复杂气象条件下考虑结冰风险的无人机飞行策略

doi:10.7527/S1000-6893.2022.27518

Abstract

Abstract:

UAVs are vulnerable to icing under complex meteorological conditions， thus threatening flight safety. To solve this problem， this paper proposes a UAV trajectory planning method considering the icing risk. Firstly， an icing meteorological prediction model based on the mesoscale Weather Research and Forecasting （WRF） model is constructed. By icing meteorological prediction with the best combination of parameterization schemes， the spatial distribution and temporal evolution of temperature， pressure and Liquid Water Content （LWC） in the Ledong area of Hainan from May to July 2021 are obtained. Secondly， a rapid prediction method for water droplet collection based on the surrogate model is established. The Optimal Latin Hypercube Sampling （OLHS） method is employed to sample the continuous maximum icing conditions in Appendix C of Federal Aviation Regulations （FAR） Part 25， and the droplet impact characteristics are numerically calculated for 40 sampling points to obtain the distribution of water droplet collection at each sampling point. Based on the Proper Orthogonal Decomposition （POD） reduced-order model and the Kriging interpolation method， a surrogate model between the water droplet collection and meteorological parameters， such as temperature， pressure， LWC and droplets Median Volumetric Diameter （MVD）， is established.On the basis of the established surrogate model， the spatial distribution and temporal evolution of water droplet collection in the target area are obtained. Finally， taking the threshold of water droplet collection at various icing intensity as the icing safety constraint， we use the Particle Swarm Optimization （PSO）-based icing tolerance trajectory planning method to optimize the flight strategy of the UAV considering the icing risk to overcome the defects of the existing icing prediction algorithms lack of icing risk quantification. The results show that the icing meteorological parameters predicted by the WRF model， such as temperature， pressure， and LWC， match well with the observations. Based on the POD model and Kriging method， the constructed surrogate model between meteorological parameters and water droplet collection can quickly and accurately predict the spatial distribution and temporal evolution of water droplet collection in the target area. The PSO-based icing tolerance trajectory planning method is competent to plan the optimal trajectory of the UAV under different icing safety constraints.

Key words: UAV, icing meteorology, Weather Research and Forecasting (WRF), surrogate model, water droplet collection, rapid prediction, trajectory planning, particle swarm optimization

CLC Number:

V211.3

Qilei GUO, Weimin SANG, Junjie NIU, Ye YUAN. UAV flight strategy considering icing risk under complex meteorological conditions[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(1): 627518.

