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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.8
- 수록면
- 2,610 - 2,615 (6page)
- DOI
- https://doi.org/10.1016/j.net.2021.02.015
이용수
초록· 키워드
오류제보하기
The prediction of irradiation-induced transition temperature shift for RPV steels is an important methodfor long term operation of nuclear power plant. Based on the irradiation embrittlement data, anirradiation-induced transition temperature shift prediction model is developed with machine learningmethod XGBoost. Then the residual, standard deviation and predicted value vs. measured value analysisare conducted to analyze the accuracy of this model. At last, Cu content threshold and saturation valuesanalysis, 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 predictingthe irradiation embrittlement trend of RPV steel. The prediction results are consistent with the currentunderstanding of RPV embrittlement mechanism
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