Probabilistic Models of Reliability of Cast Austenitic Stainless Steel Piping

The concern of toughness reduction due to thermal embrittlement of cast austenitic stainless steel (CASS) piping is increasing as nuclear power plants age. Because of the large and variable grain size of the CASS materials, the ultrasonic inspection (UT) difficulties of the CASS components increases concerns regarding their reliability. Another added concern is the presence of potential defects introduced during the casting fabrication process. The possible presence of defects and difficulty of inspection complicate the development of programs to manage the risk contributed by these potentially degraded components. Experiments have been performed in the past to evaluate the effect of thermal embrittlement on tensile properties and fracture toughness as functions of time, temperature, composition, and delta ferrite content, but considerable scatter has been shown in the results among the important variables. A probabilistic approach is proposed for the evaluation of the aging effect based on the scatter in material correlations, difficulty of inspection and presence of initial defects. The purpose of this study is to describe a probabilistic fracture mechanics analysis approach for the determination of the maximum allowable flaw sizes in CASS piping components in commercial power reactors, using Monte Carlo simulation. Attention is focused on fully embrittled CF8M material, and the probability of failure for a given crack size, load and composition is predicted considering scatter in tensile properties and fracture toughness (fracture toughness is expressed as a crack growth resistance relation in terms of J-Δa). The correlation between the reduced toughness and increased tensile properties due to thermal embrittlement is also included in the analysis. This paper presents results for CF8M to demonstrate the sensitivity of key input variables on the most severely embrittled material. The output of this study is the flaw size (length and depth) that will fail with a given probability as a function of load and geometry.