README.md

---
pretty_name: Wind Tunnel 20K Dataset
size_categories:
- 10K<n<100K
task_categories:
- feature-extraction
- graph-ml
- image-to-3d
language:
- en
tags:
- simulation
- openfoam
- physics
- windtunnel
- inductiva
- machine learning
- synthetic
---

# Wind Tunnel 20K Dataset
The Wind Tunnel Dataset contains 20,000 [OpenFOAM](https://www.openfoam.com/) simulations of 1,000 unique automobile-like objects placed in a virtual wind tunnel. 
Each object is simulated under 20 distinct conditions: 4 random wind speeds ranging from 10 to 50 m/s, and 5 rotation angles (0°, 180° and 3 random angles).
To ensure stable and reliable results, each simulation runs for 300 iterations.
The meshes for these automobile-like objects were generated using the [Instant Mesh model](https://github.com/TencentARC/InstantMesh) and sourced from the [Stanford Cars Dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-cars-dataset).
The entire dataset of 20,000 simulations is organized into three subsets: 70% for training, 20% for validation, and 10% for testing.

The data generation process itself was orchestrated using the [Inductiva API](https://inductiva.ai/), which allowed us to run hundreds of OpenFOAM simulations in parallel on the cloud. 

<p align="center">
  <img src="https://huggingface.co/datasets/inductiva/windtunnel/resolve/main/example.png", width="500px">
</p>

### 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
Each simulation in the Wind Tunnel Dataset is accompanied by several key files that provide both input and output data. 
Here’s a breakdown of the files included in each simulation:

- **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 about the input parameters and about some output results such as the force coefficients (obtained via simulation) and the path of the output files.


## Downloading the Dataset:

To download the dataset you have to install the [Datasets package](https://huggingface.co/docs/datasets/en/index) by HuggingFace:

```python
pip install datasets
```

### 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, repo_type="dataset")

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

# Download only the simulation metadata across all simulations
snapshot_download(
    repo_id=dataset_name,
    repo_type="dataset",
    local_dir="local_folder",
    allow_patterns=["*/*/*/simulation_metadata.json"]
)
```

### 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)
```

## What's next?
If you have any issues using this dataset, feel free to reach out to us at [support@intuctiva.ai](support@intuctiva.ai)

To learn more about how we created this dataset—or how you can generate synthetic datasets for Physics-AI models—visit [Inductiva.AI](inductiva.ai) or check out our blog post on [transforming complex simulation workflows into easy-to-use Python classes](https://inductiva.ai/blog/article/transform-complex-simulations).