A CFD and random forest-based numerical study of finned heat dissipation structures

Abstract

In order to overcome the limitations of conventional CFD approaches, which typically involve prohibitive computational overhead, extended processing durations, and intricate parameter interdependencies when predicting the thermal behavior of finned tubes, this research presents an integrated framework that merges numerical simulation with data - driven modeling to analyze a representative finned dissipation architecture. Initially, a spatial three - dimensional CFD grid was constructed for the finned geometry to resolve thetemperature fields and fluid motions across a diverse array of geometric configurations and environmental factors, thereby compiling a robust training dataset. Based on the above group simulation results, a Random Forest (RF) machine learning framework was used to build an intelligent prediction model for the desired thermal behaviour, and fin shape variables and functional states were selected as independent factors and heat transfer coefficient as the final objective. Through a systematic comparative assessment between the high-fidelity CFD resolutions and the RF-derived estimations, the generalizability and predictive fidelity of the decision-tree ensemble approach are rigorously established. Findings suggest that the fin profile height exerts the most substantial impact on thermal performance, as the heat flux attains its peak magnitude when the fin dimension is set at 20 mm. On the test dataset, the average relative error of the random forest model is only 0.68%, and the prediction results show good agreement with the CFD simulation results. This suggested technique can substantially decrease processing duration while preserving forecasting precision, offering a viable strategy for coefficient refinement and creation of finned thermal exchange frameworks.