Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Prediction of critical heat flux for narrow rectangular channels in a steady state condition using machine learning

  • Kim, Huiyung (Department of Mechanical Engineering, Pusan National University) ;
  • Moon, Jeongmin (Department of Mechanical Engineering, Pusan National University) ;
  • Hong, Dongjin (Department of Mechanical Engineering, Pusan National University) ;
  • Cha, Euiyoung (Department of Mechanical Engineering, Pusan National University) ;
  • Yun, Byongjo (Department of Mechanical Engineering, Pusan National University)
  • Received : 2020.07.12
  • Accepted : 2020.12.07
  • Published : 2021.06.25
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Abstract

The subchannel of a research reactor used to generate high power density is designed to be narrow and rectangular and comprises plate-type fuels operating under downward flow conditions. Critical heat flux (CHF) is a crucial parameter for estimating the safety of a nuclear fuel; hence, this parameter should be accurately predicted. Here, machine learning is applied for the prediction of CHF in a narrow rectangular channel. Although machine learning can effectively analyze large amounts of complex data, its application to CHF, particularly for narrow rectangular channels, remains challenging because of the limited flow conditions available in existing experimental databases. To resolve this problem, we used four CHF correlations to generate pseudo-data for training an artificial neural network. We also propose a network architecture that includes pre-training and prediction stages to predict and analyze the CHF. The trained neural network predicted the CHF with an average error of 3.65% and a root-mean-square error of 17.17% for the test pseudo-data; the respective errors of 0.9% and 26.4% for the experimental data were not considered during training. Finally, machine learning was applied to quantitatively investigate the parametric effect on the CHF in narrow rectangular channels under downward flow conditions.

Keywords

Acknowledgement

This work was supported by the Nuclear Safety Research Program through the Korea Foundation of Nuclear Safety, South Korea, a grant from the Nuclear Safety and Security Commission, South Korea (Grant No. 1903001), and a grant from the Nuclear Research & Development Program of the National Research Foundation of Korea, South Korea funded by the Ministry of Science, ICT and Future Planning, South Korea (Grant No. NRF-2019M2D2A1A03056998).

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