Hybrid Aerodynamic Optimization of an Urban Concept Vehicle Using GA–fmincon and CFD Validation

Abstrak

This study develops an aerodynamic optimization framework for energy-efficient Urban Concept vehicles, integrating low-order aerodynamic formulations, a hybrid genetic algorithm (GA)–fmincon, and CFD validated in ANSYS Fluent. It focuses on thirteen geometric parameters that affect wake behavior, rear pressure recovery, and underbody flow. The optimization process leverages a low-order approach to improve computational efficiency during multidimensional exploration, while final validation is performed using CFD with an SST k–ω turbulence model under steady, incompressible RANS conditions. Numerical validation on the Ahmed Body shows a drag coefficient error of less than 5% compared to experimental data. The results from the GA–fmincon optimization indicate that the optimal geometric configuration can reduce the drag coefficient by about 30% compared to the initial design and by about 40.2% after 3D modeling is validated using CFD in ANSYS Fluent. Flow analysis shows that the optimized configuration produces a smaller wake region, more stable rear flow, and better pressure recovery at the back of the vehicle. The framework developed in this study shows potential as an approach for early aerodynamic exploration before conducting high-quality CFD simulations.

Kata kunci

• Aerodynamic
• Algorithm
• CFD
• Urban
• Vehicle