Advanced Computational Methods for Aerodynamic Optimization in Next-Generation Automotive Engineering: Integrating CFD, AI-Based Surrogate Modeling, and Evolutionary Algorithms for Enhanced Vehicle Performance

Abstract

The demand for high-performance, fuel-efficient, and aerodynamically optimized vehicles has driven the integration of advanced computational methods in automotive engineering. Traditional aerodynamic optimization relied on wind tunnel experiments, but modern techniques leverage computational fluid dynamics (CFD), AI-driven surrogate models, and evolutionary algorithms for enhanced precision and efficiency. This paper explores these computational methods, highlighting their role in optimizing automotive aerodynamics while balancing performance, fuel efficiency, and design constraints. A comparative analysis of gradient-based CFD optimization, machine learning-driven surrogate modeling, and evolutionary algorithms is presented. The study also discusses the hybridization of these techniques, demonstrating their impact on reducing drag, improving stability, and enhancing energy efficiency in next-generation vehicles.