• RAKHI S
• Mr. Vignesh S

Formula 1 cars are meticulously designed to attain exceptional speeds, with two pivotal forces significantly influencing their performance: drag and downforce. Notably, a reduction in downforce amplifies the drag effect, thereby impeding speed. In the intricate aerodynamic landscape of Formula 1 cars, the distribution of forces influencing their speed is a critical consideration for performance optimization. A revealing insight into this complex equation is that approximately 30 of the total drag experienced by a Formula 1 car can be attributed to its rear wing. The relationship between downforce and speed is a key dynamic in this context. Downforce, generated by the aerodynamic elements of the car, presses it onto the track, providing essential traction and stability, particularly during high-speed maneuvers and cornering. However, a trade-off exists: reducing downforce leads to a decrease in drag, but it also compromises the car’s ability to maintain optimal speed and control. In this proposed system, the objective is to optimize the position of the car’s rear wing to enhance performance and achieve higher speeds on the track. By employing the Backpropagation using machine learning techniques combined with a computational fluid dynamics tool, the most optimal design for an F1 rear wing can be quickly predicted. Creating the optimized model is aimed at maximizing downforce while minimizing drag, striking a delicate balance that enables the car to maintain high speeds through both corners and straights.

Computational fluid dynamics, Machine learning, Rear wing, Formula 1, Backpropagation algorithm, Artificial neural networks

"Rear Wing Optimization of F1 cars using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.h747-h753, March-2024, Available :http://www.jetir.org/papers/JETIR2403800.pdf

"Rear Wing Optimization of F1 cars using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. pph747-h753, March-2024, Available at : http://www.jetir.org/papers/JETIR2403800.pdf