• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2844-2853
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• 2023

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.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.20 no.2
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• pp.97-106
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• 2024

This study proposes a practical adjustment equation for the Embrittlement Trend Curve (ETC) to effectively apply it to reactor pressure vessel (RPV) materials in individual nuclear power plants. Traditional ETC adjustment methods have limitations due to constraints in number of group-specific measurements and a lack of statistical foundations. To address these issues, KAERI applied a Markov Chain Monte Carlo (MCMC)-based mixed-effect model to the latest ETC model, ASTM E900-15. This approach quantitatively calculates the mean, standard deviation, and prediction intervals of the adjustment intercept by considering the grouping characteristics of surveillance data and uncertainties in unirradiated specimens. Although the KAERI model provides quantitative distributions of parameters and intercepts, it has challenges in practical applications due to computational complexity and low portability. In this study, a simplified equation was developed using the statistical calculations of the mixed-effect model, which retains the primary outcomes of the KAERI model while enhancing portability. This equation supports effective adjustments to the ASTM E900-15 ETC for nuclear power plants with diverse material properties and operational conditions. It enables reliable evaluations of RPV integrity using plant-specific surveillance data. The findings of this study are expected to improve the precision and practicality of ETCs.