Bayesian Analysis of Japanese Pressurized Water Reactor Surveillance Data for Irradiation Embrittlement Prediction

The goal of this study was to identify the chemical component variables that should be used in irradiation embrittlement prediction and to determine the uncertainty of prediction of irradiation embrittlement of reactor pressure vessel (RPV) steels. To this end, statistical analysis using a Bayesian nonparametric (BNP) method was performed for Japanese pressurized water reactor (PWR) surveillance test data whose neutron fluence ranged from 3 × 1018 to 1.2 × 1020 n/cm2 (E > 1 MeV). The BNP method is a machine learning statistical method that takes the complexity and uncertainty of input variables into account. Statistical analysis using an index to select the most suitable combination of input variables revealed that four variables, namely, neutron fluence and Cu, Ni, and Si contents, were the most effective combination for embrittlement prediction. Cu content had the largest effect on the degree of embrittlement, followed by Ni and Si, in that order. The shift in the reference nil-ductility temperature (ΔRTNDT) was also calculated using the probability distribution obtained by the BNP method. The overall standard deviation of the residuals between the calculated and measured values of ΔRTNDT was 8.4 °C, which was comparable to that of the current Japanese embrittlement correlation method (JEAC4201-2013). The 95% credible interval (CI) of the posterior distribution of ΔRTNDT (i.e., the range in which data can exist when the uncertainty of input data is taken into consideration) calculated by the BNP method was identical to or smaller than the margin in the current Japanese embrittlement correlation method described in JEAC4201-2013. This result indicates that an adequate margin is provided in JEAC4201-2013.