ACTA AERONAUTICAET ASTRONAUTICA SINICA >
UAV flight strategy considering icing risk under complex meteorological conditions
Received date: 2022-05-25
Revised date: 2022-06-15
Accepted date: 2022-09-09
Online published: 2022-09-13
Supported by
Open Fund of Key Laboratory of Icing and Anti/De-icing of China(IADL20200101);National Key Project of China(GJXM92579);National Science and Technology Project
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.
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 . DOI: 10.7527/S1000-6893.2022.27518
| 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. |
/
| 〈 |
|
〉 |
CJA