Revolutionizing Car Design with AI-Powered Aerodynamics

Car design is an intricate and proprietary process. Carmakers can spend years on the design phase for a car, tweaking 3D forms in simulations before building out the most promising designs for physical testing. The details and specs of these tests, including the aerodynamics of a given car design, are typically not made public. Significant advances in performance, such as in fuel efficiency or electric vehicle range, can therefore be slow and siloed from company to company.

The Challenge of Data Availability

MIT engineers say that the search for better car designs can speed up exponentially with the use of generative artificial intelligence tools that can plow through huge amounts of data in seconds and find connections to generate a novel design. While such AI tools exist, the data they would need to learn from have not been available, at least in any sort of accessible, centralized form.

Introducing DrivAerNet++

But now, the engineers have made just such a dataset available to the public for the first time. Dubbed DrivAerNet++, the dataset encompasses more than 8,000 car designs, which the engineers generated based on the most common types of cars in the world today. Each design is represented in 3D form and includes information on the car’s aerodynamics — the way air would flow around a given design, based on simulations of fluid dynamics that the group carried out for each design.

In a new dataset that includes more than 8,000 car designs, MIT engineers simulate the aerodynamics for a given car shape, which they represent in various modalities, including “surface fields” (left) and “streamlines” (right).

Using the Dataset

Each of the dataset’s 8,000 designs is available in several representations, such as mesh, point cloud, or a simple list of the design’s parameters and dimensions. As such, the dataset can be used by different AI models that are tuned to process data in a particular modality.

The Potential Impact

DrivAerNet++ is the largest open-source dataset for car aerodynamics that has been developed to date. The engineers envision it being used as an extensive library of realistic car designs, with detailed aerodynamics data that can be used to quickly train any AI model. These models can then just as quickly generate novel designs that could potentially lead to more fuel-efficient cars and electric vehicles with longer range, in a fraction of the time that it takes the automotive industry today.

Conclusion

The potential for DrivAerNet++ to revolutionize car design is vast. With this dataset, researchers and engineers can train AI models to generate new car designs that are more efficient, sustainable, and environmentally friendly. The future of car design has never been more exciting, and we are just beginning to scratch the surface of what is possible.

FAQs

Q: What is DrivAerNet++?
A: DrivAerNet++ is a dataset of over 8,000 car designs, each with detailed information on their aerodynamics, generated by MIT engineers.

Q: What does the dataset contain?
A: The dataset contains 3D representations of each car design, along with information on their aerodynamics, and can be used to train AI models to generate new car designs.

Q: How can DrivAerNet++ be used?
A: The dataset can be used to train AI models to generate new car designs that are more efficient, sustainable, and environmentally friendly.

Q: How was the dataset created?
A: The dataset was created by applying a morphing operation to each of the baseline 3D car models, making slight changes to each of 26 parameters, and then translating each design into different modalities, such as mesh, point cloud, or a list of dimensions and specs.