README.md

---
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](https://www.openfoam.com/) and [Inductiva](https://inductiva.ai/) 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](https://github.com/TencentARC/InstantMesh) and the [Stanford Cars Dataset](https://www.kaggle.com/datasets/jessicali9530/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()

```python
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()

```python
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)
```