APA
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Elrefaie, M., Dai, A., & Ahmed, F. (2024). DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Graph-Based Drag Prediction. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. (Awarded " ASME Papers of Distinction"). https://doi.org/10.48550/arXiv.2403.08055
Chicago/Turabian
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Elrefaie, Mohamed, Angela Dai, and Faez Ahmed. “DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Graph-Based Drag Prediction.” In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Vol. (Awarded " ASME Papers of Distinction"), 2024.
MLA
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Elrefaie, Mohamed, et al. “DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Graph-Based Drag Prediction.” International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, vol. (Awarded " ASME Papers of Distinction"), 2024, doi:10.48550/arXiv.2403.08055.
BibTeX Click to copy
@inproceedings{mohamed2024a,
title = {DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Graph-Based Drag Prediction},
year = {2024},
journal = {International Design Engineering Technical Conferences and Computers and Information in Engineering Conference},
volume = {(Awarded " ASME Papers of Distinction")},
doi = {10.48550/arXiv.2403.08055},
author = {Elrefaie, Mohamed and Dai, Angela and Ahmed, Faez},
howpublished = {}
}
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 vehicles. 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
