无人机气动/风能获取一体化设计

王为; 安伟刚; 宋笔锋; 杨文青; 罗洋

doi:10.7638/kqdlxxb-2024.0168

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

无人机气动/风能获取一体化设计

研究论文 | 更新时间：2026-05-28

- 无人机气动/风能获取一体化设计
- Aerodynamic and wind energy harvesting integrative design for UAVs
- 空气动力学学报 2026年44卷第1期页码：48-58
- 作者机构：
  
  1. 飞行器基础布局全国重点实验室,西安,710072
  2. 西北工业大学航空学院,西安,710072
- 作者简介：
  
  王为（1996—），男，云南昆明人，博士研究生，研究方向：动态滑翔. E-mail：Vulpes.wang@mail.nwpu.edu.cn
  安伟刚*，副教授，研究方向：仿生飞行器设计. E-mail：anweigang@nwpu.edu.cn
- 基金信息：
  
  西北工业大学博士论文创新基金（CX2024037）
- DOI：10.7638/kqdlxxb-2024.0168
  中图分类号： V22
- 纸质出版：2026
- 稿件说明：
移动端阅览
王为, 安伟刚, 宋笔锋, 等. 无人机气动/风能获取一体化设计[J]. 空气动力学学报, 2026, 44(1): 48−58.

Wang Wei, An Weigang, Song Bifeng, et al. Aerodynamic and wind energy harvesting integrative design for UAVs[J]. 2026, 44(1): 48-58.
王为, 安伟刚, 宋笔锋, 等. 无人机气动/风能获取一体化设计[J]. 空气动力学学报, 2026, 44(1): 48−58. DOI： 10.7638/kqdlxxb-2024.0168

Wang Wei, An Weigang, Song Bifeng, et al. Aerodynamic and wind energy harvesting integrative design for UAVs[J]. 2026, 44(1): 48-58. DOI： 10.7638/kqdlxxb-2024.0168.

摘要

动态滑翔作为一种通过穿越水平风梯度实现无动力长距离飞行的特殊飞行模式，在提升无人机航程与航时方面极具潜力。本文聚焦固定翼参数化设计，通过仿生关节结构的静态外形优化实现动态变形控制的替代，在避免复杂机构设计的同时提升获能效率。本文对不同设计参数对应的气动力进行计算，并基于计算结果提出无人机气动/风能获取一体化设计方法。该方法利用神经网络实现气动力的快速求解，以最大化获能为优化目标，利用高斯伪谱法求解最佳机翼外形，最终得到了满足高效能量获取的机翼外形。此外，本研究利用飞行仿真对比了基本翼、以最佳升阻比为目标设计的无人机和基于气动/风能获取一体化设计的无人机在进行动态滑翔时的获能效率。结果表明，基于气动/风能获取一体化设计的无人机获能量与获能效率最大，相比于优化前提升了979.04%，相比于最佳升阻比设计提升了10.09%。气动/风能获取一体化设计的无人机在获能段做功提升了50%以上，验证了气动/风能获取一体化设计的可行性。本文提出的设计方法为突破无人机动态滑翔获能瓶颈提供了工程支撑。

Abstract

Dynamic soaring

a special flight mode that enables unpowered long-distance flight by utilizing horizontal wind gradients

holds great potential for enhancing the range and endurance of unmanned aerial vehicles (UAVs). This paper focuses on the parametric design of fixed-wing UAVs

replacing dynamic deformation control with static shape optimization of bionic joint structures to achieve efficient energy harvesting while avoiding complex mechanisms. By calculating and analyzing aerodynamic results corresponding to different design parameters

this study proposes an aerodynamic and wind energy harvesting integrative design method for UAVs. This method employs neural networks to achieve rapid solutions for aerodynamic forces and uses the Gauss pseudospectral method to solve for the optimal wing shape

ultimately obtaining a wing configuration that meets the requirements of efficient energy harvesting. In addition

flight simulations are conducted to compare the energy harvesting efficiency of three different wing shapes during dynamic soaring: basic wing

maximum lift-to-drag ratio wing

and aerodynamic and wind energy harvesting integrative design result. Results show that the UAV with the integrated design achieves the highest energy harvest amount and efficiency

with an increase of 979.04% compared to the basic wing and 10.09% compared to the maximum lift-to-drag ratio wing. The energy gain rate (work) done by the integrated design UAV during the energy harvesting phase is improved by more than 50%

verifying the feasibility of the aerodynamic and wind energy harvesting integrative design method. This design method provides engineering support for breaking through the bottleneck of UAV dynamic soaring energy harvesting.

