Aerodynamic Analysis and Design of a Sliding Drag Reduction System Using Graph Neural Networks

To maximize competitive performance in motorsports, balancing high downforce for cornering with low drag for straight–line speed is essential. This paper presents the development and optimization of a sliding Drag Reduction System (DRS) integrated with a ducktail guide for a Student Formula racing car. To overcome the computational costs and time constraints of conventional CFD–based iterative design, a Graph Neural Network (GNN) surrogate model was developed to predict aerodynamic coefficients. Unlike traditional models, the GNN directly learns from the geometric graph structure of the multi–element wing, enabling near–instantaneous and highly accurate predictions. CFD results indicated that activating the DRS reduced drag from 82.68 N to 25.51 N, improving the lift–to–drag ratio from 1.67 to 2.67. The GNN surrogate model achieved an R2 value exceeding 0.99, demonstrating exceptional predictive fidelity compared to high–resolution simulations. Physical track testing with a Formula SAE vehicle corroborated these findings, showing a 4.6% improvement in 50 m acceleration and a 5.8% increase in maximum speed. This research establishes that GNN–based surrogate models can significantly accelerate the design and optimization of complex variable aerodynamic systems, providing a robust framework for performance enhancement in racing applications.