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): 627518-627518.

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

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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

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Fig. 17

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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

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