DrivAerNet++: 3D Meshes

Version 1.0
DrivAerNet++: 3D Meshes
Elrefaie, Mohamed; Ahmed, Faez; Dai, Angela; Morar, Florin, 2024, "DrivAerNet++: 3D Meshes", https://doi.org/10.7910/DVN/OYU2FG, Harvard Dataverse, V1
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Description
The folder contains industry-standard 3D meshes for all car designs in the DrivAerNet++ dataset. Each STL file represents a unique car geometry, including variations across different body types such as fastback, notchback, and estateback. Additionally, this folder includes models with diverse underbody configurations (smooth for electric cars and detailed for ICE/hybrid cars) and different wheel setups, providing a comprehensive collection of car geometries for aerodynamic research and design optimization.

Strict Licensing Notice: DrivAerNet/DrivAerNet++ is released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) and is exclusively for non-commercial research and educational purposes. Any commercial use—including, but not limited to, training machine learning models, developing generative AI tools, creating software products, running new simulations using the provided geometries or any derived geometries, or other commercial R&D applications—is strictly prohibited. Unauthorized commercial use of DrivAerNet/DrivAerNet++, or any derived data, will result in enforcement by the MIT Technology Licensing Office (MIT TLO) and may carry legal consequences.
Subject Engineering; Computer and Information Science; Physics; Other
Keyword aerodynamic design, computational fluid dynamics, CFD dataset, automotive aerodynamics, machine learning, geometric deep learning, generative design, surrogate modeling, high-fidelity simulations, car design optimization, aerodynamic drag, wall shear stress, pressure field, volumetric flow fields, data-driven design, neural networks, parametric modeling, design space exploration, engineering simulations, vehicle aerodynamics
Related Publication Elrefaie, M., Morar, F., Dai, A., & Ahmed, F. (2024). DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks. arXiv preprint arXiv:2406.09624.doi: 10.48550/arXiv.2406.09624
Notes The data for this project are stored on the Northeast Storage Exchange (NESE). Follow the instructions for large data download found on our website: Downloading data from NESE via Globus: Quick Start

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