Nuclear Engineering and Technology

  • 제53권12호
  • /
  • Pages.4022-4032
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  • 2021
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  • 1738-5733(pISSN)
  • /
  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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Machine learning modeling of irradiation embrittlement in low alloy steel of nuclear power plants

  • Lee, Gyeong-Geun (Materials Safety Technology Development Division, Korea Atomic Energy Research Institute (KAERI)) ;
  • Kim, Min-Chul (Materials Safety Technology Development Division, Korea Atomic Energy Research Institute (KAERI)) ;
  • Lee, Bong-Sang (Materials Safety Technology Development Division, Korea Atomic Energy Research Institute (KAERI))
  • 투고 : 2021.03.09
  • 심사 : 2021.06.07
  • 발행 : 2021.12.25
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초록

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.

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과제정보

The authors would like to thank the ASTM E10.02 committee for providing the dataset for this study. This work was supported by the Ministry of Science and ICT and the National Research Foundation of Korea (NRF) grant funded by the Korean government (2017M2A8A4015156).

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