高速折叠翼飞行器气动布局优化设计

袁亚; 刘君; 余家泉; 张程; 任鹏飞

doi:10.13700/j.bh.1001-5965.2022.0849

高速折叠翼飞行器气动布局优化设计

doi: 10.13700/j.bh.1001-5965.2022.0849

袁亚^{1, 2},
刘君^1, ,,
余家泉²,
张程²,
任鹏飞²

1.
大连理工大学运载工程与力学学部，大连 116024
2.
中国运载火箭技术研究院，北京 100076

详细信息

通讯作者:
E-mail：liujun65@dlut.edu.cn

中图分类号: V221⁺.3；TB553
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- 被引次数: 0
出版历程
- 收稿日期: 2022-10-10
- 录用日期: 2023-03-10
- 网络出版日期: 2023-03-30
- 整期出版日期: 2024-11-30

Aerodynamic layout optimization design of high-speed folding-wing vehicles

YUAN Ya^{1, 2},
LIU Jun^{1
, ,},
YU Jiaquan²,
ZHANG Cheng²,
REN Pengfei²

1.
Faculty of Vehicle Engineering and Mechanics，Dalian University of Technology，Dalian 116024，China
2.
China Academy of Launch Vehicle Technology，Beijing 100076，China

More Information

Corresponding author: E-mail：liujun65@dlut.edu.cn

摘要

摘要:
为解决在内部载荷和外部发射装置约束条件下高速复杂折叠翼飞行器气动布局优化设计问题，提出基于类型函数/形状函数转换(CST)技术和直接参数结合的折叠翼飞行器参数化建模方法，开发了基于物面法矢量快速修正的黏性气动特性快速计算方法，并围绕飞行器气动性能的多目标优化设计，构建了基于克里金代理模型-遗传算法(Kriging-GA)的高速折叠翼飞行器多目标优化算法框架和优化流程，实现高速折叠翼飞行器气动布局的优化设计，获得了多目标多约束下的优化解集，可指导高速折叠翼飞行器气动布局优化设计。
- 折叠翼飞行器 /
- 参数化 /
- 克里金代理模型-遗传算法 /
- 气动性能 /
- 多目标优化
Abstract:
In order to solve the optimization design problem of the aerodynamic layout of complex high-speed folding-wing vehicles under the constraints of internal loads and external launch devices, a parametric modeling method for folding-wing vehicles based on the combination of class shape transformation (CST) and direct parameterization was proposed. In addition, a fast calculation method of viscous aerodynamic characteristics based on the rapid correction of the normal vector of the object plane was developed. The multi-objective optimization algorithm framework and optimization process of high-speed folding-wing vehicles based on Kriging-genetic algorithm (Kriging-GA) were constructed, so as to optimize the aerodynamic layout design of high-speed folding-wing vehicles. The optimization solution set under multiple objectives and constraints was obtained, which could guide the aerodynamic layout optimization design of high-speed folding-wing vehicles.
- folding-wing vehicle /
- parametric /
- Kriging-genetic algorithm /
- aerodynamic performance /
- multi-objective optimization

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图 1 典型CST方法生成截面

Figure 1. Typical cross-section generated by CST

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图 2 CST与直接参数化结合方法

Figure 2. Combination of CST and direct parameterization

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图 3 三维建模和网格划分

Figure 3. 3D modeling and meshing

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图 4 坐标系及三角面元

Figure 4. Coordinate system and triangular surface element

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图 5 法向矢量矫正

Figure 5. Normal vector correction

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图 6 HL-20飞行器物面压力流线及气动特性

Figure 6. Surface pressure streamline and aerodynamic characteristics of HL-20 vehicles

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图 7 典型折叠翼飞行器折叠翼展开前后升阻比

Figure 7. Lift-drag ratios of typical folding-wing vehicle before and after wing spreading

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图 8 LHS方法采样

Figure 8. LHS method sampling

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图 9 升阻比代理模型均方差

Figure 9. Mean square error of surrogate model of lift-drag ratio

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图 10 飞行器外部约束

Figure 10. External constraints of vehicle

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图 11 气动布局优化框架

Figure 11. Aerodynamic layout optimization framework

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图 12 折叠翼多目标优化的Pareto前沿

Figure 12. Pareto frontiers for multi-objective optimization of folding wings

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图 13 Pareto前沿个体对比

Figure 13. Comparison of Pareto frontiers

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表 1 折叠翼飞行器归一化设计参数

Table 1. Normalized design parameters of folding-wing vehicle

范围	R_head	x₂(截面2)	z₂(截面2)	N_cu	N_cd	H_u	H_d	C_WR	l₁	l₂	$ {\chi _{{_{\mathrm {WL}}} }} $	$ {\chi _{{_{\mathrm{WT}}} }} $	θ	$ {\chi _{_{\mathrm{RL}}}} $	$ {\chi _{{_{\mathrm{RT}}} }} $
上限	0.7143	0.8889	0.5000	0.1000	0.1000	0.2000	0.2000	0.9767	0.3750	0.3750	0.9286	0.8333	0.7143	0.9231	0.9767
下限	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000

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表 2 优化后的设计变量

Table 2. Optimized design variables

约束条件	R_head	x₂(截面2)	z₂(截面2)	N_cu	N_cd	H_u	H_d	C_WR	l₁	l₂	$ {\chi _{_{\mathrm{WL}}}} $	$ {\chi _{_{\mathrm{WT}}}} $	θ	$ {\chi _{_{\mathrm{RL}}}} $	$ {\chi _{_{\mathrm{RT}}}} $
升阻比最大	0.7515	0.9827	0.7605	0.9848	1.0000	0.9949	0.8412	0.9920	0.4244	0.4437	0.9985	0.8657	0.8279	0.9464	0.8751
体积最大	0.7787	0.9460	0.7732	0.1278	0.1038	1.0000	1.0000	0.9862	0.4205	0.4525	0.9961	0.9655	0.8299	0.9622	0.8523

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表 3 Pareto前端个体性能

Table 3. Performance of Pareto frontiers

典型结果	K_unfold	C_Dfold	V/m³	S₂/m²	l₁/mm	l₂/mm	Q/(kW·m⁻²)
升阻比最大/阻力最小/体积最小	4.23	0.1833	0.40	0.1272	398	358	1315.0
升阻比最小/阻力最大/体积最大	2.19	0.5462	0.63	0.1920	400	359	1291.8

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