Session: 15-01-01: ASME International Undergraduate Research and Design Exposition

Paper Number: 96002

96002 - Innovation and Study of Formula 1 Race Car Rear Wings Through Computational Fluid Dynamics and Machine Learning 

This project was pursued because of the technical beauty and complexity of Formula 1 cars. By designing the most optimal rear wing for each racetrack and environmental condition, F1 cars can have faster and closer racing, improving the viewing experience of over 500 million F1 fans around the world. As the key to winning a Formula 1 Grand Prix, the aerodynamic performance of a racing car is a vital part of the car design. In this research project, computational fluid dynamics is used to investigate how the rear wing design could affect the drag and ground effect of a racing car. While drag impedes straight-line speed, the ground effect improves cornering speed. So, the best design can minimize drag as much as possible while also producing as much ground effect as possible. To complete this objective, first, a series of rear wings with different shapes, angles, and thicknesses are designed. Second, the aerodynamic performance of each wing is tested using ANSYS Fluent Computational Fluid Dynamics (CFD) software by using complex fluid mechanics equations and principles like the Law of Conservation of Mass and the Navier-Stokes equations. Then, machine learning is incorporated in this project by using a backpropagation (BP) artificial neural network model to quickly predict the lift and drag coefficients of an F1 wing to further optimize the rear wing design by using the ANSYS Fluent-found data as a basis to find very precise parameters of the best rear wing. Finally, a full-sized 3-dimensional F1 car model is developed using Solidworks. After amalgamating the optimized rear wing design to the 3-dimensional F1 car, ANSYS Fluent is used to simulate the turbulent flow around the car to find the velocity contour, pressure contour, as well as streamflow lines. Simulation results show that with a well-designed rear wing, the drag coefficient of a racing car could be greatly reduced, and the ground effect is enhanced because the wing has a great effect on the wake region of the turbulent flows, which further affects the aerodynamic performance of an F1 car. The research has its significance in the following areas: first, the findings create a better understanding of how rear wings affect flow properties over F1 cars; second, the findings from the testing to find the most optimal rear wing highlight what factors like camber or thickness are the most important in designing the most optimal rear wing; lastly, the testing to find the most optimal rear wing can also be used to better explain what conditions certain airfoils excel or underperform at. So, the design of the most optimal rear wing for each racetrack and environmental condition allows F1 cars to have faster and closer racing. An F1 car with an optimized rear wing design also becomes more efficient and thus more fuel-saving and sustainable, further supporting the global fight against climate change. Fuel consumption in F1 is a major problem right now. Annually, 256,000 tonnes of carbon dioxide are polluted in the sport, the equivalent of powering roughly 30,000 houses in the UK in the same period. Finally, F1 technology is also often used by car companies who produce popular road-legal cars to help make better cars for the average driver as well. With the further research of rear wing airfoils in this project, road-legal cars with spoilers can also have lower fuel consumption and take people to places they need to be in less time, positively contributing to the global community for many generations to come.

Presenting Author: Ken Cheng Crescent School

Presenting Author Biography: Ken Cheng is a Canadian high school student in Grade 10 at Crescent School in Toronto, Ontario. At school, he consistently maintains a high academic standing relative to his peers. Last year, he won the Jerry Friedman Award, the award that is gifted to the student with the highest average in Grade 9. Ken also played a major role in leading his school's VEX Robotics team to two provincial championships before the pandemic. His academic and research interests are in Aeronautical Engineering and Computer Science. He wants to continuously use his academic prowess to make a lasting impact on the future of Aeronautical Engineering by innovating to address our major real-world problems and fill the biggest gaps of potential in aviation. Ken hopes to follow in the footsteps of researchers that have contributed in great ways to the betterment of humanity by emulating their life-changing finds, whether it's saving billions of lives through creating new vaccines or innovating self-driving cars for driver and passenger safety.

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