Markov in that the transition rates are time-inhomogeneous and stochastic. Numerical solutions to the model provide insight into the effect of aging on component reliability.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1025091
- Report Number(s):
- PNNL-SA-76893; 830403000; TRN: US201120%%275
- Resource Relation:
- Conference: International Topical Meeting on Probabilistic Safety Assessment and Analysis (PSA 2011), March 13-17, 2011, Wilmington, North Carolina, 1:161-172
- Country of Publication:
- United States
- Language:
- English
Similar Records
RELIABILITY MODELS OF AGING PASSIVE COMPONENTS INFORMED BY MATERIALS DEGRADATION METRICS TO SUPPORT LONG-TERM REACTOR OPERATIONS
Interpretation of probabilistic structural analysis of an aging passive component