A Probability-Based Leak-Before-Break Application for Small Modular Reactors

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. In this study, we are going to utilize a simplified methodology based on the Univariate Dimension-Reduction (UDR) method to investigate the probability of piping rupture for postulated circumferential through-wall cracks in piping systems of small modular reactors (SMRs) inside the metal containment vessel, which typically range from 5” to 8” nominal diameter for feedwater and main steam lines. The unique design of the vacuumed metal CNV in an advanced SMR offers improved leak detection capability, which makes the LBB qualification possible even for piping with much smaller diameters than piping systems in the traditional large reactors, which typically range from 18” to 40” for LBB-qualified piping systems such as surge lines, cold legs, hot legs and main steam lines. The conditional probability that a piping with postulated crack would fail by large break, when multiplied by the probability of having a circumferential 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 properties, and flow-path crack face morphology parameters will be modeled as correlated random variables in this paper. Efficient Dimension-Reduction methods will be applied to predict the conditional probability. As sample applications of the proposed method, the relationship between the conditional probabilities and the loading factor will be established for SMR piping systems.