Rapid aircraft shape generation based on deep learning

Aerodynamic configuration design technology for aircraft is one of the critical research directions for developing advanced aircraft and achieving generational leaps in performance. Traditional aerodynamic layout design faces challenges such as difficulties in selecting aerodynamic configurations and over-reliance on designer's experience. Moreover, current aerodynamic shape parameterization methods struggle to break through pre-defined aerodynamic configuration schemes, limiting optimization to adjusting parameters within the same aerodynamic configuration during the design process. This necessitates repeated iterations and optimizations by designers, leading to prolonged design cycles and difficulties in obtaining optimal aerodynamic shapes, thereby hindering efficient and rapid scheme evaluation. This paper proposes and develops an image-based aircraft shape generation framework, employing deep marching tetrahedra methods and differentiable renderers. Leveraging the powerful nonlinear fitting capabilities of neural networks, this framework achieves rapid and intelligent generation of aerodynamic shapes. The generated aircraft surfaces are smooth, high-resolution, and controllable in dimensional envelope, offering engineering usability without the need for denoising operations.