metadata

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 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, 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 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 on 1k images from the publicly available (Apache-2.0) 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?

Generate Input Meshes: First, input meshes are generated using the InstantMesh model with images from the Stanford Cars Dataset. Post-processing is then applied to these input meshes.
Run OpenFOAM Simulations: The Inductiva API is utilized to run OpenFOAM simulations on the input meshes at various wind speeds and object angles. This process produces an output mesh named openfoam_mesh.obj, which contains all relevant simulation information.
Post-process OpenFOAM Output: The OpenFOAM output is post-processed 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.

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: 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.

input_mesh.obj

Input mesh generated with InstantMesh model from images of the Stanford Cars Dataset. This mesh was used as the input of the OpenFoam simulation.

The mesh generation process is described here.

Input Mesh	Points Histogram

openfoam_mesh.obj

Output mesh obtained from the OpenFoam simulation. The number of points is smaller than input_mesh due to internal OpenFoam processing.

Open Foam Mesh	Points Histogram

pressure_field_mesh.obj

We extracted pressure values from the openfoam_mesh.obj. Then we interpolated the pressure values with closest_point strategy on the input_mesh.obj so that we have a higher resolution mesh. As can be seen on the histogram, the distribution of points is the same as the input_mesh.obj.

More information here.

Pressure Field Mesh	Points Histogram
	)

streamlines_mesh.ply

We generated streamlines from the openfoam_mesh.obj.

More information here.

Streamlines Mesh

metadata.obj

This file contains metadata information about the simulation. It consists of input parameters like wind_speed, rotate_angle, num_iterations and ´resolution. It also has output parameters like drag_coefficient, moment_coefficient, lift_coefficient, front_lift_coefficient, rear_lift_coefficient` and the location of the output meshes:

  {
    "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 due to numerical errors in OpenFOAM. The complete dataset requires X GB of storage.

Downloading the Dataset:

To download the dataset you have to install the Datasets package by HuggingFace:

pip install datasets

1. Using snapshot_download()

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()

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

We used Inductiva Template Manager to parameterize the OpenFoam configuration files.

Need a better way to do this:

flowVelocity         ({{ wind_speed }} 0 0);

vertices
(
    ({{ x_min }} {{ y_min }} {{ z_min }})
    ({{ x_max }} {{ y_min }} {{ z_min }})
    ({{ x_max }} {{ y_max }} {{ z_min }})
    ({{ x_min }} {{ y_max }} {{ z_min }})
    ({{ x_min }} {{ y_min }} {{ z_max }})
    ({{ x_max }} {{ y_min }} {{ z_max }})
    ({{ x_max }} {{ y_max }} {{ z_max }})
    ({{ x_min }} {{ y_max }} {{ z_max }})
);

endTime         {{ num_iterations }};

magUInf         {{ wind_speed }};
lRef            {{ length }};        // Wheelbase length
Aref            {{ area }};        // Estimated

geometry
{
    object
    {
        type triSurfaceMesh;
        file "object.obj";
    }

    refinementBox
    {
        type searchableBox;
        min ({{ x_min }} {{ y_min }} {{ z_min }});
        max ({{ x_max }} {{ y_max }} {{ z_max }});
    }
};

features
(
    {
        file "object.eMesh";
        level {{ resolution + 1  }};
    }
);


refinementSurfaces
{
    object
    {
        // Surface-wise min and max refinement level
        level ({{ resolution }} {{ resolution + 1 }});

        // Optional specification of patch type (default is wall). No
        // constraint types (cyclic, symmetry) etc. are allowed.
        patchInfo
        {
            type wall;
            inGroups (objectGroup);
        }
    }
}

refinementRegions
{
    refinementBox
    {
        mode inside;
        levels ((1E15 {{ resolution - 1 }}));
    }
}

locationInMesh ({{ x_min }} {{ y_min }} {{ z_min }});

You can find the OpenFoam configuration files on github: 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.

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 or check out our blog post on transforming complex simulation workflows into easy-to-use Python classes.