DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Prediction
This addresses the problem of slow aerodynamic assessments in automotive design, enabling faster and more efficient car development, though it is incremental in advancing data-driven methods in this domain.
This study tackles the lack of large-scale datasets for aerodynamic car design by introducing DrivAerNet, a dataset 60% larger than previous ones with detailed 3D car meshes and aerodynamic data, and RegDGCNN, a model that provides high-precision drag estimates from 3D meshes in seconds, bypassing traditional limitations.
This study introduces DrivAerNet, a large-scale high-fidelity CFD dataset of 3D industry-standard car shapes, and RegDGCNN, a dynamic graph convolutional neural network model, both aimed at aerodynamic car design through machine learning. DrivAerNet, with its 4000 detailed 3D car meshes using 0.5 million surface mesh faces and comprehensive aerodynamic performance data comprising of full 3D pressure, velocity fields, and wall-shear stresses, addresses the critical need for extensive datasets to train deep learning models in engineering applications. It is 60\% larger than the previously available largest public dataset of cars, and is the only open-source dataset that also models wheels and underbody. RegDGCNN leverages this large-scale dataset to provide high-precision drag estimates directly from 3D meshes, bypassing traditional limitations such as the need for 2D image rendering or Signed Distance Fields (SDF). By enabling fast drag estimation in seconds, RegDGCNN facilitates rapid aerodynamic assessments, offering a substantial leap towards integrating data-driven methods in automotive design. Together, DrivAerNet and RegDGCNN promise to accelerate the car design process and contribute to the development of more efficient cars. To lay the groundwork for future innovations in the field, the dataset and code used in our study are publicly accessible at https://github.com/Mohamedelrefaie/DrivAerNet.