2025/09/03 by Mahdi Kheirkhah, Kheirkhah, Mahdi, Ehsan Roohi +3
Engineering · #Aerodynamics and Fluid Dynamics Research #Computational Fluid Dynamics and Aerodynamics #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Fluid Dynamics and Vibration Analysis
paper · pdf · doi:10.48550/arxiv.2509.02917
openalex publication_date 2025/09/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.