DrivAerML

Summary

Machine Learning (ML) has the potential to revolutionise the field of automotive aerodynamics, enabling split-second flow predictions early in the design process. However, the lack of open-source training data for realistic road cars, using high-fidelity CFD methods, represents a barrier to their development. To address this, a high-fidelity open-source (CC-BY-SA) public dataset for automotive aerodynamics has been generated, based on 500 parametrically morphed variants of the widely-used DrivAer notchback generic vehicle. Mesh generation and scale-resolving CFD was executed using consistent and validated automatic workflows representative of the industrial state-of-the-art. Geometries and rich aerodynamic data are published in open-source formats. To our knowledge, this is the first large, public-domain dataset for complex automotive configurations generated using high-fidelity CFD.

drivaer

CFD Solver:

All cases were run using the open-source finite-volume code OpenFOAM v2212 with custom modifications by UpstreamCFD. Please see the paper below for full details on the code and validation:

How to cite this dataset:

In order to cite the use of this dataset please cite the paper below which contains full details on the dataset.It can be found here

@article{ashton2024drivaer,
    title = {DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics},
    year = {2024},
    journal = {arxiv.org},
    url={https://arxiv.org/abs/2408.11969},
    author = {Ashton, N., Mockett, C., Fuchs, M., Fliessbach, L., Hetmann, H., Knacke, T., Schonwald, N.,
Skaperdas, V., Fotiadis, G., Walle, A., Hupertz, B., and Maddix, D}
}

How to download:

Please ensure you have enough local disk space before downloading (complete dataset is 30TB) and consider the examples below that provide ways to download just the files you need:

First Step: Install AWS Command Line Interface (CLI):

Follow instructions here: https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html

Second Step: Use the AWS CLI to download the dataset

Follow the following examples for how to download all or part of the dataset.

Note 1 : If you don’t have an AWS account you will need to add –no-sign-request within your AWS command i.e aws s3 cp –no-sign-request –recursive etc…

Note 2 : If you have an AWS account, please note the bucket is in us-east-1, so you will have the fastest download if you have your AWS service or EC2 instance running in us-east-1.

Example 1: Download all files (~30TB)
aws s3 cp --recursive s3://caemldatasets/drivaer/dataset .
Example 2: only download select files (e.g STL,images & force and moments):

Create the following bash script that could be adapted to loop through only select runs or to change to download different files e.g boundary/volume.

#!/bin/bash

# Set the S3 bucket and prefix
S3_BUCKET="caemldatasets"
S3_PREFIX="drivaer/dataset"

# Set the local directory to download the files
LOCAL_DIR="./drivaer_data"

# Create the local directory if it doesn't exist
mkdir -p "$LOCAL_DIR"

# Loop through the run folders from 1 to 500 (here you can change the number to only download a subset of the runs)
for i in $(seq 1 500); do
    RUN_DIR="run_$i"
    RUN_LOCAL_DIR="$LOCAL_DIR/$RUN_DIR"

    # Create the run directory if it doesn't exist
    mkdir -p "$RUN_LOCAL_DIR"

    # Download the drivaer_i.stl file
    aws s3 cp "s3://$S3_BUCKET/$S3_PREFIX/$RUN_DIR/drivaer_$i.stl" "$RUN_LOCAL_DIR/" --only-show-errors

    # Download the force_mom_i.csv file
    aws s3 cp "s3://$S3_BUCKET/$S3_PREFIX/$RUN_DIR/force_mom_$i.csv" "$RUN_LOCAL_DIR/" --only-show-errors

    aws s3 cp --recursive "s3://$S3_BUCKET/$S3_PREFIX/$RUN_DIR/images" "$RUN_LOCAL_DIR/images/" --only-show-errors
done

Files:

Each folder (e.g run1,run2…run”i” etc) corresponds to a different geometry that contains the following files where “i” is the run number:

  • drivaer_i.stl: geometry stl (~135mb)
  • geo_ref_i.csv: reference values for each geometry
  • geo_parameters_i.csv: reference geometry for each geometry
  • boundary_i.vtp: Boundary VTP (~500mb)
  • volume_i.vtu: Volume field VTU (~25GB)
  • force_mom_i.csv: forces/moments time-averaged (using varying frontal area/wheelbase)
  • force_mom_constref_i.csv: forces/moments time-averaged (using constant frontal area/wheelbase)
  • slices: folder containing .vtp slices in x,y,z that contain flow-field variables
  • Images: This folder contains images of various flow variables (e.g. Cp, CpT, UMagNorm) for slices of the domain at X, Y, and Z locations (M signifies minus, P signifies positive), as well as on the surface. It also includes evaluation plots of the time-averaging of the force coefficients (via the tool MeanCalc) and a residual plot illustrating the convergence.

Acknowledgements

  • CFD solver and workflow development by Charles Mockett, Marian Fuchs, Louis Fliessbach, Henrik Hetmann, Thilo Knacke & Norbert Schonwald (UpstreamCFD)
  • Geometry parameterization by Vangelis Skaperdas, Grigoris Fotiadis (BETA-CAE Systems) & Astrid Walle (Siemens Energy)
  • Meshing development workflow by Vangelis Skaperdas & Grigoris Fotiadis (BETA-CAE Systems)
  • DrivAer advise and consultation by Burkhard Hupertz (Ford)
  • Guidance on dataset preparation for ML by Danielle Maddix (Amazon Web Services)
  • Simulation runs, HPC setup and dataset preparation by Neil Ashton (Amazon Web Services)

License

This dataset is provided under the CC BY SA 4.0 license, please see LICENSE.txt for full license text.