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توضیحات

A package to facilitate the manipulation of the AirfRANS dataset simulations.

ویژگی	مقدار
سیستم عامل	-
نام فایل	airfrans-0.1.3
نام	airfrans
نسخه کتابخانه	0.1.3
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	-
ایمیل نویسنده	Florent Bonnet <bonnet@isir.upmc.fr>
آدرس صفحه اصلی	-
آدرس اینترنتی	https://pypi.org/project/airfrans/
مجوز	-

# AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier–Stokes Solutions The AirfRANS dataset makes available numerical resolutions of the incompressible Reynolds-Averaged Navier–Stokes (RANS) equations over the NACA 4 and 5 digits series of airfoils and in a subsonic flight regime setup. Readthedocs documentation is available [here](https://airfrans.readthedocs.io/en/latest/index.html). ## Features - Access to 1000 simulations. - Reynolds number between 2 and 6 million. - Angle of attack between -5° and 15°. - Airfoil drawn in the NACA 4 and 5 digits series. - Four machine learning tasks representing different challenges. ## Installation Install with ``` pip install airfrans ``` ## Usage ### Downloading the dataset From python: ``` import airfrans as af af.dataset.download(root = PATH_TO_SAVING_DIRECTORY, unzip = True) ``` You can also directly download a ready-to-use version of the dataset in the [PyTorch Geometric library](https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html#torch_geometric.datasets.AirfRANS) Finally, you can directly download the dataset in the raw OpenFOAM version [here](https://data.isir.upmc.fr/extrality/NeurIPS_2022/OF_dataset.zip), or in the more friendly pre-processed version [here](https://data.isir.upmc.fr/extrality/NeurIPS_2022/Dataset.zip). ### Loading the dataset From python: ``` import airfrans as af dataset, dataname = af.dataset.load(root = PATH_TO_DATASET, task = TASK, train = True) ``` The tasks are the one presented in the [associated paper](https://arxiv.org/pdf/2212.07564.pdf). You can choose between `'full'`, `'scarce'`, `'reynolds`' and `'aoa'`. The dataset loaded in this case is the same as the one you can directly access via the [PyTorch Geometric library](https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html#torch_geometric.datasets.AirfRANS). If you want more flexibility about the sampling of each simulation for the inputs or targets, please feel free to build a custom loader with the help of the `'Simulation'` class presented in the following. We highly recommend to handle those data with a Gemetric Deep Learning library such as [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/) or [Deep Graph Library](https://www.dgl.ai/). ### Simulation class The `'Simulation'` class is an object to facilitate the manipulation of AirfRANS simulations. Given the root folder of where the directories of the simulations have been saved and the name of a simulation you can easily manipulate it. ``` import airfrans as af name = 'airFoil2D_SST_57.872_7.314_5.454_3.799_13.179' simulation = af.Simulation(root = PATH_TO_DATASET, name = name) ``` See the documentation for more details about this object. ## License This project is licensed under the [ODbL-1.0 License](https://opendatacommons.org/licenses/odbl/1-0/). ## Reference The original paper accepted at the 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks can be found [here](https://openreview.net/forum?id=Zp8YmiQ_bDC) and the preprint [here](https://arxiv.org/abs/2212.07564). Please cite this paper if you use this dataset in your own work. ``` @inproceedings{ bonnet2022airfrans, title={Airf{RANS}: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier{\textendash}Stokes Solutions}, author={Florent Bonnet and Jocelyn Ahmed Mazari and Paola Cinnella and Patrick Gallinari}, booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, year={2022}, url={https://openreview.net/forum?id=Zp8YmiQ_bDC} } ```

نیازمندی

مقدار	نام
-	numpy
-	pyvista>=0.37.0
-	tqdm

زبان مورد نیاز

مقدار	نام
>=3.7	Python

نحوه نصب

نصب پکیج whl airfrans-0.1.3:

pip install airfrans-0.1.3.whl

نصب پکیج tar.gz airfrans-0.1.3:

pip install airfrans-0.1.3.tar.gz