Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Application of the machine learning technique for the development of a condensation heat transfer model for a passive containment cooling system

  • Lee, Dong Hyun (School of Mechanical Engineering, Pusan National University) ;
  • Yoo, Jee Min (School of Mechanical Engineering, Pusan National University) ;
  • Kim, Hui Yung (School of Mechanical Engineering, Pusan National University) ;
  • Hong, Dong Jin (School of Computer Science and Engineering, Pusan National University) ;
  • Yun, Byong Jo (School of Mechanical Engineering, Pusan National University) ;
  • Jeong, Jae Jun (School of Mechanical Engineering, Pusan National University)
  • Received : 2021.09.01
  • Accepted : 2021.12.18
  • Published : 2022.06.25
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Abstract

A condensation heat transfer model is essential to accurately predict the performance of the passive containment cooling system (PCCS) during an accident in an advanced light water reactor. However, most of existing models tend to predict condensation heat transfer very well for a specific range of thermal-hydraulic conditions. In this study, a new correlation for condensation heat transfer coefficient (HTC) is presented using machine learning technique. To secure sufficient training data, a large number of pseudo data were produced by using ten existing condensation models. Then, a neural network model was developed, consisting of a fully connected layer and a convolutional neural network (CNN) algorithm, DenseNet. Based on the hold-out cross-validation, the neural network was trained and validated against the pseudo data. Thereafter, it was evaluated using the experimental data, which were not used for training. The machine learning model predicted better results than the existing models. It was also confirmed through a parametric study that the machine learning model presents continuous and physical HTCs for various thermal-hydraulic conditions. By reflecting the effects of individual variables obtained from the parametric analysis, a new correlation was proposed. It yielded better results for almost all experimental conditions than the ten existing models.

Keywords

Acknowledgement

This research was supported by the National Research Foundation (NRF) grant funded by the Ministry of Science, and ICT of the Korean government (Grant code 2017M2A8A4015059 and 2019M2D2A1A03056998).

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