The design of aerodynamic shapes, such as airfoils, has traditionally required significant computational resources and relied on predefined design parameters, which limit the potential for novel shape synthesis. In this work, we introduce a data-driven methodology for airfoil generation using a diffusion model. Trained on a dataset of preexisting airfoils, our model can generate an arbitrary number of new airfoils from random vectors, which can be conditioned on specific aerodynamic performance metrics such as lift and drag, or geometric criteria. Our results demonstrate that the diffusion model effectively produces airfoil shapes with realistic aerodynamic properties, offering substantial improvements in efficiency, flexibility, and the potential for discovering innovative airfoil designs. This approach significantly expands the design space, facilitating the synthesis of high-performance aerodynamic shapes that transcend the limitations of traditional methods.

翻译：空气动力学外形（如翼型）的设计传统上需要大量计算资源，并依赖于预定义的设计参数，这限制了新颖形状合成的潜力。本研究提出了一种基于扩散模型的数据驱动翼型生成方法。该模型在已有翼型数据集上进行训练，能够从随机向量生成任意数量的新翼型，并可根据特定气动性能指标（如升力和阻力）或几何准则进行条件约束。结果表明，该扩散模型能有效生成具有真实气动特性的翼型形状，在效率、灵活性和发现创新翼型设计的潜力方面均有显著提升。该方法极大拓展了设计空间，有助于合成超越传统方法限制的高性能空气动力学外形。