Machine learning models are recently utilized for airfoil shape generation methods. It is desired to obtain airfoil shapes that satisfies required lift coefficient. Generative adversarial networks (GAN) output reasonable airfoil shapes. However, shapes obtained from ordinal GAN models are not smooth, and they need smoothing before flow analysis. Therefore, the models need to be coupled with Bezier curves or other smoothing methods to obtain smooth shapes. Generating shapes without any smoothing methods is challenging. In this study, we employed conditional Wasserstein GAN with gradient penalty (CWGAN-GP) to generate airfoil shapes, and the obtained shapes are as smooth as those obtained using smoothing methods. With the proposed method, no additional smoothing method is needed to generate airfoils. Moreover, the proposed model outputs shapes that satisfy the lift coefficient requirements.
翻译:机器学习模型最近用于空气油形状的生成方法。 想要获得符合所需升系数的空气油形状。 生成对抗性网络( GAN) 输出合理的空气油形状。 但是, 从 ordinal GAN 模型中获得的形状并不平滑, 需要平滑才能进行流程分析。 因此, 模型需要与贝塞尔曲线或其他平滑方法相配合, 才能获得平滑的形状。 在没有任何平滑方法的情况下生成形状具有挑战性。 在本研究中, 我们使用有条件的瓦西尔斯坦 GAN 和梯度惩罚( CWGAN-GP) 来生成空气油形状, 所获得的形状与使用平滑方法获得的形状一样平滑。 使用拟议方法, 不需要额外的平滑方法来生成空气油。 此外, 拟议的模型输出形状满足了电梯系数要求。