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In aerodynamic development of ground vehicles, the use of Computational Fluid Dynamics (CFD) is crucial for improving the aerodynamic performance, stability and comfort of the vehicle. Simulation time and accuracy are two key factors of a well working CFD procedure. Using scale-resolving simulations, accurate predictions of the flow field and aerodynamic forces are possible, but often leads to long simulation time. For a given solver, one of the most significant aspects of the simulation time/cost is the temporal resolution. In this study, this aspect is investigated using the realistic vehicle model DrivAer with the notchback geometry as the test case. To ensure a direct and accurate comparison with wind tunnel measurements, performed at TU Berlin, a large section of the wind tunnel is included in the simulation domain. All simulations are performed at a Reynolds number of 3.12 million, based on the vehicle length.

We describe experiments and numerical modeling of snow surface contamination on two simplified automotive bluff bodies: The Ahmed body and a wedge. The purpose was twofold: 1) To obtain well defined experimental results of snow contamination on simple geometries; 2) To propose a numerical modeling approach for snow contamination. The experiments were performed in a climatic wind tunnel using a snow cannon at −15 °C and the results show that the snow accumulation depends on the aerodynamics of the studied bluff bodies. Snow accumulates on surfaces in proximity to the aerodynamic wakes of the bodies and characteristic snow patterns are obtained on side surfaces. The numerical modeling approach consisted of an aerodynamic setup coupled with Lagrangian particle tracking. Particles were determined to adhere or rebound depending on an adhesion model combined with a resuspension criterion.

The automotive industry strives to develop high quality vehicles in a short period of time that satisfy the consumer needs and stand out in the competition. Full exploitation of simulation and Computer-Aided Engineering (CAE) tools can enable quick evaluation of different vehicle concepts and setups without the need of building physical prototypes. Addressing the aforementioned statements this paper presents a method for optimising the Electric Power-Assisted Steering (EPAS) ECU parameters employing solely CAE. The objective of the optimisation is to achieve a desired steering response. The developed process is tested on three specific steering metrics (friction feel, torque build-up and torque deadband) for two function parameters (basic steering torque and active return) of the EPAS. The optimisation method enabled all metrics to fall successfully within the target range.