关键词

Keywords

references

Croxall J P, Silk J R D, Phillips R A, et al. Global circumnavigations: tracking year-round ranges of nonbreeding albatrosses[J]. Science, 2005, 307(5707): 249−250. doi: 10.1126/science.1106042

Rayleigh. The soaring of birds[J]. Nature, 1883, 27(701): 534−535. doi: 10.1038/027534a0

Wang Wei, An Weigang, Song Bifeng. Dynamic soaring parameters influence regularity analysis on UAV and soaring strategy design[J]. Drones, 2023, 7(4): 271. doi: 10.3390/drones7040271

Sachs G. Kinetic energy in dynamic soaring: inertial speed and airspeed[J]. Journal of Guidance, Control, and Dynamics, 2019, 42(8): 1812−1821. doi: 10.2514/1.g003407

Bonnin V, Toomer C, Moschetta J M, et al. Energy harvesting mechanisms for UAV flight by dynamic soaring[C]//AIAA Atmospheric Flight Mechanics (AFM) Conference, Boston, MA. AIAA, 2013: AIAA 2013-4841. doi: 10.2514/6.2013-4841

Abozeid S, Pokhrel S, Eisa S. A comprehensive assessment to the potential of reinforcement learning in dynamic soaring[C]//American Institute of Aeronautics and Astronautics, 2023

Mir I, Eisa S A, Maqsood A. Review of dynamic soaring: technical aspects, nonlinear modeling perspectives and future directions[J]. Nonlinear Dynamics, 2018, 94(4): 3117−3144. doi: 10.1007/s11071-018-4540-3

Zhao Y J. Optimal patterns of glider dynamic soaring[J]. Optimal Control Applications and Methods, 2004, 25(2): 67−89. doi: 10.1002/oca.739

Hong Haichao, Zheng Huixin, Holzapfel F, et al. Dynamic soaring in unspecified wind shear: a real-time quadratic-programming approach[C]//2019 27th Mediterranean Conference on Control and Automation (MED), Akko, Israel. IEEE, 2019. doi: 10.1109/med.2019.8798573

Hong Haichao, Piprek P, Afonso R J M, et al. Trigonometric series-based smooth flight trajectory generation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 57(1): 721−728. doi: 10.1109/TAES.2020.3008576

Hong Haichao, Benedikt G, Patrick P, et al. Smooth free-cycle dynamic soaring in unspecified shear wind via quadratic programming[J]. Chinese Journal of Aeronautics, 2022, 35(7): 19−29. doi: 10.1016/j.cja.2021.09.012

Lawrance N, Sukkarieh S. Wind energy based path planning for a small gliding unmanned aerial vehicle[C]//AIAA Guidance, Navigation, and Control Conference, Chicago, Illinois. AIAA, 2009: AIAA 2009-6112. doi: 10.2514/6.2009-6112

Wang Wei, AN Weigang, Song Bifeng. Modeling and application of dynamic soaring by unmanned aerial vehicle[J]. Applied Sciences, 2022, 12(11): 5411. doi: 10.3390/app12115411

Lawrance N R J, Sukkarieh S. A guidance and control strategy for dynamic soaring with a gliding UAV[C]//2009 IEEE International Conference on Robotics and Automation, Kobe, Japan. IEEE, 2009: 3632-3637. doi: 10.1109/ROBOT.2009.5152441

Li Zhenda, Langelaan J W. Parameterized trajectory planning for dynamic soaring[C]//AIAA Scitech 2020 Forum, Orlando, FL. AIAA, 2020: AIAA 2020-0856. doi: 10.2514/6.2020-0856

Adamski J. Development and deployment of a dynamic soaring capable UAV using reinforcement learning[D]. Embry-Riddle Aeronautical University, 2022.

