Validation of the predictions is done on the basis of detailed comparisons to experimental wind-tunnel data. Results for lift and drag are found to compare favorably to the experiments, with some moderate discrepancies in predicted rear lift. Point surface-pressure measurements, oil-streak images and maps of total pressure in the flow field demonstrate the approach's capabilities to predict the fine detail of complex flow regimes found in automotive aerodynamics.
Standard DES methods can cost an order of magnitude more than traditional methods, but optimization and automation of mesh generation, setup and solution algorithms ensure quick turn-around times. Due to the fully parallel nature of these components, the entire process can be executed in a distributed fashion. Efficient solution algorithms provide exceptional accuracy when compared to Reynolds-averaged approaches without sacrificing stability, even when the flow exhibits high Courant numbers.
The proposed methodology is highly customizable, which allows for targeted developments to suit the individual needs of aerodynamics CFD. On the basis of the results presented here, the methodology is found to be appropriate and suitable for use in the industrial development process.