A Simplified Methodology for Extremely Low-Probability Rupture Prediction for PWR LBB Applications

Abstract

The leak-before-break (LBB) applicability is stated in General Design Criterion 4 (GDC-4) of Title 10 of the Code of Federal Regulation Part 50 (10 CFR 50). GDC-4 requires that analyses reviewed and approved by the U.S. Nuclear Regulatory Commission (NRC) demonstrate that the probability of fluid system piping rupture is extremely low under conditions consistent with the design basis for the piping, in order that dynamic effects associated with postulated pipe ruptures in nuclear power units may be excluded from the design basis. Standard review plan 3.6.3 (SRP-3.6.3) further requires a simultaneous safety margin of two and ten on the flaw size and leak rate detectability, respectively, for deterministic analyses, believing that the very conservative and restrictive safety margins would lead to extremely low probability of fluid system piping rupture. The technology advancements of recent years make it possible to numerically quantify the probability of rupture with confidence. Planned for completion within the next six years, a long-term, large-scale assessment tool, xLPR, is currently being developed by the U.S. NRC, in cooperation with the nuclear industry, to assess the extremely low probability of rupture. The tool will include comprehensive evaluations both before and after through-wall cracks are developed in the degraded components. In this study, we are going to utilize a simplified methodology to investigate the probability of piping rupture for a postulated through-wall crack. The conditional probability, when multiplied by the probability of having a through-wall crack during the life time of plant service, produces an overall probability of piping rupture. The major quantifiable uncertainties, such as the uncertainties associated with the material tensile properties and fracture toughness, and flow-path crack morphology parameters will be modeled as correlated random variables in this paper. Efficient Dimension-Reduction methods will be applied to predict this conditional probability and the results will be compared with the Monte Carlo simulation method. As a sample application of the proposed method, the relationship between the magnitude of the conditional probabilities and the required leak rate detection capability will be established.