Skip to main content

Advertisement

Log in

A Study on Measuring the Wind Field in the Air Using a Multi-rotor UAV Mounted with an Anemometer

  • Research Article
  • Published:
Boundary-Layer Meteorology Aims and scope Submit manuscript

Abstract

Compared to conventional wind field measurement methods such as wind masts or wind towers, UAV-based measurement is a relatively new approach to making wind field measurements. In the present study, a method for measuring wind field by using a six-rotor UAV mounted with an ultrasonic anemometer was established, and the feasibility thereof in wind field measurement was tested. Firstly, the influence of the UAS fuselage attitude on the accuracy of wind measurement results was analysed by means of wind tunnel testing. The results show that the average wind speed obtained by the UAV anemometry system (UAS) was slightly larger, but the average wind speed obtained by the UAS was consistent with that obtained by the Cobra anemometer after the modification of the fuselage attitude coefficient. Secondly, the wind field measurement results obtained by the UAS and the wind tower were compared, and the revised wind speed, wind direction, turbulence intensity and other parameters obtained by the UAS were found to be consistent with those of the anemometers at the same height on the wind tower. The difference was within 5%, and the longitudinal fluctuating wind power spectra obtained by the two were almost the same, being in good agreement with the Von Karman spectrum. Finally, the UAS was used to measure the wind field characteristic parameters of a certain site, which were compared with the corresponding parameters of national regulations. The feasibility of the UAS in measuring the air wind field was verified. These research results provide a reference for further research into UAV wind measurement methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data Availability

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Adkins KA, Swinford CJ, Wambolt PD, et al (2019) Development of a meteorological sensor suite for atmospheric boundary layer measurement using a small multirotor unmanned aerial system. In: Proceedings of the international society for atmospheric research using remotely-piloted aircraft world congress, Lugo, Spain, pp 15–19

  • Archer CL, Jacobson MZ (2003) Spatial and temporal distributions of US winds and wind power at 80 m derived from measurements. J Geophys Res Atmos 108(D9)

  • AS/NZS 1170. 2:2011 Structural design actions Part 2: wind actions. Australia/New Zealand standard

  • ASCE /SEI7-10 Minimum design loads for buildings and other structures. New York:American Society of Civil Engineers, 2010

  • Bruschi P, Piotto M, Dell’Agnello F et al (2016) Wind speed and direction detection by means of solid-state anemometers embedded on small quadcopters. Procedia Eng 168:802–805

    Article  Google Scholar 

  • Cho A, Kim J, Lee S et al (2008) Fully automatic taxiing, takeoff and landing of a UAV using a single-antenna GPS receiver only. Ifac Proc Volumes 41(2):4719–4724

    Article  Google Scholar 

  • Davenport AG (1961) The spectrum of horizontal gustiness near the ground in high winds. Q J R Meteorol Soc 87(372):194–211

    Article  Google Scholar 

  • Donnell GW, Feight JA, Lannan N, et al (2018) Wind characterization using onboard IMU of sUAS. In: 2018 Atmospheric flight mechanics conference, p 2986

  • EN 1991-1-4: 2005 Eurocode 1: actions on structures: part 1–4: general actions: wind action. Brussels, Belgium: European Committee For Standardization, 2005

  • GB50009-2012, 2012. Load code for the design of building structures. China Architecture & Building Press, Beijing, China

  • Giebel G, Schmidt Paulsen U, Reuder J, et al (2010) Autonomous aerial sensors for wind power meteorology. In: European wind energy conference & exhibition (EWEC 2010)

  • Mortensen HH, Arlov D, Innings F, Håkansson A (2018) A validation of commonly used CFD methods applied to rotor stator mixers using PIV measurements of fluid velocity and turbulence. Chem Eng Sci 177

  • Hollenbeck D, Oyama M, Garcia A, et al (2019) Pitch and roll effects of on-board wind measurements using sUAS. In: 2019 international conference on unmanned aircraft systems (ICUAS). IEEE, 2019, pp 1249–1254

  • Huang B, Li Z et al (2018) Near-ground impurity-free wind and wind-driven sand of photovoltaic power stations in a desert area. J Wind Eng Ind Aerodyn 179:483–502

    Article  Google Scholar 

  • He H, Lei X, Nie M et al (2018) Field measurement research of near ground wind field characteristics at landing center during typhoon ‘Haima.’ J Build Struct 39(10):29–36. https://doi.org/10.14006/j.jzjgxb.2018.10.004(inChinese)

    Article  Google Scholar 

  • Hui MCH, Larsen A, Xiang HF (2009) Wind turbulence characteristics study at the Stonecutters Bridge site: part II: wind power spectra, integral length scales and coherences. J Wind Eng Ind Aerodyn 97(1):48–59

    Article  Google Scholar 

  • Kaimal JC, Wyngaard JCJ, Izumi Y et al (1972) Spectral characteristics of surface-layer turbulence. Q J R Meteorol Soc 98(417):563–589

    Article  Google Scholar 

  • Kolmogorov AN (1941) The local structure of turbulence in incompressible viscous fluid for very large Reynolds numbers. Cr Acad Sci URSS 30:301–305

    Google Scholar 

  • Kondo J, Yamazawa H (1986) Aerodynamic roughness over an inhomogeneous ground surface. Bound-Layer Meteorol 35:331–348

    Article  Google Scholar 

  • Lawrence DA, Balsley BB (2013) High-resolution atmospheric sensing of multiple atmospheric variables using the DataHawk small airborne measurement system. J Atmos Oceanic Tech 30(10):2352–2366

    Article  Google Scholar 

  • Lim KEW, Palmer J, et al (2016) Full-scale flow measurement on a tall building with a continuous-wave Doppler Lidar anemometer. J Wind Eng Ind Aerodyn