Stempeck A, Hassanalian M, Abdelkefi A. Aerodynamic performance of albatross-inspired wing shape for marine unmanned air vehicles[C]//2018 Aviation Technology, Integration, and Operations Conference, Atlanta, Georgia. AIAA, 2018: AIAA 2018-3899. doi: 10.2514/6.2018-3899

Wang Wei, An Weigang, Song Bifeng. Effect of wing morphing on stability and energy harvesting in albatross dynamic soaring[J]. Chinese Journal of Aeronautics, 2024, 37(11): 317−334. doi: 10.1016/j.cja.2024.06.013

Mir I, Maqsood A, Eisa S A, et al. Optimal morphing–augmented dynamic soaring maneuvers for unmanned air vehicle capable of span and sweep morphologies[J]. Aerospace Science and Technology, 2018, 79: 17−36. doi: 10.1016/j.ast.2018.05.024

Mir I, Maqsood A, Akhtar S. Biologically inspired dynamic soaring maneuvers for an unmanned air vehicle capable of sweep morphing[J]. International Journal of Aeronautical and Space Sciences, 2018, 19(4): 1006−1016. doi: 10.1007/s42405-018-0086-3

Sachs G, Grüter B, Hong Haichao. Performance enhancement by wing sweep for high-speed dynamic soaring[J]. Aerospace, 2021, 8(8): 229. doi: 10.3390/aerospace8080229

Wang Wei, An Weigang, Song Bifeng, et al. Dynamic soaring performance of albatross-inspired morphing wing[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(24): 69−84(in Chinese).

He Xuzhao, Wei Feng, Liu Fujun, et al. Integrated design of minimal wave drag cone-derived waveriders and three-dimensional inward turning inlets[J]. Acta Aerodynamica Sinica, 2022, 40(1): 77−83(in Chinese). doi: 10.7638/kqdlxxb-2021.0375

Liu Duoneng, Hou Zhongxi, Guo Zheng, et al. Bio-inspired energy-harvesting mechanisms and patterns of dynamic soaring[J]. Bioinspiration Biomimetics, 2017, 12(1): 016014. doi: 10.1088/1748-3190/aa547c

Zhao Y J, Qi Y C. Minimum fuel powered dynamic soaring of unmanned aerial vehicles utilizing wind gradients[J]. Optimal Control Applications and Methods, 2004, 25(5): 211−233. doi: 10.1002/oca.744

Nithiyapathi C, Sreelakshmy P, Suman M. Aerodynamic characterization of an Albatross wing for bio-inspired unmanned aerial vehicle[J]. Materials Today: Proceedings, 2021, 37: 1659−1664. doi: 10.1016/j.matpr.2020.07.182

Su Jing, Sun Gang, Tao Jun. An inverse design method for three-dimensional morphing wings based on deep reinforcement learning[J]. Acta Aerodynamica Sinica, 2024, 42(10): 84−97(in Chinese) . doi: 10.7638/kqdlxxb-2024.0123

Wang Dan. Research and application of aircraft aerodynamic shape optimization design method[D]. Xi’an: Northwestern Polytechnical University, 2015: 163(in Chinese).

Chen Gong, Tang Zhigong, Deng Chen, et al. An aerodynamic configuration optimization method for reducing lateral aerodynamic nonlinearity of reentry vehicle[J]. Acta Aerodynamica Sinica, 2021, 39(2): 33−38(in Chinese). doi: 10.7638/kqdlxxb-2020.0131

Wang Chao, Wang Guidong, Bai Peng. Machine learning method for aerodynamic modeling based on flight simulation data[J]. Acta Aerodynamica Sinica, 2019, 37(3): 488−497. (in Chinese)

Han Zhonghua, Xu Chenzhou, Qiao Jianling, et al. Recent progress of efficient global aerodynamic shape optimization using surrogate-based approach[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(5): 623344(in Chinese).

Duan Zhuoyi, Wang Wei, Geng Jianzhong, et al. Challenges of high efficiency aerodynamics design for HALE solar powered UAV[J]. Acta Aerodynamica Sinica, 2017, 35(2): 156−171(in Chinese).

Sachs G. Optimal wind energy extraction for dynamic soaring[M]//Applied Mathematics in Aerospace Science and Engineering. Boston, MA: Springer US, 1994: 221-237. doi: 10.1007/978-1-4757-9259-1_10

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于深度神经网络的高阶非线性激波判别式

基于射流流动控制的飞翼布局飞行器阵风载荷减缓研究

基于风雷软件的壁面距离场的快速迭代并行求解方法

基于多支微射流的超声速射流主动控制

基于稀疏神经核的多保真度代理模型