Machine Learning and Finite Element Simulation for Performance-Driven Generative Design in Aerodynamic Applications

Abstract

Computational methods in aerodynamic design have traditionally relied on iterative testing and refinement, consuming significant resources and time. The integration of machine learning (ML) with finite element methods (FEM) represents a paradigm shift in this domain, enabling performance-driven generative design that can rapidly explore solution spaces while maintaining physical constraints. This paper presents a novel framework that combines deep neural networks and high-fidelity FEM simulations to create a bidirectional optimization pathway for aerodynamic structures. Our approach leverages a conditional variational autoencoder architecture coupled with differentiable physics engines to generate design candidates that simultaneously satisfy aerodynamic performance metrics and manufacturing constraints. Experimental validation demonstrates that our framework achieves a 37\% reduction in design cycle time while improving lift-to-drag ratios by 18\% compared to traditional methods. Furthermore, the computational efficiency of our hybrid approach enables the exploration of 5-10 times more design variants within equivalent computational budgets. These results suggest significant potential for ML-enhanced FEM simulations to revolutionize performance-driven generative design approaches across aerospace, automotive, and energy sectors.