Prediction and Analysis of Heat Transfer Characteristics of Supercritical Fluids Based on Interpretable Machine Learning

Li Haozhe; Song Meiqi; Liu Xiaojing

doi:10.13832/j.jnpe.2024.06.0063

Volume 45 Issue 6

Dec. 2024

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Li Haozhe, Song Meiqi, Liu Xiaojing. Prediction and Analysis of Heat Transfer Characteristics of Supercritical Fluids Based on Interpretable Machine Learning[J]. Nuclear Power Engineering, 2024, 45(6): 63-74. doi: 10.13832/j.jnpe.2024.06.0063

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Li Haozhe, Song Meiqi, Liu Xiaojing. Prediction and Analysis of Heat Transfer Characteristics of Supercritical Fluids Based on Interpretable Machine Learning[J]. Nuclear Power Engineering, 2024, 45(6): 63-74. doi: 10.13832/j.jnpe.2024.06.0063

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Prediction and Analysis of Heat Transfer Characteristics of Supercritical Fluids Based on Interpretable Machine Learning

doi: 10.13832/j.jnpe.2024.06.0063

Li Haozhe^{1, 3
,},
Song Meiqi^{2, 3},
Liu Xiaojing^{1, 3
,
,}

1.
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
2.
College of Smart Energy, Shanghai Jiao Tong University, Shanghai, 200240, China
3.
Shanghai Numerical Nuclear Reactor Technology Integration Innovation Center, Shanghai, 200240, China

Received Date: 2024-01-15
Rev Recd Date: 2024-07-18
Publish Date: 2024-12-17

Abstract

Abstract

The physical properties of supercritical fluids change drastically near the pseudo-critical temperature, making it challenging to accurately predict heat transfer characteristics. In this study, the method of interpretable machine learning was used to predict and analyze the heat transfer characteristics of supercritical fluids. The particle swarm optimization algorithm (PSO) was used to search for the optimal hyperparameters of the back propagation neural network (BPNN) model, the supercritical fluid heat transfer prediction model was established, and its accuracy was compared with the traditional empirical correlation. The global and local interpretation of the BPNN model was carried out by using the SHAP interpretable algorithm, and the supercritical correlation phenomenon and mechanism were found according to the change of feature importance under different conditions. The results show that the MAPE of the established neural network model on the test set is 1.4%, and the coefficient of determination R² is 0.9992, which has higher prediction accuracy compared with the empirical correlation formula. For vertical upward flow, buoyancy effect obviously has higher feature importance in heat transfer deterioration condition, which is the main factor of heat transfer deterioration behavior. Therefore, the research method based on interpretable machine learning established in this study has certain reference significance for further study of the heat transfer characteristics of supercritical fluids.
- Supercritical fluids,
- Interpretable machine learning,
- Heat transfer prediction

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References(33)

References

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