--- 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) ```