XAeroNet: Scalable Neural Models for External Aerodynamics

XAeroNet is a collection of scalable models for large-scale external aerodynamic evaluations. It consists of two models, XAeroNet-S and XAeroNet-V for surface and volume predictions, respectively.

External aerodynamics plays a crucial role in the design and optimization of vehicles, aircraft, and other transportation systems. Accurate predictions of aerodynamic properties such as drag, pressure distribution, and airflow characteristics are essential for improving fuel efficiency, vehicle stability, and performance. Traditional approaches, such as computational fluid dynamics (CFD) simulations, are computationally expensive and time-consuming, especially when evaluating multiple design iterations or large datasets.

XAeroNet addresses these challenges by leveraging neural network-based surrogate models to provide fast, scalable, and accurate predictions for both surface-level and volume-level aerodynamic properties. By using the DrivAerML dataset, which contains high-fidelity CFD data for a variety of vehicle geometries, XAeroNet aims to significantly reduce the computational cost while maintaining high prediction accuracy. The two models in XAeroNet—XAeroNet-S for surface predictions and XAeroNet-V for volume predictions—enable rapid aerodynamic evaluations across different design configurations, making it easier to incorporate aerodynamic considerations early in the design process.

XAeroNet-S

XAeroNet-S is a scalable MeshGraphNet model that partitions large input graphs into smaller subgraphs to reduce training memory overhead. Halo regions are added to these subgraphs to prevent message-passing truncations at the boundaries. Gradient aggregation is employed to accumulate gradients from each partition before updating the model parameters. This approach ensures that training on partitions is equivalent to training on the entire graph in terms of model updates and accuracy. Additionally, XAeroNet-S does not rely on simulation meshes for training and inference, overcoming a significant limitation of GNN models in simulation tasks.

The input to the training pipeline is STL files, from which the model samples a point cloud on the surface. It then constructs a connectivity graph by linking the N nearest neighbors. This method also supports multi-mesh setups, where point clouds with different resolutions are generated, their connectivity graphs are created, and all are superimposed. The Metis library is used to partition the graph for efficient training.

For the XAeroNet-S model, STL files are used to generate point clouds and establish graph connectivity. Additionally, the .vtp files are used to interpolate the solution fields onto the point clouds.

XAeroNet-V

XAeroNet-V is a scalable 3D UNet model with attention gates, designed to partition large voxel grids into smaller sub-grids to reduce memory overhead during training. Halo regions are added to these partitions to avoid convolution truncations at the boundaries. Gradient aggregation is used to accumulate gradients from each partition before updating the model parameters, ensuring that training on partitions is equivalent to training on the entire voxel grid in terms of model updates and accuracy. Additionally, XAeroNet-V incorporates a continuity constraint as an additional loss term during training to enhance model interpretability.

For the XAeroNet-V model, the .vtu files are used to interpolate the volumetric solution fields onto a voxel grid, while the .stl files are utilized to compute the signed distance field (SDF) and its derivatives on the voxel grid.

We trained our models using the DrivAerML dataset from the CAE ML Dataset collection. This high-fidelity, open-source (CC-BY-SA) public dataset is specifically designed for automotive aerodynamics research. It comprises 500 parametrically morphed variants of the widely utilized DrivAer notchback generic vehicle. Mesh generation and scale-resolving computational fluid dynamics (CFD) simulations were executed using consistent and validated automatic workflows that represent the industrial state-of-the-art. Geometries and comprehensive aerodynamic data are published in open-source formats. For more technical details about this dataset, please refer to their paper.

To train the XAeroNet-S model, follow these steps:

1. Download the DrivAer ML dataset using the provided download_aws_dataset.sh script.

2. Navigate to the surface folder.

3. Specify the configurations in conf/config.yaml. Make sure path to the dataset is specified correctly.

4. Run combine_stl_solids.py. The STL files in the DriveML dataset consist of multiple solids. Those should be combined into a single solid to properly generate a surface point cloud using the PhysicsNeMo Tesselated geometry module.

5. Run preprocessing.py. This will prepare and save the partitioned graphs.

6. Create a partitions_validation folder, and move the samples you wish to use for validation to that folder.

7. Run compute_stats.py to compute the global mean and standard deviation from the training samples.

8. Run train.py to start the training.

9. Download the validation results (saved in form of point clouds in .vtp format), and visualize in Paraview.

Fig. 17 XAeroNet-S Validation results for the sample #500.

To train the XAeroNet-V model, follow these steps:

1. Download the DrivAer ML dataset using the provided download_aws_dataset.sh script.

2. Navigate to the volume folder.

3. Specify the configurations in conf/config.yaml. Make sure path to the dataset is specified correctly.

4. Run preprocessing.py. This will prepare and save the voxel grids.

5. Create a drivaer_aws_h5_validation folder, and move the samples you wish to use for validation to that folder.

6. Run compute_stats.py to compute the global mean and standard deviation from the training samples.

7. Run train.py to start the training. Partitioning is performed prior to training.

8. Download the validation results (saved in form of voxel grids in .vti format), and visualize in Paraview.

Fig. 18 XAeroNet-V Validation results.

We mainly use TensorBoard for logging training and validation losses, as well as the learning rate during training. You can also optionally use Weight & Biases to log training metrics. To visualize TensorBoard running in a Docker container on a remote server from your local desktop, follow these steps:

1. Expose the Port in Docker: Expose port 6006 in the Docker container by including -p 6006:6006 in your docker run command.

2. Launch TensorBoard: Start TensorBoard within the Docker container:

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   tensorboard --logdir=/path/to/logdir --port=6006
           

3. Set Up SSH Tunneling: Create an SSH tunnel to forward port 6006 from the remote server to your local machine:

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   ssh -L 6006:localhost:6006 <user>@<remote-server-ip>
           

   Replace <user> with your SSH username and <remote-server-ip> with the IP address of your remote server. You can use a different port if necessary.

4. Access TensorBoard: Open your web browser and navigate to http://localhost:6006 to view TensorBoard.

Note: Ensure the remote server’s firewall allows connections on port 6006 and that your local machine’s firewall allows outgoing connections.