  Airfoil shape optimization plays a critical role in the design of
high-performance aircraft. However, the high-dimensional nature of airfoil
representation causes the challenging problem known as the "curse of
dimensionality". To overcome this problem, numerous airfoil parameterization
methods have been developed, which can be broadly classified as
polynomial-based and data-driven approaches. Each of these methods has
desirable characteristics such as flexibility, parsimony, feasibility, and
intuitiveness, but a single approach that encompasses all of these attributes
has yet to be found. For example, polynomial-based methods struggle to balance
parsimony and flexibility, while data-driven methods lack in feasibility and
intuitiveness. In recent years, generative models, such as generative
adversarial networks and variational autoencoders, have shown promising
potential in airfoil parameterization. However, these models still face
challenges related to intuitiveness due to their black-box nature. To address
this issue, we developed a novel airfoil parameterization method using
physics-aware variational autoencoder. The proposed method not only explicitly
separates the generation of thickness and camber distributions to produce
smooth and non-intersecting airfoils, thereby improving feasibility, but it
also directly aligns its latent dimensions with geometric features of the
airfoil, significantly enhancing intuitiveness. Finally, extensive comparative
studies were performed to demonstrate the effectiveness of our approach.
