提出了基于代理模型的两步优化方法,用于翼型在黏性流场中气动外形的优化设计。第一步优化使用基于代理模型的遗传算法(GA)获得全局最优解的大致范围,以本征正交分解(POD)方法作为第一步优化中气动力计算的代理模型方法,降低遗传算法的计算量,并对其采样解的生成方法进行改进,提高了计算精度;第二步优化使用基于Navier-Stokes方程的最速下降法(SDA),既改善了第一步优化结果,又修正了代理模型所引入的误差。针对传统型函数方法在翼型后缘表达不足的缺陷进行改进,提高了型函数对翼型的表达精度。不同外形翼型的反设计结果以及不同来流状态下的优化设计结果表明,本文提出的两步优化方法是一种高效且具有实用价值的优化方法。
This article presents a two-step optimization based on a surrogate model for the aerodynamic shape optimization in a viscous flow. The first-step employs the genetic algorithm (GA) to search the range of the global optimal solution. The surrogate model gappy proper orthogonal decomposition (POD) is used to replace computational dluid dynamics to estimate the flow solution, and an improvement is made on the snapshots to raise the accuracy of the gappy POD. The second-step optimization employs the steepest descent algorithm (SDA) based on the Navier-Stokes equations to refine the result of the first-step optimization. The Hicks-Henne bump function, by which the airfoil shape is parameterized, is improved to overcome its inherent flaw at the trailing edge of the airfoil. Finally, the results of the inverse design for different shapes of the airfoil and the optimal design in different states of freestream flow demonstrate that the two-step optimization is efficient and practical for the airfoil design in the viscous flow.
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