Aerodynamic optimization of Gurney flap parameters for a Formula 1 wing using experimental and coupled GA-ANN-CFD methods

This study presents a comprehensive numerical and experimental investigation of the aerodynamic performance of Gurney flaps applied to an inverted multi-element airfoil representative of a Formula 1 front wing. A coupled Genetic Algorithm & Artificial Neural Network & Computational Fluid Dynamics (GA-ANN-CFD) framework is developed and applied to a three-element airfoil at a Reynolds number of 500 × 10 3 . Four baseline configurations, defined by flap orientation (vertical and surface-normal) and geometry (solid and slotted), are independently optimized, yielding eight optimal designs. The results demonstrate that flap height is the dominant parameter governing downforce generation, with maximum-height configurations increasing downforce by up to 11.24%, albeit with a moderate reduction in aerodynamic efficiency. In contrast, smaller-height slotted configurations provide a more favorable balance between downforce and drag. The experimental investigation evaluates nine Gurney flap geometries, combining three heights (1.5%, 3%, and 5% chord) and three installation angles (54°, 72°, and 90°), over Reynolds numbers ranging from 250 × 10 3 to 500 × 10 3 . Surface pressure measurements and wake velocity profiles reveal that increasing flap height significantly enhances pressure differentials and wake momentum deficit, leading to higher downforce. Overall, the results identify flap height as the primary design parameter governing downforce generation, while installation angle and slotting provide secondary control over aerodynamic efficiency and wake characteristics.