Enhancing vehicle performance through the application of airfoils as spoilers with movable trailing edge.

Abstract

Background: Vehicle safety and stability are critical in the automotive industry, with aerodynamics playing a key role in enhancing these attributes. Spoilers, when effectively designed, can significantly influence airflow, downforce, and lift. This study investigates the aerodynamic performance of spoilers modeled as airfoils with adjustable trailing edges, aiming to dynamically control aerodynamic forces and improve vehicle stability and performance.

Methods: Computational Fluid Dynamics (CFD) simulations were conducted using ANSYS Fluent® to analyze the impact of varying trailing edge angles (AOTE) on aerodynamic forces. A detailed Tesla vehicle model was created in CATIA™, and simulations were performed across a speed range of 120-350 km/h. The Shear Stress Transport (SST) k-ω turbulence model was employed to ensure accurate flow prediction. A wind tunnel domain and grid independence validation were used to ensure numerical reliability. Boundary conditions included velocity inlets, pressure outlets, and no-slip wall boundaries.

Results: Adjusting the trailing edge angle produced significant variations in lift and downforce. At an angle of 30°, the negative lift (downforce) increased by up to 36%. At 0°, it increased by up to 17%. During acceleration phases, the controlled generation of positive lift improved aerodynamic efficiency, yielding a total lift increase of up to 15%. The simulated drag coefficient was 0.256, differing by 6% from Tesla's reported value of 0.24, primarily due to mesh refinement level and geometric simplifications.

Conclusions: This study demonstrates that a spoiler with a movable trailing edge can significantly enhance vehicle handling, acceleration, and aerodynamic stability by actively modulating lift and downforce. The findings support the integration of active aerodynamic control systems in vehicle design. Future research will focus on control system development and experimental validation under real-world driving conditions.