Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Leak flow prediction during loss of coolant accidents using deep fuzzy neural networks

  • Park, Ji Hun (Department of Nuclear Engineering, Chosun University) ;
  • An, Ye Ji (Department of Nuclear Engineering, Chosun University) ;
  • Yoo, Kwae Hwan (Korea Atomic Energy Research Institute) ;
  • Na, Man Gyun (Department of Nuclear Engineering, Chosun University)
  • Received : 2020.06.17
  • Accepted : 2021.01.31
  • Published : 2021.08.25
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Abstract

The frequency of reactor coolant leakage is expected to increase over the lifetime of a nuclear power plant owing to degradation mechanisms, such as flow-acceleration corrosion and stress corrosion cracking. When loss of coolant accidents (LOCAs) occur, several parameters change rapidly depending on the size and location of the cracks. In this study, leak flow during LOCAs is predicted using a deep fuzzy neural network (DFNN) model. The DFNN model is based on fuzzy neural network (FNN) modules and has a structure where the FNN modules are sequentially connected. Because the DFNN model is based on the FNN modules, the performance factors are the number of FNN modules and the parameters of the FNN module. These parameters are determined by a least-squares method combined with a genetic algorithm; the number of FNN modules is determined automatically by cross checking a fitness function using the verification dataset output to prevent an overfitting problem. To acquire the data of LOCAs, an optimized power reactor-1000 was simulated using a modular accident analysis program code. The predicted results of the DFNN model are found to be superior to those predicted in previous works. The leak flow prediction results obtained in this study will be useful to check the core integrity in nuclear power plant during LOCAs. This information is also expected to reduce the workload of the operators.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (Grant No. NRF-2018M2B2B1065651) and the Korea Institute of Energy Technology and Planning (KETEP) grant funded by the Korean government (MOTIE) (Grant No. 20181510102340, Development of a real-time detection system for unidentified RCS leakage less than 0.5 gpm).

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