Nuclear Engineering and Technology

  • 제56권6호
  • /
  • Pages.2266-2273
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  • 2024
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  • 1738-5733(pISSN)
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  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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A machine learning informed prediction of severe accident progressions in nuclear power plants

  • JinHo Song (Department of Nuclear Engineering, Hanyang University) ;
  • SungJoong Kim (Department of Nuclear Engineering, Hanyang University)
  • 투고 : 2023.11.06
  • 심사 : 2024.01.24
  • 발행 : 2024.06.25
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초록

A machine learning platform is proposed for the diagnosis of a severe accident progression in a nuclear power plant. To predict the key parameters for accident management including lost signals, a long short term memory (LSTM) network is proposed, where multiple accident scenarios are used for training. Training and test data were produced by MELCOR simulation of the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident at unit 3. Feature variables were selected among plant parameters, where the importance ranking was determined by a recursive feature elimination technique using RandomForestRegressor. To answer the question of whether a reduced order ML model could predict the complex transient response, we performed a systematic sensitivity study for the choices of target variables, the combination of training and test data, the number of feature variables, and the number of neurons to evaluate the performance of the proposed ML platform. The number of sensitivity cases was chosen to guarantee a 95 % tolerance limit with a 95 % confidence level based on Wilks' formula to quantify the uncertainty of predictions. The results of investigations indicate that the proposed ML platform consistently predicts the target variable. The median and mean predictions were close to the true value.

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과제정보

This work was supported by the National Research Foundation of Korea (NRF) grant (No. RS-2022-00144202) and by the Innovative Small Modular Reactor Development Agency grant (No. RS-2023-00259516) funded by the Korean government.

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