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摘要:
针对连续变弯度机翼，采用主动气动弹性机翼(AAW)技术，发展基于遗传算法的机翼结构综合优化设计方法。以结构质量最小化为目标，变弯度前/后缘等效偏转角、翼根弯矩和颤振速度为约束条件，在稳态滚转机动状态下对基于多个连续变弯度结构配平的小展弦比飞机缩比模型进行优化设计，并与传统单变弯度结构配平的设计方法进行对比。结果表明：变弯度前/后缘较传统舵面可提高34.71%的操纵效率，且采用多段变弯度前/后缘联合变形的AAW技术可充分利用机翼结构的柔性，从而有效降低机翼机动载荷，结构质量可减轻12.9%。
Abstract:An integrated optimization design methodology is developed for variable camber wing using active aeroelastic wing (AAW) technology, based on a genetic optimization algorithm. The optimization design for low aspect ratio aircraft’s scale model’s trim using multiple variable camber wings is conducted in a steady rolling maneuver. The goal is to reduce the structural mass while taking into account the limitations of the wing root’s moment, flutter speed, and the equivalent deflection angle of the morphing leading and trailing edges. A comparison between the new method and the conventional design method with single variable camber is also presented. The results showed that morphing the leading and trailing edges could increase control efficiency by 34.71% when compared to the traditional control surface. By coordinating the morphing of multiple leading and trailing edges, the AAW technology could fully utilize the flexibility of the wing structure to effectively reduce maneuver load and the wing’s structural mass by 12.9%.
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Key words:
- wing /
- variable camber /
- active aeroelastic /
- optimization design /
- genetic algorithm /
- maneuver load
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图 2 变形翼肋结构有限元模型变形分布
Figure 2. Deformation distribution of flexible rib’s structural finite element model
图 3 优化设计对象结构有限元模型
Figure 3. Structural finite element model of optimization design object
图 4 气动网格模型及变弯度结构分布
Figure 4. Aerodynamic mesh model and variable camber structure’s distribution
图 6 TEO等效偏转角为5°时滚转角速度随来流速度的变化
Figure 6. Rolling angular velocity changing with flow velocity when equivalent deflection angle of TEO is 5 degrees
图 9 不同配平方案下配平等效偏转角随来流速度变化
Figure 9. Trim equivalent deflection angel of different trim scheme changing with flow velocity
图 10 不同配平方案下翼根弯矩随来流速度变化
Figure 10. Wing root’s bending moment of different trim scheme changing with flow velocity
表 1 优化变量取值范围
Table 1. Optimization variables range
变量 最小值 最大值 n1 −10 10 n2 −10 10 δLEO/(°) −10 10 δTEO/(°) −10 10 δTEI/(°) −10 10 
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表 2 约束变量取值范围
Table 2. Constrained variables range
取值范围 Mwingroot/(N·m) n1 n2 δLEO/(°) δTEO/(°) δTEI/(°) Vf/(m·s−1) 最小值 −10 −10 −10 −10 −10 −10 255 最大值 10 10 10 10 10 10 10
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表 3 优化结果
Table 3. Optimization outcome
配平方案 δLEO/(°) δTEO/(°) δTEI/(°) Mwingroot/(N·m) TEO单独配平 0 −9.98 0 −9.3 TEO/TEI/LEO联合配平 −8.5 −0.9 3.2 −8.5
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