Research on Prediction and Sensitivity Analysis of Minimum Film Boiling Temperature of Quenching Boiling Based on Machine Learning

Zhang Junquan; Deng Jian; Luo Yan; Lu Tao

doi:10.13832/j.jnpe.2024.04.0069

Volume 45 Issue 4

Aug. 2024

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Zhang Junquan, Deng Jian, Luo Yan, Lu Tao. Research on Prediction and Sensitivity Analysis of Minimum Film Boiling Temperature of Quenching Boiling Based on Machine Learning[J]. Nuclear Power Engineering, 2024, 45(4): 69-76. doi: 10.13832/j.jnpe.2024.04.0069

Citation:

Zhang Junquan, Deng Jian, Luo Yan, Lu Tao. Research on Prediction and Sensitivity Analysis of Minimum Film Boiling Temperature of Quenching Boiling Based on Machine Learning[J]. Nuclear Power Engineering, 2024, 45(4): 69-76. doi: 10.13832/j.jnpe.2024.04.0069

Citation:

Zhang Junquan, Deng Jian, Luo Yan, Lu Tao. Research on Prediction and Sensitivity Analysis of Minimum Film Boiling Temperature of Quenching Boiling Based on Machine Learning[J]. Nuclear Power Engineering, 2024, 45(4): 69-76. doi: 10.13832/j.jnpe.2024.04.0069

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Research on Prediction and Sensitivity Analysis of Minimum Film Boiling Temperature of Quenching Boiling Based on Machine Learning

doi: 10.13832/j.jnpe.2024.04.0069

Zhang Junquan^1
,,
Deng Jian²,
Luo Yan^{1
,
,},
Lu Tao¹

1.
College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, 100029, China
2.
Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, 610213, China

Received Date: 2023-09-19
Rev Recd Date: 2024-01-02
Publish Date: 2024-08-12

Abstract

Abstract

Quenching boiling is widely used in the cooling process of fuel rods after the loss of coolant accident in nuclear reactor. The determination of the minimum film boiling temperature (T_min) is very important for the safe operation of nuclear reactors. Based on the experimental data in the literature, this paper selects three typical machine learning models: Random Forest (RF), Artificial Neural Network (ANN) and eXtreme Gradient Boosting (XGBoost) to predict T_min during quenching boiling and conduct a sensitivity analysis of influencing factors. The results show that the machine learning method can effectively improve the accuracy of T_min prediction compared to the traditional empirical correlation. Among the models, the RF model exhibits the best predictive performance with a coefficient of determination R² of 0.9770. By combining the RF model with the Sobol’ global sensitivity method, the study identifies the coolant subcooling as the most influential parameter on T_min, followed by initial wall temperature, while length-diameter ratio, pressure and thermophysical properties have a smaller impact. The findings of this research will provide theoretical guidance for improving the safety of nuclear reactors.
- Quenching boiling,
- Minimum film boiling temperature,
- Machine learning,
- Global sensitivity

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References(28)

References

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