AeroGraphNet for external aerodynamic evaluation
This example demonstrates how to train the AeroGraphNet model for external aerodynamic analysis of simplified (Ahmed body-type) car geometries. AeroGraphNet is based on the MeshGraphNet architecture. It achieves good accuracy on predicting the pressure and wall shear stresses on the surface mesh of the Ahmed body-type geometries, as well as the drag coefficient.
To goal is to develop an AI surrogate model that can use simulation data to learn the external aerodynamic flow over parameterized Ahmed body shape. This serves as a baseline for more refined models for realistic car geometries. The trained model can be used to predict the change in drag coefficient,and surface pressure and wall shear stresses due to changes in the car geometry. This is a stepping stone to applying similar approaches to other application areas such as aerodynamic analysis of aircraft wings, real car geometries, etc.
Industry-standard Ahmed-body geometries are characterized by six design parameters: length, width, height, ground clearance, slant angle, and fillet radius. Refer to the wiki for details on Ahmed body geometry. In addition to these design parameters, we include the inlet velocity to address a wide variation in Reynolds number. We identify the design points using the Latin hypercube sampling scheme for space filling design of experiments and generate around 500 design points.
The aerodynamic simulations were performed using the GPU-accelerated OpenFOAM solver for steady-state analysis, applying the SST K-omega turbulence model. These simulations consist of 7.2 million mesh points on average, but we use the surface mesh as the input to training which is roughly around 70k mesh nodes.
To request access to the full dataset, please reach out to the NVIDIA Modulus team.
The AeroGraphNet model is based on the MeshGraphNet architecture which is instrumental for learning from mesh-based data using GNNs. The inputs to the model are:
Ahmed body surface mesh
Reynolds number
Geometry parameters (optional, including length, width, height, ground clearance, slant angle, and fillet radius)
surface normals (optional)
Output of the model are:
Surface pressure
Wall shear stresses
Drag coefficient
Fig. 2 Comparison between the AeroGraphNet prediction and the ground truth for surface pressure, wall shear stresses, and the drag coefficient for one of the samples from the test dataset.
The input to the model is in form of a .vtp file and is then
converted to bi-directional DGL graphs in the dataloader. The final
results are also written in the form of .vtp files in the inference
code. A hidden dimensionality of 256 is used in the encoder, processor,
and decoder. The encoder and decoder consist of two hidden layers, and
the processor includes 15 message passing layers. Batch size per GPU is
set to 1. Summation aggregation is used in the processor for message
aggregation. A learning rate of 0.0001 is used, decaying exponentially
with a rate of 0.99985. Training is performed on 8 NVIDIA A100 GPUs,
leveraging data parallelism. Total training time is 4 hours, and
training is performed for 500 epochs.
The dataset for this example is not publicly available. To get access, please reach out to the NVIDIA Modulus team.
This example requires the pyvista and vtk libraries. Install
with
pip install pyvista vtk
To train the model, run
python train.py
Data parallelism is also supported with multi-GPU runs. To launch a multi-GPU training, run
mpirun -np <num_GPUs> python train.py
If running in a docker container, you may need to include the
--allow-run-as-root in the multi-GPU run command.
Progress and loss logs can be monitored using Weights & Biases. To
activate that, set wandb_mode to online in the constants.py.
This requires to have an active Weights & Biases account. You also need
to provide your API key. There are multiple ways for providing the API
key but you can simply export it as an environment variable
export WANDB_API_KEY=<your_api_key>
The URL to the dashboard will be displayed in the terminal after the run
is launched. Alternatively, the logging utility in train.py can be
switched to MLFlow.
Once the model is trained, run
python inference.py
This will save the predictions for the test dataset in .vtp format
in the outputs directory. Use Paraview to open and explore the
results.