面向狭小空间的起落架收放轨迹智能设计方法

朱林昊; 邹泽铧; 印寅; 刘相阳; 蔡新之

doi:10.13700/j.bh.1001-5965.2020.0440

面向狭小空间的起落架收放轨迹智能设计方法

doi: 10.13700/j.bh.1001-5965.2020.0440

1.
南京航空航天大学理学院, 南京 210016
2.
南京航空航天大学机电学院, 南京 210016
3.
南京航空航天大学机械结构力学及控制国家重点实验室, 南京 210016
4.
南京工业大学2011学院, 南京 211816

基金项目:

国家自然科学基金 51805249

江苏省自然科学基金 BK20180436

中央高校基本科研业务费专项资金 NF2018001

江苏高校优势学科建设工程

详细信息

通讯作者:
印寅, E-mail: yinyin@nuaa.edu.cn

中图分类号: V226
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- 文章访问数: 490
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- PDF下载量: 26
- 被引次数: 0
出版历程
- 收稿日期: 2020-08-20
- 录用日期: 2020-12-21
- 网络出版日期: 2021-12-20

Intelligent design method of landing gear retraction and extension trajectory for narrow space

1.
College of Science, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2.
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
3.
State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
4.
2011 College, Nanjing Tech University, Nanjing 211816, China

Funds:

National Natural Science Foundation of China 51805249

Natural Science Foundation of Jiangsu Province BK20180436

the Fundamental Research Funds for the Central Universities NF2018001

Priority Academic Program Development of Jiangsu Higher Education Institutions

More Information

Corresponding author: YIN Yin, E-mail: yinyin@nuaa.edu.cn

摘要

摘要:
扁平化气动外形是高超声速飞行器获得较高升阻比的优先布局，但该外形严重约束了起落架的收藏空间，常规机构很难满足要求，只能采用复杂机构的三维运动实现起落架的窄空间收放。然而，当前主流的计算机辅助设计迭代试凑法在解决空间机构设计问题方面非常依赖工程经验，耗时耗力且很难得到最优结果。为解决这一问题，创新性地提出基于智能优化算法的起落架复杂机构自主设计方法。首先，分析并建立起落架收放机构的运动学理论模型；然后，建立起落架结构间距离描述及碰撞检测模型，并运用深度神经网络自主设计起落架收放机构的最优运动轨迹；最后，以某狭窄舱段的起落架收放策略设计为例，应用该设计方法进行设计。结果表明：所提设计方法可以快速得到最优的起落架收放机构设计方案，可用于指导高超声速飞行器起落架收放机构的设计。
- 起落架收放 /
- 智能设计 /
- 路径分析 /
- 优化驱动 /
- 三角网格
Abstract:
The flattened aerodynamic shape is the preferred layout for hypersonic vehicles to obtain high lift drag ratio, but this shape seriously restricts the collection space of the landing gear, and the conventional mechanism is difficult to meet the requirements. Engineers can only use the three-dimensional motion of the complex mechanism to realize the narrow space retraction and retraction of the landing gear. However, the current mainstream iterative trial and error method of computer-aided design relies heavily on engineering experience in solving the design problems of spatial mechanisms, which is time-consuming and labor-consuming, and it is difficult to obtain the optimal results. To address this problem, an intelligent optimization algorithm based method for autonomous design of complex landing gear mechanisms is proposed in this paper. First, theoretical kinematics model of retraction mechanism is built after analysis. Then, the model of inter-structure distances between landing gears and fuselage is established for detecting collisions, and the optimal motion trajectory of landing gear mechanisms is designed autonomously by using the deep neural network based learning optimization algorithm. Finally, the proposed method is applied to a landing gear mechanism design of certain hypersonic aircraft with a narrow cabin. The results show that the optimal design scheme for retractable mechanism can be obtained quickly by the proposed method, which can be used to guide the design of landing gear retractable mechanism for hypersonic aircraft.
- landing gear retraction and extension /
- intelligent design /
- path analysis /
- optimization drive /
- triangular mesh

HTML全文

图 1 起落架的收放过程示意图

Figure 1. Schematic diagram of landing gear retraction and extension process

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图 2 起落架运动过程中产生的扫掠体示意图

Figure 2. Schematic diagram of swept body generated during landing gear movement

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图 3 坐标与位置向量示意图

Figure 3. Schematic diagram of coordinate and position vector

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图 4 转轴分布平面

Figure 4. Shaft distribution plan

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图 5 深度图方法

Figure 5. Depth map method

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图 6 点云方法

Figure 6. Point cloud method

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图 7 体素方法

Figure 7. Voxel method

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图 8 网格方法

Figure 8. Mesh method

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图 9 可能解构成的区域

Figure 9. Regions of possible solutions

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图 10 机舱舱段模型

Figure 10. Cabin model

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图 11 起落架模型

Figure 11. Landing gear model

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图 12 收放位置示意图

Figure 12. Schematic diagram of retraction and extension position

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图 13 低精度的舱段划分

Figure 13. Low-precision cabin division

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图 14 高精度的舱段划分

Figure 14. High-precision cabin division

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图 15 低精度的起落架划分

Figure 15. Low-precision landing gear division

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图 16 高精度起落架划分

Figure 16. High-precision landing gear division

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图 17 回收路径的最短距离与参数θ的关系

Figure 17. Relationship between the shortest distance of retraction path and parameter θ

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表 1 应用不同的划分方式得到的结果

Table 1. Results by different partition methods

起落架精度	舱段精度	最优参数/(°)	计算时间/s	最短距离/mm
高精度	高精度	231.0	1 666.7	120.1
低精度	低精度	227.0	232.9	128.6
高精度	低精度	230.0	1 050.6	146.7
低精度	高精度	226.0	354.6	99.8

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表 2 选取不同优化参数得到的结果

Table 2. Results by selecting different optimization parameters

学习率	θ－θ₀/(°)	φ－φ₀/(°)	最短距离/mm
0	0	0	128.6
0.05	0.119 661	-0.012 962	130.082 994
0.1	0.238 078	-0.154 524	132.254 419
0.2	0.323 544	-0.063 298	132.072 357

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