Analysis of conjugate convective heat transfer in a fully insulated enclosure at various inclination angles in ecological problem by CFD and machine learning methods
摘要
The study of conjugate convection plays a key role in heat transfer systems such as collectors, heat exchangers, electrical cooling, and thermal insulation. In this paper, we investigated conjugate natural convection in a square inclined cavity with a heat-conducting baffle using computational fluid dynamics and machine learning. The effect of inclination angles on conjugate natural convection was examined, ranging from 0 to 330°. The goal of the study was to improve the accuracy and efficiency of predicting temperature changes in the computational domain. To achieve this goal, three regression models were developed and tested: gradient boosting, random forest, and decision tree. The Computational fluid dynamics (CFD) model solved the Navier-Stokes governing equations using the control volume method. Analysis of the results showed that the gradient boosting model demonstrated the best performance, with high values of the coefficient of determination (best case 0.988) and minimal prediction errors. The random forest model also demonstrated good results, although slightly inferior to the gradient boosting model. The tree model demonstrated the least effective solution among all the models considered, but still achieved acceptable accuracy. It should be noted that the gradient boosting model yielded an R2 of 0.987, a Mean Squared Error (MSE) of 0.033, a Mean Absolute Error (MAE) of 0.13, and a Mean Absolute Percentage Error (MAPE) of 0.00044. Thus, the obtained error values are the lowest compared to other machine learning models. The obtained results confirm the potential of machine learning methods for quickly obtaining solutions to fluid dynamics and heat transfer modeling problems.