论文标题
使用Bézier生成对抗网络的翼型设计参数化和优化
Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks
论文作者
论文摘要
由于设计空间的高维度，空气动力学形状的全球优化通常需要大量昂贵的计算流体动力学模拟。解决此问题的一种方法是通过获得新的表示来减少设计空间维度。这需要一个参数函数，该函数紧凑，充分描述了形状的有用变化。我们提出了一个深层生成模型Bézier-GAN，以通过从现有数据库中的形状变化中学习来参数化空气动力学设计。所得的新参数化可以通过改善表示压缩，同时保持足够的表示能力来加速设计优化。我们以机翼设计为例来演示这一想法并分析Bézier-Gan的表示能力和紧凑性。结果表明，Bézier-GAN（1）都学习了各种翼型的平滑形状表示形式，并且（2）与最先进的参数化方法相比，经验将优化收敛至少两倍。
Global optimization of aerodynamic shapes usually requires a large number of expensive computational fluid dynamics simulations because of the high dimensionality of the design space. One approach to combat this problem is to reduce the design space dimension by obtaining a new representation. This requires a parametric function that compactly and sufficiently describes useful variation in shapes. We propose a deep generative model, Bézier-GAN, to parameterize aerodynamic designs by learning from shape variations in an existing database. The resulted new parameterization can accelerate design optimization convergence by improving the representation compactness while maintaining sufficient representation capacity. We use the airfoil design as an example to demonstrate the idea and analyze Bézier-GAN's representation capacity and compactness. Results show that Bézier-GAN both (1) learns smooth and realistic shape representations for a wide range of airfoils and (2) empirically accelerates optimization convergence by at least two times compared to state-of-the-art parameterization methods.