Figures/Tables 24

Table 1

Fig. 1

Algorithm 1　Ice tolerance route planning based on PSO

结冰容限航迹规划算法

1. 设置必要算法参数，并随机产生一个初始种群

2. 计算每个粒子在初始时刻总的违反约束度TD _m，超过容限则重新生成粒子

3. 迭代计算初始时刻种群的全局最优位置 $P g (0)$ 与每个粒子的个体最优位置 $P i (0)$

4. while k ≤ k_max do

5. for i=1：K do

6. 更新PSO算法中的粒子i的速度 $V i k$ 与位置 $p i k$

7. 根据式（20）计算粒子i的约束

8. if $T D m = 0$ do

9. 粒子i视为暂时可行粒子

10. else

11. 粒子i视为暂时不可行粒子

12. end if

13. 如式（18）更新粒子i的目标函数值 $J c o s t p i k$

14. 利用解的可行性原则更新粒子i的个体最优位置 $P i k$

15. end for

16. 更新种群的全局最优位置 $P g k$

17. for i=1：K do

18. 更新PSO算法中粒子的控制参数：w、c₁、c₂

19. end for

20. 迭代次数自加，即k = k + 1

21. end while

22. 输出种群的全局最优位置 $P g k m a x$ 所代表的最优航迹

Fig. 2

Fig. 3

Table 2

Fig. 4

Fig. 5

Table 3

Fig. 6

Fig. 7

Table 4

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

Table 5

Fig. 13

Fig. 14

Fig. 15

Table 6

Fig. 16

Fig. 17

References 41

1	HANN R. UAV icing： Comparison of LEWICE and FENSAP-ICE for ice accretion and performance degradation［C］∥ 2018 Atmospheric and Space Environments Conference. Reston： AIAA， 2018.
2	李琳佩. 基于无人机的高能效通信策略研究［D］. 北京：北京邮电大学， 2021.
	LI L P. Research on energy efficient communication strategies based on unmanned aerial vehicles［D］. Beijing： Beijing University of Posts and Telecommunications， 2021 （in Chinese）.
3	CAO Y H， TAN W Y， WU Z L. Aircraft icing： An ongoing threat to aviation safety［J］. Aerospace Science and Technology， 2018， 75： 353-385.
4	WU Q， XU H J， PEI B B， et al. Conceptual design and preliminary experiment of icing risk management and protection system［J］. Chinese Journal of Aeronautics， 2022， 35（6）： 101-115.
5	LI H R， ZHANG Y F， CHEN H X. Optimization design of airfoils under atmospheric icing conditions for UAV［J］. Chinese Journal of Aeronautics， 2022， 35（4）： 118-133.
6	Federal Aviation Administration. Federal aviation regulations Part 25： Airworthiness standards transport category airplanes ［S］. Washington， D.C.： Federal Aviation Administration， 2022.
7	刘旭光. 数值预报产品在航空气象预报中的应用［J］. 四川气象， 2001， 21（4）： 18-22.
	LIU X G. Application of numerical forecast products in aviation weather forecast［J］. Journal of Sichuan Meteorology， 2001， 21（4）： 18-22 （in Chinese）.
8	POLITOVICH M， SAND W. A proposed icing severity index based upon meteorology［C］∥International Conference on Aviation Weather Systems， 1991： 157-162.
9	THOMPSON G， BRUINTJES R T， BROWN B G， et al. Intercomparison of in-flight icing algorithms. Part I： WISP94 real-time icing prediction and evaluation program［J］. Weather and Forecasting， 1997， 12（4）： 878-889.
10	FORBES G S， HU Y， BROWN B G， et al. Examination of conditions in the proximity of pilot reports of aircraft icing during STORM-FEST［C］∥International Conference on Aviation Weather Systems， 1993： 282-286.
11	MCDONOUGH F， BERNSTEIN B， POLITOVICH M， et al. The forecast icing potential algorithm［C］∥ 42nd AIAA Aerospace Sciences Meeting and Exhibit. Reston： AIAA， 2004.
12	BERNSTEIN B C， MCDONOUGH F， POLITOVICH M K， et al. Current icing potential： Algorithm description and comparison with aircraft observations［J］. Journal of Applied Meteorology， 2005， 44（7）： 969-986.
13	BERNSTEIN B C， WOLFF C A， MCDONOUGH F. An inferred climatology of icing conditions aloft， including supercooled large drops. Part I： Canada and the continental United States［J］. Journal of Applied Meteorology and Climatology， 2007， 46（11）： 1857-1878.
14	王洪芳，刘健文，纪飞，等. 飞机积冰业务预报技术研究［J］. 气象科技， 2003， 31（3）： 140-146.
	WANG H F， LIU J W， JI F， et al. Operational forecast technique of aircraft icing［J］. Meteorological Science and Technology， 2003， 31（3）： 140-146 （in Chinese）.
15	李佰平，戴建华，孙敏，等. 一种改进的飞机自然结冰潜势算法研究［J］. 气象， 2018， 44（11）： 1377-1390.
	LI B P， DAI J H， SUN M， et al. An improved aircraft natural icing potential algorithm［J］. Meteorological Monthly， 2018， 44（11）： 1377-1390 （in Chinese）.
16	王义凡. 考虑历史和未来气候变化的台风风场多尺度模拟［D］. 杭州：浙江大学， 2020.
	WANG Y F. Multi-scale simulation of typhoon wind field considering historical and future climate changes［D］. Hangzhou： Zhejiang University， 2020 （in Chinese）.
17	FERNÁNDEZ-GONZÁLEZ S， SÁNCHEZ J L， GASCÓN E， et al. Weather features associated with aircraft icing conditions： A case study［J］. The Scientific World Journal， 2014， 2014： 279063.
18	MERINO A， GARCÍA-ORTEGA E， FERNÁNDEZ-GONZÁLEZ S， et al. Aircraft icing： In-cloud measurements and sensitivity to physical parameterizations［J］. Geophysical Research Letters， 2019， 46（20）： 11559-11567.
