Evaluating Machine Learning as an Alternative to CFD for Heat Transfer Modeling

Abstract

This study investigates the feasibility of replacing computational fluid dynamics (CFD) techniques with machine learning (ML) models for heat transfer modeling, focusing on forced convection processes. The research leverages artificial intelligence algorithms, specifically random forests (RF), super-gradient boosting (SGBoost), and artificial neural networks (ANN), to predict key heat transfer metrics such as Reynolds number, nanoparticle size, volume percentage, and Nusselt number. Using a dataset of 210 data points, the ML models are systematically applied to forecast heat transfer outcomes. Model performance is evaluated using Root Mean Squared Error (RMSE), Pearson’s correlation coefficient (r), and Mean Absolute Error (MAE). Results indicate that SGBoost achieves an accuracy of 91%, RF 90%, and ANN 86%, with corresponding RMSE values of 1.07, 1.65, and 16.1, respectively. These findings demonstrate that ML models not only deliver high accuracy and predictive power but also outperform traditional CFD methods in computational efficiency and adaptability to new data. Unlike conventional techniques that rely on predefined physical models and require extensive computational resources, ML approaches streamline the modeling process and enhance accessibility for diverse engineering applications. This study underscores the transformative potential of ML in advancing thermal analysis and optimizing forced convection heat transfer simulations.