基于变精度遗传算法的翼型快速优化设计方法

低碳环保的电动飞机在要求较高升阻比的同时,需要尽量降低成本、缩短研制周期。但高精度的数值模拟时间代价很大,因此针对电动飞机翼型设计中初始翼型较难选取、优化速度较慢的问题,提出了一种基于变精度遗传算法的翼型多点快速优化方法。以常用的 Hicks-Henne 型函数为基础,改进了其对翼型后缘描述不精确的问题。在数值模拟阶段,介绍了一种快速气动参数计算软件XFOIL,并分析了该软件的适用性与局限。之后给出了使用XFOIL 与 Matlab 进行联合求解的方法,在无人干预的情况下完全实现了翼型设计与优化的自动化,提高了设计效率。在翼型优化阶段,为保持较高的精度和寻优效率,设计了翼型参数的实数编码方法。针对传统遗传优化算法了改进,设计了染色体变精度杂交方法以及动态惩罚方法。最后,给出了基于遗传算法的多点优化方案,以及翼型多目标快速优化一体化设计方案。仿真分成两部分进行,首先改进的 Hicks-Henne 型函数能够有效实现参数化翼型的后缘夹角改变。通过与 NSGA-II 方法的优化结果对比,本文的方法在一定迭代次数范围内获得的升阻比更高,失速特性更加缓和,特别是在综合提高翼型优化效率方面表现较好。仿真结果表明,该方法能够快速获得多种工况下具有较高升阻比的翼型,也可以作为进一步优化的初始翼型,能提高翼型优化效率。

Rapid design and optimization of airfoil based on improved genetic algorithm

Electric aircraft with low carbon consumption is gradually developed along with the growing demand of civilian aircraft. The production of electric aircraft pursues lower costs and shorter development cycle. In the process of designing an airfoil, it is hard to select the initial airfoil, and most optimization methods are very time consuming. An improved genetic algorithm ( GA) with variable resolution is developed for rapid multi-objective optimization of airfoils. Based on the original Hicks-Henne shape function, the representation of airfoil on trailing edge is improved. In the calculation of aerodynamic parameters, a subsonic airfoil development system XFOIL is introduced which is faster than conventional CFD software, and the applicability and limitation of XFOIL is also analyzed. Then a joint method combining XFOIL and Matlab is proposed, and it realizes a full automatic design of airfoil without the intervention of human. In the stage of optimization, parameters of airfoil are real-coded to maintain high accuracy and efficiency. In addition, the conventional GA is improved by hybridization with variable resolution and dynamic penalty. At last, the integrated design solution of rapid multi-objective and multi-point optimization is summarized. Simulation is divided into two parts, and the improved Hicks-Henne shape function can change the angle of trailing edge effectively. By the comparison with elitist nondominated sorting genetic algorithm (NSGA-II), the proposed method will get higher lift to drag ratio within certain number of iterations, the stall characteristic is more moderate, and it especially improves efficiency performance. The integrated design solution is accelerated by numerical calculation and improved GA, and it has a lower computational cost. The simulation results show that the method is useful in engineering conditioning for the rapid design and optimization of airfoil shapes, particularly in the preliminary design stage, such as the selection of initial airfoil. Currently it is uncertain whether the results of proposed method are better than others after a long time of iterations, and our future work will focus on it.