  • Mauder M, Eggert M, Gutsmuths C, Oertel S, Wilhelm P, Voelksch I, Wanner L, Tambke J, Bogoev I (2020) Comparison of turbulence measurements by a CSAT3B sonic anemometer and a high-resolution bistatic Doppler lidar. Atmos Meas Tech 13(2)

  • Meier K, Hann R, Skaloud J et al (2022) Wind estimation with multirotor UAVs. Atmosphere 13(4):551

    Article  Google Scholar 

  • Neumann PP, Bartholmai M (2015) Real-time wind estimation on a micro unmanned aerial vehicle using its inertial measurement unit. Sens Actuators A 235:300–310

    Article  Google Scholar 

  • Palomaki RT , Rose NT, Michael VDB, et al (2017) Wind estimation in the lower atmosphere using multi-rotor aircraft. J Atmos Ocean Technol 2017, JTECH-D-16–0177.1

  • Prudden S, Fisher A , Mohamed A , et al (2016) A flying anemometer quadrotor: part 1. In: The international micro air vehicle conference and competition 2016 (IMAV 2016). 2016

  • Prudden S, Fisher A et al (2018) Measuring wind with small unmanned aircraft systems. J Wind Eng Ind Aerodyn 176(197):210

    Google Scholar 

  • Reuder J, Brisset P, Jonassen M et al (2008) SUMO: A small unmanned meteorological observer for atmospheric boundary layer research. IOP Conf Ser Earth Environ Sci 1:012014

    Article  Google Scholar 

  • Rhudy M, Larrabee T, Chao H, et al (2013) UAV attitude, heading and wind estimation using GPS/INS and an air data system. In: AIAA guidance navigation and control conference

  • Schiano F, Alonso-Mora J, Rudin K, et al (2014) Towards estimation and correction of wind effects on a quadrotor UAV. IMAV 2014: International Micro Air Vehicle Conference and Competition 2014. In: International micro air vehicle conference and competition 2014 (IMAV 2014), 2014, pp 134–141

  • Shelekhov A, Afanasiev A, Shelekhova E et al (2022) Low-altitude sensing of urban atmospheric turbulence with UAV. Drones 6(3):61

    Article  Google Scholar 

  • Shiau BS, Chen YB (2001) In situ measurement of strong wind velocity spectra and wind characteristics at Keelung coastal area of Taiwan. Atmos Res 57(3):171–185

    Article  Google Scholar 

  • Shimura T, Inoue M, Tsujimoto H et al (2018) Estimation of wind vector profile using a hexarotor unmanned aerial vehicle and its application to meteorological observation up to 1000 m above surface. J Atmos Oceanic Tech 35(8):1621–1631

    Article  Google Scholar 

  • Vinković K, Andersen T, de Vries M et al (2022) Evaluating the use of an Unmanned Aerial Vehicle (UAV)-based active AirCore system to quantify methane emissions from dairy cows. Sci Total Environ 831:154898

    Article  Google Scholar 

  • Von Karman T (1948) Progress in the statistical theory of turbulence. Proc Natl Acad Sci 34(11):530–539

    Article  Google Scholar 

  • Weiguo S (2008) Climate resources. China Meteorological Press, pp 232–282

  • Wenhai S, Zhengnong L, Jianjia W (2013) The influence of rotor rotation of hexacopter on wind measurement accuracy. Acta Aerodyn Sin (in Chinese)

  • Wu H, Zhang L, Feng H, et al (2021) Wind field measurement over complex landforms based on multi-rotor nmanned aircraft. J Exp Fluid Mech 35(02):92–103. https://doi.org/10.11729/syltlx20200055

  • Xiangting Z (2006) Structural wind engineering. China Construction Industry Press, Beijing, pp 69–71

    Google Scholar 

  • Xu R, Zhang W, Wong NH, Tong S, Wu X (2022) A novel methodology to obtain ambient temperatures using multi-rotor UAV-mounted sensors. Urban Clim 41

  • Xu YL, Zhan S (2001) Field measurements of Di Wang Tower during Typhoon York. J Wind Eng Ind Aerodyn 89(1):73–93

    Article  Google Scholar 

  • Yang Y (2019) Research on aerial wind measurement of multi-rotor UAV in situ. Nanjing University of Information Science & Technology (in Chinese)

  • Zhengnong L, Hao F, Ou P et al (2021) Research on boundary layer wind profile measurement based on six-rotor UAV anemometer. Eng Mech 38(08):121–132. https://doi.org/10.6052/j.issn.1000-4750.2020.08.0553

    Article  Google Scholar 

  • Zhengnong L, Haohui H, Yijun S (2019) The influence of rotor rotation of hexacopter on wind measurement accuracy. J Exp Fluid Mech. https://doi.org/10.11729/syltlx20190047.

Download references

Acknowledgements

This research was supported by the National Natural Science Foundation of China [Grant Numbers 51278476, 51908430, 52068019]; the Opening Fund of Key Laboratory of Desert and Desertification, Chinese Academy of Sciences [Grant Numbers KLDD-2020-007]; Science and Technology special fund of Hainan Province, China [Grant Numbers ZDYF2020207]; the Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City, China [Grant Numbers 2021CXLH0024]; and the Hainan Provincial Natural Science Foundation of China [Grant Numbers 522RC605 and 520QN231].

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhengnong Li.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Z., Pu, O., Pan, Y. et al. A Study on Measuring the Wind Field in the Air Using a Multi-rotor UAV Mounted with an Anemometer. Boundary-Layer Meteorol 188, 1–27 (2023). https://doi.org/10.1007/s10546-023-00798-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10546-023-00798-x

Keywords

Navigation