19	STITH J L， DYE J E， BANSEMER A， et al. Microphysical observations of tropical clouds［J］. Journal of Applied Meteorology， 2002， 41（2）： 97-117.
20	LAWSON P， GURGANUS C， WOODS S， et al. Aircraft observations of cumulus microphysics ranging from the tropics to midlatitudes： Implications for a “new” secondary ice process［J］. Journal of the Atmospheric Sciences， 2017， 74（9）： 2899-2920.
21	柯元惠，马明明，郑艳，等. 海南岛雷暴大风天气形势和环境参数特征分析［J］. 暴雨灾害， 2022， 41（1）： 86-93.
	KE Y H， MA M M， ZHENG Y， et al. Analysis of synoptic situation and environmental parameters of thunderstorm gales in Hainan［J］. Torrential Rain and Disasters， 2022， 41（1）： 86-93 （in Chinese）.
22	HONG S Y， NOH Y， DUDHIA J. A new vertical diffusion package with an explicit treatment of entrainment processes［J］. Monthly Weather Review， 2006， 134（9）： 2318-2341.
23	KAIN J S. The Kain-Fritsch convective parameterization： An update［J］. Journal of Applied Meteorology， 2004， 43（1）： 170-181.
24	MLAWER E J， TAUBMAN S J， BROWN P D， et al. Radiative transfer for inhomogeneous atmospheres： RRTM， a validated correlated-k model for the longwave［J］. Journal of Geophysical Research： Atmospheres， 1997， 102（D14）： 16663-16682.
25	DUDHIA J. Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model［J］. Journal of the Atmospheric Sciences， 1989， 46（20）： 3077-3107.
26	FAIRALL C W， BRADLEY E F， HARE J E， et al. Bulk parameterization of air-sea fluxes： Updates and verification for the COARE algorithm［J］. Journal of Climate， 2003， 16（4）： 571-591.
27	CHEN F， DUDHIA J. Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I： Model implementation and sensitivity［J］. Monthly Weather Review， 2001， 129（4）： 569-585.
28	HONG S Y， LIM J O J. The WRF single-moment 6-class microphysics scheme （WSM6）［J］. Journal of the Korean Meteorological Society， 2006， 42： 129–151.
29	CHEN S H， SUN W Y. A one-dimensional time dependent cloud model［J］. Journal of the Meteorological Society of Japan Ser II， 2002， 80（1）： 99-118.
30	THOMPSON G， FIELD P R， RASMUSSEN R M， et al. Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II： Implementation of a new snow parameterization［J］. Monthly Weather Review， 2008， 136（12）： 5095-5115.
31	MORRISON H， THOMPSON G， TATARSKII V. Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line： Comparison of one- and two-moment schemes［J］. Monthly Weather Review， 2009， 137（3）： 991-1007.
32	刘藤，李栋，黄冉冉，等. 基于降阶模型的翼型结冰冰形预测方法［J］. 北京航空航天大学学报， 2019， 45（5）： 1033-1041.
	LIU T， LI D， HUANG R R， et al. Ice shape prediction method of aero-icing based on reduced order model［J］. Journal of Beijing University of Aeronautics and Astronautics， 2019， 45（5）： 1033-1041 （in Chinese）.
33	SIROVICH L. Turbulence and the dynamics of coherent structures. Ⅰ-Coherent structures［J］. Quarterly of Applied Mathematics， 1987， 45（3）： 561-571.
34	SIROVICH L. Turbulence and the dynamics of coherent structures. Ⅱ-Symmetries and transformations［J］. Quarterly of Applied Mathematics， 1987， 45（3）： 573-582.
35	SIROVICH L. Turbulence and the dynamics of coherent structures. Ⅲ-Dynamics and scaling［J］. Quarterly of Applied Mathematics， 1987， 45（3）： 583-590.
36	邱亚松，白俊强，华俊. 基于本征正交分解和代理模型的流场预测方法［J］. 航空学报， 2013， 34（6）： 1249-1260.
	QIU Y S， BAI J Q， HUA J. Flow field estimation method based on proper orthogonal decomposition and surrogate model［J］. Acta Aeronautica et Astronautica Sinica， 2013， 34（6）： 1249-1260 （in Chinese）.
37	唐必伟. 粒子群算法的改进及其在无人机任务规划中的应用［D］. 西安：西北工业大学， 2017.
	TANG B W. An improved particle swarm optimization method and its application on mission planning of unmanned aerial vehicle［D］. Xi’an： Northwestern Polytechnical University， 2017 （in Chinese）.
38	KENNEDY J， EBERHART R. Particle swarm optimization［C］∥Proceedings of ICNN'95-International Conference on Neural Networks. Piscataway： IEEE Press， 1995： 1942-1948.
39	ZHANG Y， GONG D W， ZHANG J H. Robot path planning in uncertain environment using multi-objective particle swarm optimization［J］. Neurocomputing， 2013， 103： 172-185.
40	TANG B W， XIANG K， PANG M Y， et al. Multi-robot path planning using an improved self-adaptive particle swarm optimization［J］. International Journal of Advanced Robotic Systems， 2020， 17（5）： 172988142093615.
41	JONES S L. Simulation of meteorological fields for icing applications at the summit of mount Washington［D］. Lincoln： University of Nebraska-Lincoln， 2014.

