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The phenomenon of three-dimensional flow separation is and has been in the focus of many researchers. An improved understanding of the physics and the driving forces is desired to be able to improve numerical simulations and to minimize aerodynamic drag over bluff bodies. To investigate the sources of separation one wants to understand what happens at the surface when the flow starts to detach and the upwelling of the streamlines becomes strong. This observation of a flow leaving the surface could be captured by investigating the limiting streamlines and surface parameters as pressure, vorticity or the shear stress. In this paper, numerical methods are used to investigate the surface pressure and flow patterns on a sedan passenger vehicle. Observed limiting streamlines are compared to the pressure distribution and their correlation is shown. For this investigation the region behind the antenna and behind the wheel arch, are pointed out and studied in detail.

Efforts towards ever more energy efficient passenger cars have become one of the largest challenges of the automotive industry. This involves numerous different fields of engineering, and every finished model is always a compromise between different requirements. Passenger car aerodynamics is no exception; the shape of the exterior is often dictated by styling, engine bay region by packaging issues etcetera. Wheel design is also a compromise between different requirements such as aerodynamic drag and brake cooling, but as the wheels and wheel housings are responsible for up to a quarter of the overall aerodynamic drag on a modern passenger car, it is not surprising that efforts are put towards improving the wheel aerodynamics.

Flow visualization techniques are widely used in aerodynamics to investigate the surface trace pattern. In this experimental investigation, the surface flow pattern over the rear end of a full-scale passenger car is studied using tufts. The movement of the tufts is recorded with a DSLR still camera, which continuously takes pictures. A novel and efficient tuft image processing algorithm has been developed to extract the tuft orientations in each image. This allows the extraction of the mean tuft angle and other such statistics. From the extracted tuft angles, streamline plots are created to identify points of interest, such as saddle points as well as separation and reattachment lines. Furthermore, the information about the tuft orientation in each time step allows studying steady and unsteady flow phenomena. Hence, the tuft image processing algorithm provides more detailed information about the surface flow than the traditional tuft method.