Airfoil Design

Airfoil design aims to optimize airfoil shapes for enhanced aerodynamic performance, typically focusing on maximizing lift and minimizing drag. Current research heavily utilizes machine learning, employing generative models like diffusion probabilistic models and GANs, as well as physics-embedded transfer learning and reinforcement learning frameworks, to efficiently explore the vast design space and predict aerodynamic characteristics. These data-driven approaches, often coupled with high-fidelity simulations and novel parameterization techniques, promise to significantly accelerate the design process and potentially lead to more efficient and innovative airfoil geometries for various applications.