航空学报 > 2011, Vol. 32 Issue (1): 75-82   doi: CNKI:11-1929/V.20101111.0911.016

多控制面飞行器结构与配平鲁棒气动弹性优化方法

杨超, 肖志鹏, 万志强   

  1. 北京航空航天大学 航空科学与工程学院, 北京 100191
  • 收稿日期:2010-04-26 修回日期:2010-06-17 出版日期:2011-01-25 发布日期:2011-01-25

A Robust Aeroelastic Optimization Method of Structure and Trim for Air Vehicle with Multiple Control Surfaces

YANG Chao, XIAO Zhipeng, WAN Zhiqiang   

  1. School of Aeronautic Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
  • Received:2010-04-26 Revised:2010-06-17 Online:2011-01-25 Published:2011-01-25

摘要: 基于遗传算法,提出了一种考虑多控制面飞行器结构参数和配平关系中的不确定性的鲁棒气动弹性优化方法,并在一个带有主动气动弹性机翼(AAW)的复杂小展弦比飞机的结构和控制面传动比的鲁棒设计中得到了应用。以非概率形式来衡量设计变量的不确定性变化,采用单目标函数形式,并引入一个反映目标函数相对变化的额外约束来描述鲁棒优化问题。在严重载荷状态下,以结构质量最小化为目标,以控制面偏转角、铰链力矩、翼根载荷和临界颤振速度为鲁棒约束条件,实现了结构和传动比的同步优化设计。通过与不考虑鲁棒性要求的传统设计方法进行比较,论证了鲁棒设计方法的有效性。研究结果表明,鲁棒最优解虽然要付出一定的质量代价,但对结构设计变量和传动比设计变量的不确定性摄动具有更好的抗干扰性。

关键词: 多控制面, 鲁棒优化设计, 主动气动弹性机翼, 遗传算法, 颤振, 飞行载荷

Abstract: A robust aeroelastic optimization method which is based on the genetic algorithm and takes into consideration the uncertainties in structure and trim is presented for an air vehicle with multiple control surfaces.This method is applied to the robust design of structure and gear ratios between the control surfaces for a complicated aircraft with a low-aspect-ratio and an active aeroelastic wing(AAW). The uncertain variation of design variables is estimated with a non-probability method. Robust optimization is formulated in the form of a single objective by introducing an additional constraint which reflects the relative change of objective. The concurrent optimization design of the structure and gear ratios is implemented in the case of critical load conditions. The objective is to minimize the structural mass subject to the robust constraints of deflection angles of control surfaces, hinge moments, loads acting on the wing root and critical flutter speed. A comparison is also performed between the robust method and the traditional method in which the robust requirements are not considered, and the validity of the robust method is demonstrated. The result shows that the robust optimization solution is more resistant to jamming in the face of the perturbation of design variables of structure and gear ratios. However, a mass increment has to be paid for satisfying the robust requirements.

Key words: multiple control surfaces, robust optimization design, active aeroelastic wing, genetic algorithm, flutter, flight loads

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