Bézier-GAN

Experiment code associated with the paper: Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks

License

This code is licensed under the MIT license. Feel free to use all or portions for your research or related projects so long as you provide the following citation information:

Chen, W., Chiu, K., & Fuge, M. D. (2020). Airfoil Design Parameterization and Optimization Using Bézier Generative Adversarial Networks. AIAA Journal, 58(11), 4723-4735.

@article{chen2020airfoil,
  title={Airfoil Design Parameterization and Optimization Using B{\'e}zier Generative Adversarial Networks},
  author={Chen, Wei and Chiu, Kevin and Fuge, Mark D},
  journal={AIAA Journal},
  volume={58},
  number={11},
  pages={4723--4735},
  year={2020},
  publisher={American Institute of Aeronautics and Astronautics}
}

Required packages

• tensorflow<2.0.0
• pyDOE
• sklearn
• numpy
• matplotlib
• autograd

Dataset

Our airfoil designs come from UIUC airfoil coordinates database.

The raw data contains variable number of points along airfoil curves. We created the training data by applying B-spline interpolation on these designs and removed outlier designs.

CFD solver

We use XFOIL as the CFD solver to evaluate the performance of the airfoil design.

Usage

Train/evaluate Bézier-GAN

Go to Bézier-GAN's directory:

cd beziergan

Train a Bézier-GAN or evaluate a trained Bézier-GAN:

python train_gan.py

positional arguments:

mode    train or evaluate
latent  latent dimension
noise   noise dimension

optional arguments:

-h, --help              show this help message and exit
--model_id      model ID
--save_interval     number of intervals for saving the trained model and plotting results

The trained model and synthesized shape plots will be saved under directory beziergan/trained_gan/<latent>_<noise>/<model_id>, where <latent>, <noise>, and <model_id> are latent dimension, noise dimension, and model ID specified in the above arguments.

Run optimization

Bézier-GAN + Two Stage Optimization

python optimize_gan_bo_refine.py

positional arguments:

latent  latent dimension
noise   noise dimension

optional arguments:

--n_runs        number of experiment runs
--n_eval        number of evaluations per run
-h, --help              show this help message and exit

Bézier-GAN + One Stage Optimization

python optimize_gan_bo.py

positional arguments:

latent  latent dimension
noise   noise dimension

optional arguments:

--n_runs        number of experiment runs
--n_eval        number of evaluations per run
-h, --help              show this help message and exit

Optimization history and optimal airfoil shapes are saved under directory results_opt.

Results

Randomly generated airfoils:

Optimization history:

Optimized airfoils: