摘要
翼型参数化方法对于翼型制造、气动和隐身等的优化设计具有非常重要的作用,为进一步提高参数化方法的表示能力,避免在优化过程中产生奇异外形进而提升翼型优化设计效率,结合外形控制能力更加灵活的类别形状函数变换方法(CST)及能够学习数据潜在分布的生成对抗网络模型(GAN),基于现有翼型数据库构建了一种新型翼型参数化方法:CST-GAN。通过考察生成翼型的几何质量及其表示误差,研究设计维度对CST-GAN翼型参数化的影响,并与Bezier、B样条及主成分分析(PCA)方法的表示精度进行了对比,最后基于该方法开展翼型优化设计。结果表明,该方法可以生成光滑、有效的几何外形,并能实现对翼型较为精确的描述。与其他几种常用参数化方法相比,CST-GAN方法具有较快的优化收敛速度和较好的优化效果,有助于改善优化效率,节约计算成本。此外,该方法鲁棒性强、易于实现,有拓展至三维机翼及整机的参数化建模并进行气动优化设计的应用潜力。
Airfoil parameterization method plays a very important role in airfoil manufacturing,aerodynamic and stealth optimization design.To further enhance the representation capability of the airfoil parameterization method,avoid abnormal geometric shapes during the optimization process,and improve the efficiency of airfoil optimization de⁃sign,in this paper,first,based on the existing airfoil databases,we propose a new airfoil parameterization method:CST-GAN,which combines more flexible Class and Shape Transformation(CST)method and Generative Adversarial Network(GAN)model where latent data distribution can be learnt.Then,the effect of design dimension on CST-GAN airfoil parameterization is studied by examining the geometric quality and representation error of the generated airfoils.Moreover,the representation accuracy of CST-GAN is compared with that of Bezier,B-spline,and Principal Compo⁃nent Analysis(PCA)method.Finally,airfoil optimization design based on the proposed parametrization method is conducted.The results show that the proposed method can generate smooth and effective geometric shapes and de⁃scribe the airfoil shape more precisely.Compared with other commonly used parameterization methods,the CSTGAN method exhibits faster optimization convergence speed and better optimization results,which contributes to opti⁃mization efficiency improvement and computational cost saving.In addition,the proposed method is robust and easy to implement,with potential applications to parametric modeling and aerodynamic optimization design of threedimensional wings and the entire aircraft.
作者
田洁华
孙迪
屈峰
白俊强
TIAN Jiehua;SUN Di;QU Feng;BAI Junqiang(School of Aeronautics,Northwestern Polytechnical University,Xi’an 710072,China)
出处
《航空学报》
EI
CAS
CSCD
北大核心
2023年第18期80-92,共13页
Acta Aeronautica et Astronautica Sinica
基金
翼型、叶栅空气动力学重点实验室基金(614220121010117)。
关键词
翼型
参数化
生成对抗网络
气动优化设计
类别形状函数变换
airfoil
parameterization
generative adversarial network
aerodynamic optimization design
class and shape transformation
