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The aerodynamics of the front wing of modern race cars are critical to the performance of the vehicle. The Formula 1 line up represents the state of the art in this field as there are some very complex aerodynamic designs on display. It is strange, however, that there is no agreement on twist direction for the multiple wing sections of the front wing. This paper addresses this question by posing it as an optimization problem. The geometry of the wings has been simplified so that the twist of the upper sections could be studied in isolation. The whole assembly consisted of only two high lift surfaces. The forward wing remained fixed for the study, and twist of the secondary wing became the primary focus. Its geometry was generated by lofting a set of cross-sections at specified angles to create the surface. The resulting geometry was automatically meshed and then evaluated using CFD. This fully automated process was then used to find an ideal twist distribution of the secondary wing.

High quality mesh generation technology coupled with a robust shape deformation technique enables large design space exploration for optimization without the need to remesh the geometry. To demonstrate this, we present a collection of best practices for cleaning complex analytic CAD data that together with a robust grid generation algorithm enable the automatic generation of high quality boundary layer resolved grids that retain their quality when morphed during the optimization process. The case study for this work is the DrivAer model developed by the Institute of Aerodynamics and Fluid Mechanics at the Technische Universität München. The first step in the proposed automated optimization framework is to use a technique called Solid Meshing to heal faults in the provided geometry and recover its original engineering intent.