This study presents a comparative assessment of two machine learning approaches for predicting aerodynamic drag coefficients (Cd) in automotive vehicle designs using data derived from computational fluid dynamics (CFD) simulations. The first approach employs traditional regression models trained on structured parametric data generated through controlled geometric variations, while the second approach integrates unstructured point-cloud geometry with structured metadata using a multi-modal deep learning framework.
Both methods are evaluated within their respective contexts to understand their strengths, limitations and potential roles in automotive aerodynamic workflows. Rather than identifying a single best approach, the study highlights how these methods address different design needs and resource constraints, providing insights for future hybrid strategies that combine interpretability with geometric sensitivity.
The work aims to establish a foundation for data-driven aerodynamic analysis in automotive design, emphasizing the role of machine learning (ML) in enhancing simulation-based engineering processes.