Study on Heat Transfer Surrogate Model of Supercritical Pressure Hydrocarbon Fuel Under Rotating Condition
In order to predict the convective heat transfer performance of supercritical pressure hydrocarbon fuel under rotating condition,a heat transfer surrogate model for Nu number predic-tion was established based on the LightGBM algorithm.For different experimental sections in the U-shaped channel,single-section models and a unified prediction model based on all data were es-tablished,and the prediction performance of the models on the data set was studied,while feature importance was used to further understand the physical laws of the heat transfer process.The re-sults show that the prediction model errors for the centrifugal section,the horizontal section,and the centripetal section are 2.47％,5.09％,and 4.48％,respectively,and the average error of the three models on all data is 4.06％,while the unified prediction model with error of 3.32％performs better.It shows that even if there are differences in the heat transfer laws of different experimental sections,the similarity in the data helps to improve the performance of the model.
machine learningLightGBMrotating conditionsupercritical pressure hydrocarbon fuelturbulent heat transfer
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