Research on Judgment of Supercritical Water Heat Transfer Deterioration Based on Machine Learning

Ma Dongliang; Zhou Tao; Huang Yanping

doi:10.13832/j.jnpe.2021.04.0091

Volume 42 Issue 4

Aug. 2021

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Ma Dongliang, Zhou Tao, Huang Yanping. Research on Judgment of Supercritical Water Heat Transfer Deterioration Based on Machine Learning[J]. Nuclear Power Engineering, 2021, 42(4): 91-95. doi: 10.13832/j.jnpe.2021.04.0091

Citation:

Ma Dongliang, Zhou Tao, Huang Yanping. Research on Judgment of Supercritical Water Heat Transfer Deterioration Based on Machine Learning[J]. Nuclear Power Engineering, 2021, 42(4): 91-95. doi: 10.13832/j.jnpe.2021.04.0091

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Research on Judgment of Supercritical Water Heat Transfer Deterioration Based on Machine Learning

doi: 10.13832/j.jnpe.2021.04.0091

1.
School of Information Engineering and Computer Science, Hebei Finance University, Baoding, Hebei, 071051, China
2.
School of Nuclear Science and Engineering, North China Electric Power University, Beijing, 102206, China
3.
School of Energy and Environment, Southeast University, Nanjing, 211189, China
4.
CNNC Key Laboratory on Nuclear Reactor Thermal Hydraulics Technology, Nuclear Power Institute of China, Chengdu, 610213, China

Received Date: 2020-06-16
Rev Recd Date: 2021-04-25
Publish Date: 2021-08-15

Abstract

Abstract

In order to further improve the safety and stability of supercritical water reactors, avoid the occurrence of the heat transfer deterioration in supercritical water, based on the existing experimental data of supercritical water heat transfer, using several main machine learning algorithms, the classification and judgment and prediction accuracy analysis of the experimental parameter state points of supercritical water were made to determine the occurrence of the heat transfer deterioration. The research results show that the random forest algorithm has the highest average prediction accuracy for the test data, reaching about 97.8%. The average prediction accuracy of the K-nearest neighbor algorithm is the lowest, but it also reaches about 91%. At the same time, the importance of various influence parameters on the selection of heat transfer deterioration was analyzed.The most important parameter related to the heat transfer deterioration judgment is the specific enthalpy, and the second important parameter is the heat transfer coefficient. The third important parameter with contribution to the heat transfer deterioration is the pipe diameter.
- Supercritical water,
- Heat transfer deterioration,
- Machine learning,
- Random forest,
- K-nearest neighbor(KNN)

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References(12)

References

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