Prediction of Heat Transfer Performance of Finned Tubes Using CFD and Random Forest

To address the issues of high computational cost, long calculation time, and complex parameter coupling in predicting the heat transfer performance of finned structures, this study takes a typical finned heat exchanger as the research object and adopts a combined approach of numerical simulation and data-driven modeling. First, a three-dimensional model of the finned structure is established based on the CFD method, and the heat transfer performance under different structural parameters and operating conditions is simulated to obtain a dataset. On this basis, a random forest algorithm is introduced to develop a predictive model, in which geometric parameters and operating conditions are used as input variables, and the heat transfer coefficient is taken as the output target. By comparing the simulation results with the predicted results, the effectiveness of the random forest model is validated. The results indicate that the proposed method can significantly reduce computational time while maintaining prediction accuracy, providing a feasible approach for the optimal design of finned heat exchanger structures.