Model migration for CFD and verification of a six-rotor UAV downwash

Shenghui Yang, Qing Tang, Yongjun Zheng, Xingxing Liu, Jian Chen, Xiaolong Li

Abstract


Currently, Computational Fluid Dynamics (CFD) has been used to investigate agricultural UAV downwash. However, the validations of CFD models are difficult to deal with. Current verification methods are to use either water-sensitive papers or wind-speed arrays, which could get wind distribution or speed only. In this study, model migration was used to develop and verify downwash CFD models. The basic idea is to try to use the results of a scaled-down drone to represent that of a real-used UAV. The CFD models of both a real-used six-rotor UAV, JF01-10, and a 1:10 scaled-down small drone were developed by ANSYS. Then, the scaled-down drone was utilized to conduct trials by particle image velocimetry (PIV), so that not only distribution and speed but also flowing direction of downwash could be obtained. Results indicated the relative error between the PIV tests and the CFD models of the small UAV was less than 12%, while that between the tests and the CFD models of JF01-10 was less than 34%. It could be indicated that model migration could reflect multiple downwash characteristics but should be optimized in some complex details. This study was a preliminary but fundamental attempt to investigate CFD modelling and validation of agricultural UAVs and provided a novel thinking of downwash verification.
Keywords: multi-rotor UAV, downwash, numerical simulation, computational fluid dynamics, verification, model migration
DOI: 10.25165/j.ijabe.20201304.5569

Citation: Yang S H, Tang Q, Zheng Y J, Liu X X, Chen J, Li X L. Model migration for CFD and verification of a six-rotor UAV downwash. Int J Agric & Biol Eng, 2020; 13(4): 10–18.

Keywords


multi-rotor UAV, downwash, numerical simulation, computational fluid dynamics, verification, model migration

Full Text:

PDF

References


Lan Y B, Chen S D, Fritz B K. Current status and future trends of precision agricultural aviation technologies. Int J Agric & Biol Eng, 2017; 10(3): 1–17.

Li J Y, Lan Y B, Shi Y Y. Research progress on airflow characteristics and field pesticide application system of rotary-wing UAV. Transactions of the CSAE, 2018; 34(12): 104–118. (in Chinese)

Wang G B, Lan Y B, Qi H X, Chen P C, Hewitt A, Han Y X. Field evaluation of an unmanned aerial vehicle (UAV) sprayer: effect of spray volume on deposition and the control of pests and disease in wheat. Pest Management Science, 2019; 75(6): 1546–1555.

Liao J, Zang Y, Luo X W, Zhou Z Y, Lan Y B, Zang Y, et al. Optimization of variables for maximizing efficacy and efficiency in aerial spray application to cotton using unmanned aerial systems. Int J Agric & Biol Eng, 2019; 12(2): 10–17.

Wang J, Lan Y B, Zhang H H, Zhang Y L, Wen S, Yao W X, et al. Drift and deposition of pesticide applied by UAV on pineapple plants under different meteorological conditions. Int J Agric & Biol Eng, 2018; 11(6): 5–12.

Meng Y H, Lan Y B, Mei G Y, Guo Y W, Song J L, Wang Z G. Effect of aerial spray adjuvant applying on the efficiency of small unmanned aerial vehicle on wheat aphids control. Int J Agric & Biol Eng, 2018; 11(5): 46–53.

Tang Y, Hou C J, Luo S M, Lin J T, Yang Z, Huang W F. Effects of operation height and tree shape on droplet deposition in citrus trees using an unmanned aerial vehicle. Computers and Electronics in Agriculture, 2018; 148: 1–7.

Wang C L, He X K, Wang X N, Wang Z C, Wang S L, Li L L, et al. Testing method and distribution characteristics of spatial pesticide spraying deposition quality balance for unmanned aerial vehicle. Int J Agric & Biol Eng, 2018; 11(2): 18–26.

Zheng Y J, Yang S H, Zhao C J, Chen L P, Lan Y B, Tan Y. Modelling operation parameters of UAV on spray effects at different growth stages of corns. Int J Agric & Biol Eng, 2017; 10(3): 57–66.

Ay F, İnce G. Application of pesticide using unmanned aerial vehicle. In: 23nd Signal Processing and Communications Applications Conference (SIU), IEEE, 2015; pp.1268-1271.

