• Developed a validated numerical model of fin-assisted natural convection in square cavities. • Generated a large dataset (40,590 cases) spanning wide geometric and thermal parameter ranges. • Compared five ML regression models for predicting average Nusselt numbers on hot and cold walls. • Decision Tree and Ensemble models achieved R 2 > 0.999 with minimal error (MAE 0.94), with the Decision Tree and Ensemble methods outperforming the others across all datasets. Specifically, the cold wall models achieved R 2 > 0.999, RMSE < 0.05, and MAE < 0.027, whereas the hot wall models achieved RMSE ≈ 0.023 and MAE < 0.0145. Feature importance analysis revealed Ra as the most influential parameter in both regimes, with fin length having a significant impact on hot-wall heat transfer. The ML-based models effectively captured nonlinear interactions between geometric and thermal parameters, surpassing traditional empirical methods in both accuracy and efficiency. These results demonstrate the suitability of ML-based models for real-time prediction, thermal design optimization, and control of convection-dominated systems.
Bawazeer et al. (Wed,) studied this question.