metadata

pretty_name: Wind Tunnel dataset
size_categories:
  - 10K<n<100K

Wind Tunnel Dataset

The Wind Tunnel Dataset contains 20,000 wind tunnel simulations, organized into three subsets: 70% training, 20% validation, and 10% test. The simulations were generated using OpenFOAM and Inductiva and are based on 1,000 unique objects, each with 20 variations. The simulations cover 4 wind speeds and 5 different rotation angles, with each simulation running for 300 iterations. The input object meshes were generated using the Instant Meshes model and the Stanford Cars Dataset.

Dataset Structure

data
├── train
│   ├── <SIMULATION_ID>
│   │   ├── input_mesh.obj
│   │   ├── openfoam_mesh.obj
│   │   ├── pressure_field_mesh.vtk
│   │   ├── simulation_metadata.json
│   │   └── streamlines_mesh.ply
│   └── ...
├── validation
│   └── ...
└── test
    └── ...

Dataset Files

input_mesh.obj: OBJ file with the input mesh.
openfoam_mesh.obj: OBJ file with the OpenFOAM mesh.
pressure_field_mesh.vtk: VTK file with the pressure field data.
streamlines_mesh.ply: PLY file with the streamlines.
metadata.json: JSON with metadata such as input parameters and some output results.

Downloading the Dataset:

1. Using snapshot_download()

from huggingface_hub import snapshot_download

dataset_name = "inductiva/windtunnel"

# Download the entire dataset
snapshot_download(repo_id=dataset_name)

# Download to a specific local directory
snapshot_download(repo_id=dataset_name, local_dir="local_folder")

# Download only the input mesh files across all simulations
snapshot_download(allow_patterns=["*/*/*/input_mesh.obj"], repo_id=dataset_name)

2. Using load_dataset()

from datasets import load_dataset

# Load the dataset (streaming is supported)
dataset = load_dataset("inductiva/windtunnel", streaming=False)

# Display dataset information
print(dataset)

# Access a sample from the training set
sample = dataset["train"][0]
print("Sample from training set:", sample)