Data-Driven Aerodynamic Design and Optimization of Vehicles

Abstract

This paper investigates the aerodynamic design and optimization of vehicles based on data-driven methods, aiming to enhance the energy efficiency and performance of transportation vehicles. The research integrates machine learning algorithms, big data analytics, and Computational Fluid Dynamics (CFD) simulation techniques to establish an efficient aerodynamic optimization model. By analyzing massive design parameters and flow field data, innovative design solutions are proposed and validated for practical application. This study offers new approaches to traditional design methods, significantly shortening the design cycle, reducing research and development costs, and laying a technological foundation for the future development of intelligent transportation vehicles. The findings are of great significance for promoting green transportation and achieving energy conservation and emission reduction, providing both theoretical support and practical guidance for sustainable transportation development.