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The experimental investigation was conducted with a 25%-scaled realistic car model called “DrivAer” mounted in a wind tunnel. This model includes geometric elements of a BMW 3 series and an Audi A4, accommodating modular, rear-end geometries so that it represents a generalized modern production car. The measurements were done with two different DrivAer rear end configurations (fastback and notchback) at varying side-wind conditions and a Reynolds number of up to Re=3.2·106. An array of more than 300 pressure ports distributed over the entire rear section measured the temporal pressure distribution. Additionally, extensive flow visualizations were conducted. The combination of flow visualization, and spatially and temporally resolved surface pressure measurements enables a deep insight into the flow field characteristics and underlying mechanisms.

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.