基于改进CST参数化方法和转捩模型的翼型优化设计

为提高翼型优化设计效率,增大设计空间,采用B样条基函数替代传统的形状类别函数(CST)方法中的Bezier多项式,增强了对翼型参数化表达的局部控制能力并提高了翼型局部表达精度。为了确保翼型在优化设计过程中的几何光顺特性和代理模型的准确性,采用小波分解技术提出了多分辨率翼型的局部光顺方法。采用基于本征正交分解(POD)的流场数值代理模型,并结合γ-Reθt转捩模型实现了快速准确的气动力与流动转捩预测。采用小波技术光顺的CST翼型参数化建模、POD流场数值计算代理模型以及γ-Reθt转捩模型,结合遗传算法建立了完整的翼型气动优化设计系统。针对低速层流翼型与超临界翼型进行优化设计,优化设计后的翼型升阻比分别提高了47.42%和45.85%,且对改进前后参数化建模方法的优化性能进行了对比,结果表明本文构建的翼型气动优化设计系统具备很高的优化效率。

Airfoil optimization based on improved CST parametric method and transition model

In this paper, B-spline basis function is implemented in class and shape transformation (CST) parameterization method in place of the traditional Bezier polynomials to enhance the local ability of control and accuracy to represent an airfoil shape. To guarantee the requirements on geometric smoothing performance and proper orthogonal decomposition (POD) reconstruction accuracy in airfoil design optimization process, the local fairing method for multi-resolution airfoil is proposed based on the wavelet decomposition technique, expanding the design space. A surrogate model based on POD method and γ-Re_θt transition predicting model is adopted to achieve fast and accurate prediction of aerodynamic forces and transition. The computed results of flow around airfoils show that the combined surrogate model is an effective method in design optimization of natural laminar airfoil. A complete aerodynamic design optimization system for natural laminar airfoil is constructed by integrating genetic algorithm, the improved CST parameterization method with wavelet decomposition fairing, POD surrogate model and γ-Re_θt transition model. The system is used for the design optimization of low-speed and transonic airfoils, achieving an increase in lift-drag ratio of 47.42% and 45.85%, respectively, which validates the efficiency of the design optimization system proposed in this paper.