Aerodynamics-Informed Design Optimization of Vehicles with Machine Learning (Case No. 2025-083)

Intro Sentence:

UCLA researchers from the Department of Mechanical and Aerospace Engineering have developed a machine-learning–guided aerodynamic optimization framework that accelerates the design of electric vehicles by coupling high-fidelity simulations with data-driven shape prediction.

Background:

Electric vehicle (EV) technologies have revolutionized transportation, driving the need for efficient aerodynamic design. Traditional vehicle optimization relies on computational fluid dynamics (CFD) simulations and wind tunnel testing, which are limited by their high computational costs, prolonged design cycles, and restricted ability to explore large geometric parameter spaces. Small modifications in vehicle design can lead to nonlinear changes in aerodynamic drag, making manual optimization particularly challenging. Given that aerodynamic drag accounts for up to 80% of driving resistance at highway speeds, reducing drag is essential to extending range and improving energy efficiency. Existing methods for aerodynamic analysis lack scalability and adaptability to the various vehicle designs. The demand for a rapid, data-driven, and physically interpretable approach to aerodynamic optimization has grown across the automotive industry, and there remains an unmet need for a computational framework that can accurately predict and optimize aerodynamic performance across a wide range of vehicle geometries.

Innovation:

UCLA researchers from the Department of Mechanical and Aerospace Engineering have developed a machine-learning–guided aerodynamic optimization framework that enables rapid, data-driven vehicle design with high efficiency and precision. This innovation integrates high-fidelity Large Eddy Simulation (LES) data with a nonlinear, PCA-assisted autoencoder to learn low-dimensional representations of vehicle geometries and their corresponding aerodynamic performance. The model allows designers to modify and optimize geometries directly in this learned space to achieve lower drag coefficients by capturing the nonlinear relationships between shape and drag within a reduced “latent space”. The framework successfully generated vehicle designs exhibiting up to an 11% reduction in aerodynamic drag, which were further validated through independent LES analyses. This innovation offers a computationally efficient, physically interpretable, and generalizable surrogate model that can accelerate the iterative design process for next-generation electric vehicles while maintaining industrial-grade accuracy and fidelity.

Potential Applications: