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A sophisticated Bayesian multilevel model for estimating group bias was developed to improve the utility of the ASTM E900-15 embrittlement trend curve (ETC) to assess the conditions of nuclear power plants (NPPs). For multilevel model development, the Baseline 22 surveillance dataset was basically classified into groups based on the NPP name, product form, and notch orientation. By including the notch direction in the grouping criteria, the developed model could account for TTS differences among NPP groups with different notch orientations, which have not been considered in previous ETCs. The parameters of the multilevel model and biases of the NPP groups were calculated using the Markov Chain Monte Carlo method. As the number of data points within a group increased, the group bias approached the mean residual, resulting in reduced credible intervals of the mean, and vice versa. Even when the number of surveillance test data points was less than three, the multilevel model could estimate appropriate biases without overfitting. The model also allowed for a quantitative estimate of the changes in the bias and prediction interval that occurred as a result of adding more surveillance test data. The biases estimated through the multilevel model significantly improved the performance of E900-15.

In this study, machine learning (ML) techniques were used to model surveillance test data of nuclear power plants from an international database of the ASTM E10.02 committee. Regression modeling was conducted using various techniques, including Cubist, XGBoost, and a support vector machine. The root mean square deviation of each ML model for the baseline dataset was less than that of the ASTM E900-15 nonlinear regression model. With respect to the interpolation, the ML methods provided excellent predictions with relatively few computations when applied to the given data range. The effect of the explanatory variables on the transition temperature shift (TTS) for the ML methods was analyzed, and the trends were slightly different from those for the ASTM E900-15 model. ML methods showed some weakness in the extrapolation of the fluence in comparison to the ASTM E900-15, while the Cubist method achieved an extrapolation to a certain extent. To achieve a more reliable prediction of the TTS, it was confirmed that advanced techniques should be considered for extrapolation when applying ML modeling.

The prediction of irradiation-induced transition temperature shift for RPV steels is an important method for long term operation of nuclear power plant. Based on the irradiation embrittlement data, an irradiation-induced transition temperature shift prediction model is developed with machine learning method XGBoost. Then the residual, standard deviation and predicted value vs. measured value analysis are conducted to analyze the accuracy of this model. At last, Cu content threshold and saturation values analysis, temperature dependence, Ni/Cu dependence and flux effect are given to verify the reliability. Those results show that the prediction model developed with XGBoost has high accuracy for predicting the irradiation embrittlement trend of RPV steel. The prediction results are consistent with the current understanding of RPV embrittlement mechanism.

Existing elastic analysis methods cannot be adhered to in order to assess the structural integrity of a reactor vessel and internals for a beyond design basis earthquake. Elasto-plastic analysis methods are required, and the factors that affect the elasto-plastic behavior of reactor materials should be taken into account. In this study, a material behavior model was developed that considers the irradiation embrittlement effect, which affects the elasto-plastic behavior of the reactor material. This was used to perform the elasto-plastic time history analyses of the reactor vessel and its internals for beyond design basis earthquake. For this investigation, appropriate beyond design basis earthquakes and reliable finite element models were used. Based on the analysis results, consideration was given to the load reduction effect and the margin change. These were transferred to the internals due to the plastic deformation of the reactor vessel.

High-Cr martensitic steel HT-9 is one of the candidate materials for advanced nuclear energy systems. Thanks to its excellent thermal conductivity and irradiation resistance, ferritic/martensitic steels such as HT-9 are considered for in-core applications of advanced nuclear reactors. The harsh neutron irradiation environments at the reactor core region pose a unique challenge for structural and cladding materials. Microstructural and microchemical changes resulting from displacement damage are anticipated for structural materials after prolonged neutron exposure. Consequently, various irradiation effects on the service performance of in-core materials need to be understood. In this work, the fundamentals of radiation damage and irradiation effects of the HT-9 martensitic steel are reviewed. The objective of this paper is to provide a background introduction of displacement damage, microstructural evolution, and subsequent effects on mechanical properties of the HT-9 martensitic steel under neutron irradiations. Mechanical test results of the irradiated HT-9 steel obtained from previous fast reactor and fusion programs are summarized along with the information of irradiated microstructure. This review can serve as a starting point for additional investigations on the in-core applications of ferritic/martensitic steels in advanced nuclear reactors.