Machine Learning-Driven Optimisation of Aerodynamic Designs for High-Performance Vehicles

Abstract

Aerodynamic design optimization is critical in improving the performance of high-performance vehicles, primarily electric and autonomous vehicles. However, traditional methods such as Computational Fluid Dynamics (CFD) simulations face challenges such as long computational time and high cost. This article discusses the implementation of machine learning to overcome these limitations, highlighting algorithms such as neural networks, Gaussian process regression, and reinforcement learning. The results show that machine learning can reduce the design iteration time by up to 80%, from 24-48 hours/design in CFD methods to only 10-30 minutes/design. The accuracy of the predictive model is also very high, with an average error margin of less than 5%. Case studies on Formula 1 vehicles and electric vehicles show a reduction in drag coefficient of up to 10%, which directly improves the cruising efficiency of electric cars by up to 15% and increases downforce by 12% for high-speed vehicle stability. In addition, generative algorithms such as GANs enable the exploration of innovative designs, while reinforcement learning can generate adaptive designs responsive to changing operating conditions. With this capability, machine learning not only accelerates the design cycle and lowers development costs but also drives innovation in the development of electric, autonomous, and uncrewed aircraft vehicles. In conclusion, machine learning technology is a superior solution for optimizing aerodynamic design to meet the demands of efficiency, performance, and sustainability of future cars.