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The foremost aim of the work presented in this paper is to improve fuel economy and decrease CO2 emissions by reducing the aerodynamic drag of passenger vehicles. In vehicle development, computer aided engineering (CAE) methods have become a development driver tool rather than a design assessment tool. Exploring and developing the capabilities of current CAE tools is therefore of great importance. An efficient method for vehicle shape optimization has been developed using recent years' advancements in neural networks and evolutionary optimization. The proposed method requires the definition of design variables as the only manual work. The optimization is performed on a solver approximation instead of the real solver, which considerably reduces computation time. A database is generated from simulations of sampled configurations within the pre-defined design space. The database is used to train an artificial neural network which acts as an approximation to the simulations.

Many aerodynamic wind tunnels used for testing of ground vehicles have advanced ground simulation systems to account for the relative motion between the ground and the vehicle. One commonly used approach for ground simulation is a five belt system, where moving belts are used, often in conjunction with distributed suction and tangential blowing that reduces the displacement thickness of the boundary layer along the wind tunnel floor. This paper investigates the effects from aft-belt tangential blowing in the Volvo Cars Aerodynamic wind tunnel. First the uniformity of the boundary layer thickness downstream of the blowing slots is examined in the empty tunnel. This is followed by investigations of how the measured performance of different vehicle types in several configurations, typically tested in routine aerodynamic development work, depends on whether the tangential blowing system is active or not.