Aerodynamic Shape Optimization

Aerodynamic shape optimization aims to design shapes that minimize drag or maximize lift, improving vehicle performance and efficiency. Current research heavily utilizes machine learning, employing deep neural networks (including convolutional and variational autoencoders), reinforcement learning, and Gaussian processes to model complex aerodynamic interactions and efficiently explore the design space. These methods are applied to various domains, including automotive and aerospace engineering, enabling faster and more effective optimization compared to traditional methods. The resulting improvements in design efficiency and performance have significant implications for fuel consumption, emissions, and overall vehicle design.