Aerodynamic investigation and flow control on DrivAer models using numerical (CFD) methods
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Abstract (EN)
Drag reduction is one of the most critical subjects for vehicles, especially considering the emerging trend of electric vehicles that have been known to drive range and efficiency issues. In this work, deflector plates were tested numerically as a passive flow control element at the upper and lower regions of the rear end of the closed body estateback, fastback, and notchback DrivAer generic car models. Plate slant angles varied to find optimum deflectors. CFD simulations have been conducted by the k-ω SST RANS turbulence model and the setup of the base model is verified with the drag force coefficient of previous experimental works. Then, parametric, genetic algorithm response surface and adjoint optimization stages were applied on the upper and lower deflectors, respectively. Consequently, simultaneous usage of these two deflectors with optimum angles resulted in reductions of CD by 17.90%, 4.82%, and 7.25% for estateback, fastback, and notchback respectively. However, detailed qualitative analysis is shown that upper and lower deflectors have different drag reduction mechanisms at the wake. The upper deflector reduces wake statistics and attains pressure recovery at the back surface by improved downwash for all models. On the other hand, the lower deflector restricts airflow beneath the car for the estateback. Hereby, this provides pressure recovery at the slanted diffuser section by blocking its functionality while the restricted airflow is deflected to the upperbody and results in pressure recovery at the rear surface, too by improved upwash, although it increases the wake region. For the fastback model, the lower deflector provides flow control by improving the upwash, while for the notchback model, it provides a flow control by preventing the flow interaction between the downwash and the upwash.
Author
Oğuz Baş
How to Cite
Oğuz Baş (Doctorate thesis). Aerodynamic investigation and flow control on DrivAer models using numerical (CFD) methods, 2023, Çukurova University.
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