Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Improvement of the subcooled boiling model using a new net vapor generation correlation inferred from artificial neural networks to predict the void fraction profiles in the vertical channel

  • Tae Beom Lee (Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology (KAIST) ) ;
  • Yong Hoon Jeong (Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology (KAIST) )
  • Received : 2022.03.31
  • Accepted : 2022.07.31
  • Published : 2022.12.25
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Abstract

In the one-dimensional thermal-hydraulic (TH) codes, a subcooled boiling model to predict the void fraction profiles in a vertical channel consists of wall heat flux partitioning, the vapor condensation rate, the bubbly-to-slug flow transition criterion, and drift-flux models. Model performance has been investigated in detail, and necessary refinements have been incorporated into the Safety and Performance Analysis Code (SPACE) developed by the Korean nuclear industry for the safety analysis of pressurized water reactors (PWRs). The necessary refinements to models related to pumping factor, net vapor generation (NVG), vapor condensation, and drift-flux velocity were investigated in this study. In particular, a new NVG empirical correlation was also developed using artificial neural network (ANN) techniques. Simulations of a series of subcooled flow boiling experiments at pressures ranging from 1 to 149.9 bar were performed with the refined SPACE code, and reasonable agreement with the experimental data for the void fraction in the vertical channel was obtained. From the root-mean-square (RMS) error analysis for the predicted void fraction in the subcooled boiling region, the results with the refined SPACE code produce the best predictions for the entire pressure range compared to those using the original SPACE and RELAP5 codes.

Keywords

Acknowledgement

This work was financially supported by the grant of research and development project, Technology Development for Innovative Small Modular Reactor (Contract No.: L21S068000), funded by the KHNP (Korea Hydro and Nuclear Power Co., Ltd) in South Korea.

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