This report addresses the potential application of probabilistic fracture mechanics computer codes to support the Proactive Materials Degradation Assessment (PMDA) program as a method to predict component failure probabilities. The present report describes probabilistic fracture mechanics calculations that were performed for selected components using the PRO-LOCA and PRAISE computer codes. The calculations address the failure mechanisms of stress corrosion cracking, intergranular stress corrosion cracking, and fatigue for components and operating conditions that are known to make particular components susceptible to cracking. It was demonstrated that the two codes can predict essentially the same failure probabilities if both codes start with the same fracture mechanics model and the same inputs to the model. Comparisons with field experience showed that both codes predict relatively high failure probabilities for components under operating conditions that have resulted in field failures. It was found that modeling assumptions and inputs tended to give higher calculated failure probabilities than those derived from data on field failures. Sensitivity calculations were performed to show that uncertainties in the probabilistic calculations were sufficiently large to explain the differences between predicted failure probabilities and field experience.iii iv
Executive SummaryThe U.S. Nuclear Regulatory Commission (NRC) has supported the research program Proactive Materials Degradation Assessment (PMDA). The objective of this program has been to predict future occurrences of materials degradation that may or may not have been observed in the field or in the laboratory. Evaluations have focused on materials degradation modes associated with operating environments for specific components, including stress corrosion cracking, fatigue, flow-accelerated corrosion, boric acid corrosion, thermal embrittlement, and radiation effects. A detailed review addressed over 2000 components in the primary, secondary, and tertiary systems of specific reactor designs. A group of experts provided their judgments to score individual components in terms of "degradation susceptibility" and the "extent of knowledge" available for developing mitigation actions.This report addresses the possible application of probabilistic fracture mechanics computer codes to support the PMDA program as a method to predict component failure probabilities. Probabilistic fracture mechanics calculations are described that were performed for selected components using the PRO-LOCA and PRAISE computer codes. The calculations address the failure mechanisms of stress corrosion cracking, intergranular stress corrosion cracking, and fatigue for components and operating conditions that are known to have failed components in the field. The calculations allowed the two computer codes to be benchmarked against each other and, more importantly, benchmarked against field experience.A review of the calculations showed how uncertainties and modeling assumptions can im...
This paper describes an application of data on cracking, leak and rupture events from nuclear power plant operating experience to estimate failure frequencies for piping components that had been previously evaluated using the PROLOCA and PRAISE probabilistic fracture mechanics (PFM) computer codes. The calculations had addressed the failure mechanisms of stress corrosion cracking, intergranular stress corrosion cracking and fatigue for materials and operating conditions that were known to have failed components. The first objective was to benchmark the calculations against field experience. A second objective was a review of uncertainties in the treatments of the data from observed failures and in the structural mechanics models. The database PIPExp-2006 was applied to estimate failure frequencies. Because the number of reported failure events was small, there were also statistical uncertainties in the estimates of frequencies. Comparisons of predicted and observed failure frequencies showed that PFM codes correctly predicted relatively high failure probabilities for components that had experienced field failures. However, the predicted frequencies tended to be significantly greater than those estimated from plant operating experience. A review of the PFM models and inputs to the models showed that uncertainties in the calculations were sufficiently large to explain the differences between the predicted and observed failure frequencies.
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