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针对超临界层流翼型设计问题,提出一种两轮优化策略。采用γ-Reθt转捩模型耦合剪切应力输运(SST)模式的湍流模型对翼型边界层转捩进行预测。翼型几何参数化建模采用形状分类函数转换(CST)方法,设计变量为描述翼型几何特征的参数。第1轮优化的目的是尽量提高层流区域的比例,气动分析模型为基于Kriging模型的代理模型,优化算法为遗传算法,通过优化获得满足约束要求的层流翼型。第2轮优化目的是对第1轮优化获得的翼型进行微调,进一步提高翼型的升阻比,气动分析直接采用CFD程序,优化算法采用基于梯度的优化算法。算例表明,应用本文提出的两轮优化策略,可将超临界翼型NASA SC(2)0412优化设计成超临界层流翼型,翼型的上下表面层流区比例分别达到了55.5%和47.0%,升阻比提高了38.1%。
Aiming at the problem of supercritical laminar airfoil design, a two-round optimization strategy is proposed. The airfoil boundary layer transition was predicted by the turbulence model of the γ-Reθt transition coupled shear stress transport (SST) model. Airfoil geometrical parametric modeling uses the Shape Classification Function Conversion (CST) method, which is a parameter describing the geometric characteristics of the airfoil. The first round of optimization aims to maximize the proportion of laminar flow area. The aerodynamic analysis model is a Kriging model-based proxy model. The optimization algorithm is genetic algorithm, and the laminar flow airfoils satisfying the constraints are obtained through optimization. The purpose of the second round of optimization is to fine-tune the airfoil optimized in the first round to further improve the lift-drag ratio of the airfoil. The CFD program is used for the aerodynamic analysis directly, and the optimization algorithm uses a gradient-based optimization algorithm. The numerical examples show that the supercritical airfoil NASA SC (2) 0412 can be optimized to be a supercritical laminar flow airfoil with two rounds of optimization strategies proposed in this paper. The laminar flow areas at the upper and lower surfaces of the airfoil reach 55.5% and 47.0%, lifting resistance ratio increased 38.1%.