Research on Prediction of Subcooled Flow Boiling CHF for Spiral Flow Based on Machine Learning

Yan Jianguo; Zheng Shumin; Guo Pengcheng; Zhao Li; Wang Shuai; Liu Kun; Zhu Xutao

doi:10.13832/j.jnpe.2023.03.0065

Volume 44 Issue 3

Jun. 2023

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Article Navigation > Nuclear Power Engineering > 2023 > 44(3): 65-73

Yan Jianguo, Zheng Shumin, Guo Pengcheng, Zhao Li, Wang Shuai, Liu Kun, Zhu Xutao. Research on Prediction of Subcooled Flow Boiling CHF for Spiral Flow Based on Machine Learning[J]. Nuclear Power Engineering, 2023, 44(3): 65-73. doi: 10.13832/j.jnpe.2023.03.0065

Citation:

Yan Jianguo, Zheng Shumin, Guo Pengcheng, Zhao Li, Wang Shuai, Liu Kun, Zhu Xutao. Research on Prediction of Subcooled Flow Boiling CHF for Spiral Flow Based on Machine Learning[J]. Nuclear Power Engineering, 2023, 44(3): 65-73. doi: 10.13832/j.jnpe.2023.03.0065

Citation:

Yan Jianguo, Zheng Shumin, Guo Pengcheng, Zhao Li, Wang Shuai, Liu Kun, Zhu Xutao. Research on Prediction of Subcooled Flow Boiling CHF for Spiral Flow Based on Machine Learning[J]. Nuclear Power Engineering, 2023, 44(3): 65-73. doi: 10.13832/j.jnpe.2023.03.0065

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Research on Prediction of Subcooled Flow Boiling CHF for Spiral Flow Based on Machine Learning

doi: 10.13832/j.jnpe.2023.03.0065

1.
State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an, 710048, China
2.
School of Energy & Architecture, Xi’an Aeronautical Institute, Xi’an, 710077, China

Received Date: 2022-07-27
Rev Recd Date: 2023-02-27
Publish Date: 2023-06-15

Abstract

Abstract

Subcooled boiling is widely used in cooling applications with high heat fluxes represented by International Thermonuclear Experimental Reactor (ITER). In this paper, predictions on the critical heat flux (CHF) of subcooled water boiling under high heat flux and swirl flow conditions are focused and a database of subcooled boiling CHF is established. Machine learning methods are applied, in which four typical machine learning models are adopted, namely Back Propagation (BP) neural network, Genetic Algorithm (GA)-BP neural network, Radial Basis Function (RBF) neural network and Extreme Learning Machine (ELM). The results indicate that machine learning models can effectively predict subcooled boiling CHF with swirling flow, and the prediction performances are obviously promoted, comparing with traditional empirical correlations. Among typical machine learning models, the ELM model possesses the best performance, with the MAE and RMSE are equal to 2.79% and 4.22%, respectively. The results provide a new path to making accurate predictions on the CHF for subcooled boiling under high heat flux and swirling flow conditions.
- Critical heat flux (CHF),
- Subcooled boiling,
- Machine learning,
- Heat transfer,
- Two-phase flow

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References(31)

References

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