Salyani M, Fox R D. Performance of image analysis for assessment of simulated spray droplet distribution. Transactions of the ASAE, 1994; 37(4): 1083–1089.

Zhang B, Tang Q, Chen L P, Xu M. Numerical simulation of wake vortices of crop spraying aircraft close to the ground. Biosystems Engineering, 2016; 145: 52–64.

Yang F B, Xue X Y, Cai C, Sun Z, Zhou Q Q. Numerical Simulation and analysis on spray drift movement of multirotor plant protection unmanned aerial vehicle. Energies, 2018; 11(9): 1–20.

Wang L, Chen D, Zhang M C, Wang Y, Yao Z, Wang S M. CFD Simulation of low-attitude droplets deposition characteristics for UAV based on multi-feature fusion. IFAC-PapersOnLine, 2018; 51(17): 648–653.

Li J Y, Shi Y Y, Lan Y B, Guo S. Vertical distribution and vortex structure of rotor wind field under the influence of rice canopy. Computers and Electronics in Agriculture, 2019; 159: 140–146.

Tan F, Lian Q, Liu C L, Jin B K. Measurement of downwash velocity generated by rotors of agriculture drones. Inmateh-Agricultural Engineering, 2018; 55(2): 141–150.

Zhang H, Qi L J, Wu Y L, Liu W W, Cheng Z Z, MUSIU E. Spatial-temporal distribution of down-wash airflow for multi-rotor plant protection UAV based on porous model. Transactions of the CSAE, 2018, 50(2): 112–122. (in Chinese)

Chen S D, Lan Y B, Brandley K F, Li J Y, Liu A M, Mao Y D. Effect of wind field below rotor on distribution of aerial spraying droplet deposition by using multi-rotor UAV. Transactions of the CSAM, 2017; 48(8): 105–113. (in Chinese)

Tang Q, Zhang R R, Chen L P, Xu M, Yi T C, Zhang B. Droplets movement and deposition of an eight-rotor agricultural UAV in downwash flow field. Int J Agric & Biol Eng, 2017; 10(3): 47–56.

Wang H L, Li Q D, Ren Z, Zhao Q L, Dong X W, Liu F. Modeling of hypersonic vehicle via model migration method. Journal of Beijing University of Aeronautics and Astronautics, 2016; 42(12): 2640–2647. (in Chinese)

Doty M J, Brooks T F, Burley C L, Bahr C J, Pope D. Jet noise shielding provided by a hybrid wing body aircraft. International Journal of Aeroacoustics, 2018; 17(1-2): 135–158.

Raffel M C, Willert C E, Kompenhans J. Particle image velocimetry: A practical guide. 2nd Edition, Springer Berlin Heidelberg, 2007; 448p.

Zheng Y J, Yang S H, Liu X X, Wang J, Norton T, Chen J, et al. The computational fluid dynamic modeling of downwash flow field for a six-rotor UAV. Frontiers of Agricultural Science and Engineering, 2018; 5(2): 159–167.

Zhang Y N, Ke C F, Gao Y N, Liu S Y, Pan Y Y, Zhou N, et al. Syngas production from microwave-assisted air gasification of biomass: Part 2 model validation. Renewable Energy, 2019; 140: 625–632.

Khalilzadeh A, Ge H, Ng H D. Effect of turbulence modeling schemes on wind-driven rain deposition on a mid-rise building: CFD modeling and validation. Journal of Wind Engineering and Industrial Aerodynamics, 2019; 184: 362–377.

Thumthae C, Chitsomboon T. Optimal angle of attack for untwisted blade wind turbine. Renewable Energy, 2009; 34(5): 1279–1284.

Cummings R M, Forsythe J R, Morton S A, Squires K D. Computational challenges in high angle of attack flow prediction. Progress in Aerospace Sciences, 2003; 39(5): 369–384.

Tyson W C, Roy C J. A higher-order error estimation framework for finite-volume CFD. Journal of Computational Physics, 2019; 394: 632–657.




Copyright (c) 2020 International Journal of Agricultural and Biological Engineering

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

2023-2026 Copyright IJABE Editing and Publishing Office