微物理过程	方案特征
WSM6方案	考虑了雨、水汽、云水、雪、云冰、霰等6种水成物的处理，允许混合相变过程和过冷水的存在，分开处理冰与水的饱和调整过程，适合格距在云尺度和中尺度间的格点研究。
Purdue-Lin方案	一维云模型，可对水汽、云水、雨、云冰、雪、霰等6种水成物处理，考虑了夹带、云微物理、压力扰动、横向涡流扩散和垂直涡流扩散的影响，适合理论研究及高分辨率的实测数据研究。
Thompson方案	可预测云水、云冰、雨、雪和霰等5种水凝物质量浓度及云冰和雨的数量浓度。该方案最初针对航空结冰问题而改进设定，采用了相对更为复杂的混合相过程公式，尤其是对雪类转换机制的定义使其对云中LWC预测表现出色。
Morrison方案	基于完整的二矩（即质量混合比和数量浓度）方案，可预测与Thompson方案相同的5种水凝物的质量浓度，还可获得云冰、雪、雨和霰的数量浓度。对上述4种水凝物的质量混合比和数量浓度的预测可以更可靠地描述其尺寸分布。

预测参数	误差参数	Purdue-Lin	WSM6	Thompson	Morrison
压力	MAE/hPa	0.801	0.797	0.801	0.804
	RMAE/%	0.079 5	0.079 1	0.079 5	0.079 8
	r	0.892	0.893	0.891	0.891
温度	MAE/K	0.980	0.963	1.005	0.981
	RMAE/%	0.325	0.319	0.333	0.325
	r	0.914	0.917	0.916	0.918

工况	MVD/μm	LWC/（g·m^-³）	T/K	H/m	V_∞/（m·s^-1）	AOA/（°）
Case 1	15.0	0.445 5	258	4 629	87.2	4
Case 2	20.8	0.239 8	255	2 643	75.7	4
Case 3	27.1	0.265 8	263	126	53.9	4
Case 4	33.6	0.243 5	269	3 934	94.9	4
Case 5	40.0	0.100 0	263	2 699	67.9	4

