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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2023, Vol. 44 ›› Issue (1): 627518-627518.doi: 10.7527/S1000-6893.2022.27518

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UAV flight strategy considering icing risk under complex meteorological conditions

Qilei GUO1,2, Weimin SANG1,3(), Junjie NIU1, Ye YUAN4   

  1. 1.School of Aeronautics,Northwestern Polytechnical University,Xi’an 710072,China
    2.Engineering Techniques Training Center,Civil Aviation Flight University of China,Guanghan 618307,China
    3.Key Laboratory of Icing and Anti/De-icing,China Aerodynamics Research and Development Center,Mianyang 621000,China
    4.Shanghai Aircraft Design and Research Institute,Commercial Aircraft Corporation of China Ltd,Shanghai  201210,China
  • Received:2022-05-25 Revised:2022-06-15 Accepted:2022-09-09 Online:2023-01-15 Published:2022-09-13
  • Contact: Weimin SANG E-mail:aeroicing@sina.cn
  • 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

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

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