Multi-Objective Aerodynamic Optimization of Ride Height and Rake Angle in a Sedan Car Using CFD and Machine Learning

Mahdi Kheirkhah; Ehsan Roohi; Mahmoud Pasandidehfard

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arXiv · 2509.02917

Multi-Objective Aerodynamic Optimization of Ride Height and Rake Angle in a Sedan Car Using CFD and Machine Learning

Mahdi Kheirkhah·Ehsan Roohi·Mahmoud Pasandidehfard

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Abstract

This study investigates the aerodynamic performance of an Audi A4 sedan using CFD analysis. A 3D model was developed in SolidWorks and validated against DrivAer Notchback wind-tunnel data, showing only a 3.25 percent deviation in drag coefficient (Cd). Ride height varied from 1.336 to 1.536 m and rake angle from 0 to 5 degrees, across four Reynolds numbers. Gradient Boosting emerged as the most accurate predictive model (R square = 0.97 for Cd and 0.96 for lift coefficient, Cl), outperforming Random Forest and LightGBM. Differential Evolution optimization was performed under balanced, drag-focused, and downforce-focused conditions. Reynolds number had minimal impact on optimum location; therefore, detailed results are reported for one Reynolds number, with other Re showing similar trends. The baseline geometry exhibited Cd = 0.313 and Cl = 0.0288. Balanced optimization achieved Cd = 0.287 and Cl = - 0.0826. Minimum drag condition reached Cd = 0.285 with slight positive lift (Cl = 0.0142), while maximum downforce optimization reached Cl = - 0.1084 with a 6.71 percent drag penalty (Cd = 0.334). Near-optimal solutions were found within ride heights of 1.341 to 1.365 m and rakes of 0.158 to 4.610 degrees, indicating robust aerodynamic performance. Machine learning predictions were further validated against CFD with a small error in Cd.

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BibTeX RIS

Mahdi Kheirkhah, Ehsan Roohi, Mahmoud Pasandidehfard. 2025-09-03. Multi-Objective Aerodynamic Optimization of Ride Height and Rake Angle in a Sedan Car Using CFD and Machine Learning. https://arxiv.org/abs/2509.02917

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