Diffusion model-driven multi-objective generative design of supercritical airfoils

In the engineering practice of aircraft aerodynamic design, design specifications are typically proposed by the overall engineering team, while the aerodynamic design department implements design goals through numerous iterations and extensive numerical simulations. This process usually consumes significant resources. Generative models show the potential to directly generate design solutions that meet predefined goals, significantly reducing the iterative process in traditional design. This paper proposes a multi-objective generative airfoil design method based on diffusion models. By conditioning on multiple performance metrics such as buffeting lift coefficient, cruise drag coefficient and thickness, the method generates the airfoil designs that simultaneously satisfy these metrics. The use of conditional diffusion models to progressively generate effective airfoils in the design space avoids the complex iterative calculations of traditional optimization methods. Comparative experiments with conditional variational autoencoders demonstrate the advantages of diffusion models in terms of generation accuracy, stability, and diversity. The results indicate that diffusion models can not only generate airfoils that meet performance requirements but also offer greater diversity and design space exploration capability, providing an efficient new approach for future airfoil design.