AI Revolutionizes Car Design: How Artificial Intelligence Slashes Aerodynamic Testing Time

For all the sketches, concepts, and slick imagery produced by automotive designers, the production cars that reach roads worldwide are shaped most significantly by aerodynamics. How smoothly a vehicle cuts through the air directly impacts fuel efficiency, and in the era of electric vehicles, it can offset battery weight to extend overall range.

Yet the aerodynamic analyses relied upon by car designers are notoriously slow. “We’ll release a design surface, and then it can take days or weeks to get a full set of analysis back on the performance of that surface,” says Bryan Styles, director of design innovation and technology operations at General Motors. “By that time, the design surface has changed, and then we’re trying to understand, well, how do these results actually translate into the surface that we now have in design?”

Those delays could soon be a thing of the past. Major car companies are increasingly turning to artificial intelligence to accelerate aerodynamic work to a scale unimaginable in the early days of wind tunnels and even today’s computational fluid dynamics modeling. GM and Jaguar Land Rover are among the companies adopting new AI tools to address one of the biggest bottlenecks in car design.

GM’s “Virtual Wind Tunnel” Uses AI to Speed Up Design

GM has developed what it calls a “virtual wind tunnel,” an AI model trained on previous computer-based aerodynamic simulations. By applying past analyses to new designs, GM’s designers and engineers can quickly assess how a contour would perform in a physical wind tunnel test. This data is then fed directly into digital sculpting tools used to shape cars.

“We are using it on our next products,” says Rene Strauss, GM’s director of virtual integration engineering. “So this isn’t a vision of the future. This is happening right now.”

Jaguar Land Rover Leverages AI for High-Volume Aerodynamic Testing

Like GM, Jaguar Land Rover is deploying AI tools to run robust aerodynamic performance tests on its car designs, often at a scale of hundreds or even thousands per day. While aerodynamics is a well-established science, each automaker is developing its own AI model using existing vehicles to enable more accurate predictions of drag or air pressure on diverse models—from boxy Land Rover SUVs to sleek Chevrolet Corvettes.

“The better the training data, the better the model performance,” says Scott Parrish, a technical fellow and lab group manager in research and development for GM. “We use a variety of vehicles and we actually alter their shape so we can gather more and more surfaces for robust prediction. If a designer brings in a vehicle and moves a surface up or down or in or out, the training data comprehends that.”

Jaguar Land Rover is collaborating with Neural Concept, an external startup spun out of an