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. The main advantages over other flow visualization methods, such as oil paint, is that experimental facilities are not contaminated and statistical data can be extracted.
The investigated surface pattern shows a symmetric flow on the entire rear end section of the passenger car. The flow field on the roof, backlight, and upper trunk deck is attached almost everywhere. However, two small regions indicate the presence of two counter-rotating vortices at the lower edge of the backlight (rear window). Those vortices are also detectable in the distribution of the tuft angle standard deviation. A bifurcation line is present at each side of the trunk due to the streamwise vortices originating at the C-pillars. The tuft streamlines created with this novel tuft method are compared to a standard oil paint flow visualization to validate the calculated tuft flow pattern. A critical comparison between the methods confirms that the flow tuft analysis algorithm functions flawlessly as a highly detailed flow analysis tool without the mess of oil paint.