Datasets:
Languages:
English
Size:
10K - 100K
| 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 | |
| <p align="center"> | |
| <img src="https://huggingface.co/datasets/inductiva/windtunnel/resolve/main/assets/pressure_field_mesh.png", width="500px"> | |
| </p> | |
| # Wind Tunnel 20K Dataset | |
| The Wind Tunnel Dataset contains 19,812 OpenFOAM 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). | |
| The meshes for these automobile-like objects were generated using the Instant Mesh model on images sourced from the Stanford Cars Dataset. | |
| To ensure stable and reliable results, each simulation runs for 300 iterations. | |
| 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. | |
| # Why | |
| Existing object datasets have many limitations: they are either small in size, closed source, or have low quality meshes. | |
| Hence, we decided to generate a new dataset using the [InstantMesh](https://github.com/TencentARC/InstantMesh) model, | |
| which is open-source (Apache-2.0) and is currently state-of-the-art in image-to-mesh generation. | |
| The automobile-like meshes were generated by running the image-to-mesh model [InstantMesh](https://github.com/TencentARC/InstantMesh) | |
| on 1k images from the publicly available (Apache-2.0) | |
| [Stanford Cars Dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-cars-dataset) consisting of 16,185 images of automobiles. | |
| Naturally, running the image-to-mesh model leads to meshes that may have certain defects, such as irregular surfaces, asymmetry issues | |
| and disconnected components. Therefore, after running the image-to-mesh model, we run a custom post-processing step where we try to | |
| improve the meshes quality. We used PCA to align the mesh with the main axis and we removed disconnected components. | |
| The resulting set of meshes still have little defects, such as presence of "spikes" or "cavities" in supposedly flat areas and | |
| asymmetric shapes, among others. We consider these little defects as valuable features of the dataset not as issues, since from the | |
| point of view of the learning problem, they bring challenges to the model that we believe will contribute to obtaining more robust | |
| and generalizable models. | |
| # How did we generate the dataset | |
| 1. Generating Input Meshes, using InstantMesh on Standord Cars Dataset and postprocess the input meshes. | |
| 2. Running OpenFoam on the Input Meshes with the Inductiva API. | |
| We ran OpenFoam simulations with different wind speeds and object angles on the input meshes. | |
| This produces an output mesh that contains all the information named `openfoam_mesh.obj`; | |
| 4. We postprocessed the OpenFoam output to generate streamlines and pressure_map meshes. | |
| The code used to generate the meshes and postprocess them is available on github: [https://github.com/inductiva/datasets-generation](https://github.com/inductiva/datasets-generation). | |
| # 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 the input and the output data of the simulations. | |
| Here’s a breakdown of the files included in each simulation: | |
| - **[input_mesh.obj](#input_meshobj)**: OBJ file with the input mesh. These were generated using the InstantMesh model by the process described in section link. | |
| - **[openfoam_mesh.obj](#openfoam_meshobj)**: OBJ file with the OpenFOAM mesh. (explicar) | |
| - **[pressure_field_mesh.vtk](#pressure_field_meshvtk)**: VTK file with the pressure field data. (explicar) | |
| - **[streamlines_mesh.ply](#streamlines_meshply)**: PLY file with the streamlines. (posprocessing) | |
| - **[metadata.json](#metadatajson)**: 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. | |
| ### input_mesh.obj | |
| | **Input Mesh** | **OpenFOAM Mesh** | | |
| |-------------------------------|------------------------------| | |
| |  |  | | |
| ### openfoam_mesh.obj | |
| | **Input Mesh** | **OpenFOAM Mesh** | | |
| |-------------------------------|------------------------------| | |
| |  |  | | |
| ### pressure_field_mesh.obj | |
| | **Input Mesh** | **OpenFOAM Mesh** | | |
| |-------------------------------|------------------------------| | |
| |  |  | | |
| ### streamlines_mesh.ply | |
| | **Input Mesh** | **OpenFOAM Mesh** | | |
| |-------------------------------|------------------------------| | |
| |  |  | | |
| ### metadata.obj | |
| ```json | |
| { | |
| "id": "1w63au1gpxgyn9kun5q9r7eqa", | |
| "object_file": "object_24.obj", | |
| "wind_speed": 35, | |
| "rotate_angle": 332, | |
| "num_iterations": 300, | |
| "resolution": 5, | |
| "drag_coefficient": 0.8322182, | |
| "moment_coefficient": 0.3425206, | |
| "lift_coefficient": 0.1824983, | |
| "front_lift_coefficient": 0.4337698, | |
| "rear_lift_coefficient": -0.2512715, | |
| "input_mesh_path": "data/train/1w63au1gpxgyn9kun5q9r7eqa/input_mesh.obj", | |
| "openfoam_mesh_path": "data/train/1w63au1gpxgyn9kun5q9r7eqa/openfoam_mesh.obj", | |
| "pressure_field_mesh_path": "data/train/1w63au1gpxgyn9kun5q9r7eqa/pressure_field_mesh.vtk", | |
| "streamlines_mesh_path": "data/train/1w63au1gpxgyn9kun5q9r7eqa/streamlines_mesh.ply" | |
| } | |
| ``` | |
| ### Dataset Statistics | |
| The dataset comprises 19,812 valid samples out of a total of 20,000 simulations, with [188 submissions failing](https://huggingface.co/datasets/inductiva/windtunnel/resolve/main/failed_tasks.txt) due to numerical errors in OpenFOAM. | |
| The complete dataset requires X GB of storage. Below, the histograms illustrate the distribution of points: | |
| | **Input Mesh** | **OpenFOAM Mesh** | | |
| |-------------------------------|------------------------------| | |
| |  |  | | |
| ## 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 | |
| import huggingface_hub | |
| dataset_name = "inductiva/windtunnel-20k" | |
| # Download the entire dataset | |
| huggingface_hub.snapshot_download(repo_id=dataset_name, repo_type="dataset") | |
| # Download to a specific local directory | |
| huggingface_hub.snapshot_download( | |
| repo_id=dataset_name, repo_type="dataset", local_dir="local_folder" | |
| ) | |
| # Download only the simulation metadata across all simulations | |
| huggingface_hub.snapshot_download( | |
| repo_id=dataset_name, | |
| repo_type="dataset", | |
| local_dir="local_folder", | |
| allow_patterns=["*/*/*/simulation_metadata.json"] | |
| ) | |
| ``` | |
| ### 2. Using load_dataset() | |
| ```python | |
| import datasets | |
| # Load the dataset (streaming is supported) | |
| dataset = datasets.load_dataset("inductiva/windtunnel-20k", streaming=False) | |
| # Display dataset information | |
| print(dataset) | |
| # Access a sample from the training set | |
| sample = dataset["train"][0] | |
| print("Sample from training set:", sample) | |
| ``` | |
| ## OpenFoam Parameters | |
| You can find the OpenFoam configuration files on github: [https://github.com/inductiva/wind-tunnel/tree/main/windtunnel/templates](https://github.com/inductiva/wind-tunnel/tree/main/windtunnel/templates) | |
| ## 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). | |
| If you detect any clearly problematic mesh, please let us know so we can correct that issue for the next version of the | |
| Windtunnel-20k dataset. | |
| 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). |