From CFD Simulations to Machine Learning: A Comparative Study of ML Tools for Aerodynamic Drag Prediction

This study presents the development of machine learning frameworks for predicting aerodynamic drag coefficients in automotive vehicle designs using data derived from computational fluid dynamics (CFD) simulations. The input data includes both structured tabulated design parameters and unstructured geometric representations in the form of point clouds, enabling a multi-modal approach to aerodynamic modeling. A range of machine learning models was explored, from traditional regression techniques to advanced deep learning architectures such as graph neural networks (GNNs), each tailored to handle specific data formats. These frameworks were designed to integrate seamlessly with CFD workflows, supporting efficient training and evaluation using standard regression metrics. The work aims to establish a foundation for data-driven aerodynamic analysis in automotive design, emphasizing the role of machine learning in enhancing simulation-based engineering processes.