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• W4380478933 startingPage "109965" @default.
• W4380478933 abstract "A reliable irradiation embrittlement model of reactor pressure vessel (RPV) is critical to the safe operation and life extension of nuclear power plants (NPP). In this study, 474 datasets from NUREG/CR-6551 and 6 machine learning (ML) methods were adopted to accurately predict the irradiation embrittlement behavior (ΔRTNDT) of RPV with comprehensive input factors involving irradiation conditions and metal composition. The GBDT model has the best prediction performance. The calculated result by GBDT model is close to those of JEAC-4201 and ASTM E900-15. Cu content is the most sensitive factor for ΔRTNDT, followed by neutron fluence. This work demonstrates a successful application of ML in promoting NPP safety and RPV materials design." @default.
• W4380478933 created "2023-06-14" @default.
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• W4380478933 date "2023-11-01" @default.
• W4380478933 modified "2023-09-27" @default.
• W4380478933 title "Study on irradiation embrittlement behavior of reactor pressure vessels by machine learning methods" @default.
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• W4380478933 doi "https://doi.org/10.1016/j.anucene.2023.109965" @default.
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