--- pretty_name: Wind Tunnel dataset size_categories: - 10K<n<100K --- # Wind Tunnel 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: ### 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) ``` ## 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).