Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Research on prediction and analysis of supercritical water heat transfer coefficient based on support vector machine

  • Ma Dongliang (School of Information Engineering and Computer Science, Hebei Finance University) ;
  • Li Yi (School of Finance Technology, Hebei Finance University) ;
  • Zhou Tao (School of Energy and Environment, Southeast University) ;
  • Huang Yanping (Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China)
  • Received : 2023.02.01
  • Accepted : 2023.07.23
  • Published : 2023.11.25
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Abstract

In order to better perform thermal hydraulic calculation and analysis of supercritical water reactor, based on the experimental data of supercritical water, the model training and predictive analysis of the heat transfer coefficient of supercritical water were carried out by using the support vector machine (SVM) algorithm. The changes in the prediction accuracy of the supercritical water heat transfer coefficient are analyzed by the changes of the regularization penalty parameter C, the slack variable epsilon and the Gaussian kernel function parameter gamma. The predicted value of the SVM model obtained after parameter optimization and the actual experimental test data are analyzed for data verification. The research results show that: the normalization of the data has a great influence on the prediction results. The slack variable has a relatively small influence on the accuracy change range of the predicted heat transfer coefficient. The change of gamma has the greatest impact on the accuracy of the heat transfer coefficient. Compared with the calculation results of traditional empirical formula methods, the trained algorithm model using SVM has smaller average error and standard deviations. Using the SVM trained algorithm model, the heat transfer coefficient of supercritical water can be effectively predicted and analyzed.

Keywords

Acknowledgement

This research is financially supported by Beijing natural science foundation (3172032) and Special Fund for "Double First Class" Discipline Construction of Southeast University (3203002104A2).

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