工况	T/K	H/m	MVD/μm	LWC/ （g·m^-³）	V/ （m∙s^-1）
Case 01	258	3 000	15	0.445 5	50
Case 02	268	1 200	15	0.697 0	55
Case 03	243.15	6 700	15	0.200 0	60
Case 04	268.15	1 200	25	0.402 7	65
Case 05	258	3 000	25	0.227 7	70
Case 06	243.15	6 700	25	0.099 5	75
Case 07	268	1 200	35	0.206 7	80
Case 08	258	3 000	35	0.117 3	85
Case 09	243.15	6 700	35	0.049 8	90

结冰等级	单位时间结冰厚度/（mm∙min^-1）	水滴收集质量/（kg∙s^-1∙m^-2）
微量结冰	<0.6	<0.009 2
轻度结冰	0.6~1.0	0.009 2~0.015 3
中度结冰	1.1~2.0	0.016 8~0.030 6
严重结冰	>2.0	>0.030 6

UAV flight strategy considering icing risk under complex meteorological conditions

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 24

References 41

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Xiangyu YU, Wen LI, Jie YAN, Shizhe LIANG. Simulation research on thermal management system of fuel cell for liquid hydrogen powered UAV [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(9): 630964-630964.
[2]	Pengqian YANG, Yutong CHEN, Junhui LIU, Jiehao YANG, Jiayuan SHAN, Shijun SUN. Aerodynamic and operational characteristics analysis for tandem wing cargo UAV at high angle of attack [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(9): 131056-131056.
[3]	Rongzu LI, Li LIU, Dun YANG. Optimal design of hydrogen-powered UAV based on multi-source domain fusion surrogate model [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(9): 630979-630979.
[4]	Bingbing XU, Kai HAN, Richang DONG, Wenbin GONG, Qianyi REN. A high-speed laser backbone node deployment approach for next-generation GNSS [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(9): 331124-331124.
[5]	Kaifang WAN, Zhilin WU, Yunhui WU, Haozhi QIANG, Yibo WU, Bo LI. Cooperative location of multiple UAVs with deep reinforcement learning in GPS-denied environment [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(8): 331024-331024.
[6]	Lingfeng JIANG, Xinkai LI, Hai ZHANG, Hanwei LI, Hongli ZHANG. Mapless navigation of UAVs in dynamic environments based on an improved TD3 algorithm [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(8): 331035-331035.
[7]	Yunwen FENG, Da TENG, Cheng LU, Rui WANG, Junyu CHEN. Integrated mechanism and generative adversarial surrogate modeling for aircraft systems reliability evaluation [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(7): 230948-230948.
[8]	Zhengyu SONG. Promoting continuous innovation in space transportation systems: Control technologies and challenges [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(6): 531446-531446.
[9]	Jinwu XIANG, Kai MA, Zi KAN, Daochun LI, Kexin ZHENG, Hanxuan CHEN. Review of key technologies for hydrogen powered unmanned aerial vehicles [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(5): 531603-531603.
[10]	Kaishang LI, Haoqi FAN, Runzi WANG, Xiancheng ZHANG, Shantung TU. Life design method of high-temperature equipment based on dual-scale damage theory [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(5): 531706-531706.
[11]	Qishuai DING, Bangjun LEI, Zhengping WU. A lightweight single object tracking algorithm for UAVs based on Siamese network [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(4): 330925-330925.
[12]	Nuo MA, Shechun WEI, Junhui MENG, Qingyang LIU, Yusheng LEI. Flow field characteristics and dynamics of internal supply chamber separating from UAV considering effect of deceleration parachutes [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(3): 130755-130755.
[13]	Yiquan WU, Kang TONG. Research advances on deep learning-based small object detection in UAV aerial images [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(3): 30848-030848.
[14]	Xunliang YAN, Yuxuan YANG, Jiawei SHI, Peichen WANG. Rapid ascent-phase trajectory planning for near-optimal fuel consumption of RBCC vehicle [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(18): 231876-231876.
[15]	Chen WANG, Caisheng WEI, Zeyang YIN, Kai JIN, Xingchen LI. Collaborative planning of multi-UAV trajectories and communication strategies considering channel resource constraints [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(18): 331837-331837.