Effective thermal conductivity of ellipsoidal inclusion-reinforced composites: Data-driven prediction.

The prediction of thermal conductivity of aligned arrangement ellipsoidal particles reinforced composites is realized by a data-driven method based on machine learning models. The models of random forest regression, polynomial regression, gradient boosting regression, and multi-layer perceptron regression are compared and analyzed. Furthermore, the effects of the thermal conductivity ratio of matrix and particle, volume fraction, aspect ratio, orientation angle of particles, and interfacial thermal resistance on the thermal conductivity of composites were analyzed by machine learning model. The results show that the data-driven model based on random forest regression has the highest accuracy in predicting the thermal conductivity of ellipsoidal particle reinforced composites, with R 2 = 0.994 and MAPE = 1.82%. However, the model has errors in the details of predicting the trend of thermal conductivity change. Therefore, the data-driven model is used to calibrate the parameters of the theoretical model. The modified theoretical model combines the advantages of high numerical accuracy of the machine learning model with the advantages of accurate prediction of the thermal conductivity trend of the theoretical model. • A data-driven model was constructed to accurately predict the thermal conductivity of composites. • The effects of multiple parameters on thermal conductivity were quantitatively evaluated using the random forest model. • For the first time, the data-driven model is used to construct the theoretical model, improving its accuracy. [ABSTRACT FROM AUTHOR]