Enhancing formula student car performance: Nose shape optimization via adjoint method

Abstract

For a formula student vehicle, the nose is the first point of flow contact and decides the flow direction to help other aero components work efficiently. Therefore, an optimized nose shape is crucial for the optimum running condition of the vehicle. In recent years, Gradient-based optimization in the automotive industry has emerged with an adjoint approach. The adjoint solver is used to modify the geometry such as the nose of a car to the best optimal shape with respect to other design variables. Hence, the present study encompasses an examination conducted under six distinct Reynolds number conditions, specifically, 8.15 × 105, 9.05 × 105, 9.96 × 105, 10.87 × 105, 11.77 × 105, and 12.68 × 105. The simulation setup is validated using the Ahmed Body's 30° slant angle. The setup uses the k-omega SST turbulence model to capture the airflow phenomenon over the racing car. The simulation setup shows only a 0.07 % variation from the existing experimental findings. The results are represented in terms of different 2D plots, contours and streamline plots. The optimized nose has 27 % less drag and consumes 27.09 % less fuel than the base model at a Reynolds number of 10.8 × 105. To the automotive industry and race community, this optimization technique will be expected to enhance the vehicle's overall performance.

Keywords

• Gradient-based optimization
• Adjoint CFD
• FSAE
• Automotive industry
• Aerodynamics