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During the development of the aerodynamic properties of fore coming road vehicles down scaled models are often used in the initial phase. However, if scale models are to be utilised even further in the aerodynamic development they have to include geometrical representatives of most of the components found in the real vehicle. As the cooling package is one of the biggest single generators of aerodynamic drag the heat exchangers are essential to include in a wind tunnel model. However, due mainly to limitations in manufacturing techniques it is complicated to make a down scaled heat exchanger and instead functional dummy heat exchangers have to be developed for scaled wind tunnel models. In this work a Computational Fluid Dynamics (CFD) code has been used to show that it is important that the simplified heat exchanger model has to be of comparable size to that of the full scale unit.

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.

The aerodynamic drag, fuel consumption and hence CO2 emissions, of a road vehicle depend strongly on its flow structures and the pressure drag generated. The rear end flow which is an area of complex three-dimensional flow structures, contributes to the wake development and the overall aerodynamic performance of the vehicle. This paper seeks to provide improved insight into this flow region to better inform future drag reduction strategies. Using experimental and numerical techniques, two vehicle shapes have been studied; a 30% scale model of a Volvo S60 representing a 2003MY vehicle and a full scale 2010MY S60. First the surface topology of the rear end (rear window and trunk deck) of both configurations is analysed, using paint to visualise the skin friction pattern. By means of critical points, the pattern is characterized and changes are identified studying the location and type of the occurring singularities.

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.

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.