  The current design of aerodynamic shapes, like airfoils, involves
computationally intensive simulations to explore the possible design space.
Usually, such design relies on the prior definition of design parameters and
places restrictions on synthesizing novel shapes. In this work, we propose a
data-driven shape encoding and generating method, which automatically learns
representations from existing airfoils and uses the learned representations to
generate new airfoils. The representations are then used in the optimization of
synthesized airfoil shapes based on their aerodynamic performance. Our model is
built upon VAEGAN, a neural network that combines Variational Autoencoder with
Generative Adversarial Network and is trained by the gradient-based technique.
Our model can (1) encode the existing airfoil into a latent vector and
reconstruct the airfoil from that, (2) generate novel airfoils by randomly
sampling the latent vectors and mapping the vectors to the airfoil coordinate
domain, and (3) synthesize airfoils with desired aerodynamic properties by
optimizing learned features via a genetic algorithm. Our experiments show that
the learned features encode shape information thoroughly and comprehensively
without predefined design parameters. By interpolating/extrapolating feature
vectors or sampling from Gaussian noises, the model can automatically
synthesize novel airfoil shapes, some of which possess competitive or even
better aerodynamic properties comparing to airfoils used for model training
purposes. By optimizing shapes on the learned latent domain via a genetic
algorithm, synthesized airfoils can evolve to target aerodynamic properties.
This demonstrates an efficient learning-based airfoil design framework, which
encodes and optimizes the airfoil on the latent domain and synthesizes
promising airfoil candidates for required aerodynamic performance.

Computational Engineering, Finance, and Science;Machine Learning