Persistent Identifier
|
doi:10.7910/DVN/K7PWNJ |
Publication Date
|
2024-11-19 |
Title
| DrivAerNet++: Pressure |
Author
| Elrefaie, MohamedMITORCIDhttps://orcid.org/0009-0008-9981-0930
Ahmed, FaezMIT
Dai, AngelaTUM
Morar, FlorinBETA CAE SYSTEMS USA |
Point of Contact
|
Use email button above to contact.
Elrefaie, Mohamed (MIT) |
Description
| The VTK files contain high-fidelity pressure field data on the surfaces of various industry-standard car designs, capturing key aerodynamic features. Each file provides the pressure distribution across different car body types (fastback, notchback, estateback), underbody designs (smooth and detailed), and wheel configurations.
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 https://doi.org/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
|
Depositor
| Barbosa, Sonia |
Deposit Date
| 2024-10-07 |