Evaluation of the determination of the aerodynamic characteristics of the propeller for an unmanned aerial vehicle through numerical modeling and 3D scanning


Аuthors

Ismagilov F. R., Vavilov V. E.*, Mustaev E. I.**, Urazbakhtin R. R., Kilmetov R. A.

Ufa University of Science and Technology, 32, Zaki Walidi str., Ufa, 450076, Russia

*e-mail: s2_88@mail.ru
**e-mail: edgar.mustaev@mail.ru

Abstract

The article evaluates the effectiveness of the methodology for the aerodynamic characteristics determining of a multirotor-type unmanned aerial vehicle based on a computational experiment with the creation of a geometry model using 3D scanning methods. The methodology effectiveness evaluation was performed according to the criteria of accuracy and labor intensity through validation based on publicly available data. By the computational experiment was meant the three-dimensional numerical modeling performing using computational fluid dynamics methods in the STAR CCM+ application software package. The simulation was performed by solving a system of Reynolds-averaged Navier-Stokes equations, closed by the SST k-ω turbulence model and the ideal gas model. The propulsor rotation was modeled by the method of a single rotating frame of reference. The DJI Phantom 3 9450 Plastic direct rotation propeller was chosen as the object of numerical simulation. To scan the propeller, a desktop 3D scanner Shining 3D EinScan-SP V2, based on structured light technology with an object digitization accuracy not exceeding a deviation value of 50 microns, was applied. The aerodynamic characteristics computing of a rotating propeller, as well as determining the time-stable gas dynamic parameters of flows in the environment surrounding the propulsor, were performed in a hovering mode in the range of its operating rotational speeds. A total of 14 rotational speeds were numerically studied.
By the results of validation, maximum and minimum deviations of the calculated values from the experimental ones were found for the entire studied range of rotational speeds according to the moment of resistance, equal respectively to 8.229 and 0.295%. Similarly, the maximum and minimum thrust deviations were 7.658 and 3.306%, respectively. The differences between the calculated and experimental values of the moment of resistance increases with increasing rotational speed. The differences in thrust for most of the studied rotational speeds are in the range from 3 to 5%.
To perform computations, 16 cores of 2 threads with a clock frequency of 4.8 GHz were employed. The time сconsumption for achieving convergence of the solution was of the order of 8 hours for one rotational speed under study. Convergence was being achieved in 800 iterations at an average. Thus, 112 hours of machine time were spent to obtain the aerodynamic characteristics of the propeller for 14 rotational speeds.

Keywords:

aerodynamic characteristics; numerical modeling; 3D scanning; propeller; validation; unmanned aerial vehicle

References

  1. Karimov A.Kh. Trudy MAI, 2011, no. 47. URL: https://trudymai.ru/eng/published.php?ID=26768
  2. Agaev F.G., Asadov Kh.G., Aslanova A.B. Trudy MAI, 2021, no. 117. URL: https://trudymai.ru/eng/published.php?ID=156313. DOI: 10.34759/trd-2021-117-16
  3. Karimov A.Kh. Trudy MAI, 2011, no. 47. URL: https://trudymai.ru/eng/published.php?ID=26767
  4. Karimov A.Kh. Trudy MAI, 2011, no. 47. URL: https://trudymai.ru/eng/published.php?ID=26769
  5. Deters R.W., Kleinke S., Selig M.S. Static testing of propulsion elements for small multirotor unmanned aerial vehicles, 35th AIAA Applied Aerodynamics Conference, 2017, AIAA 2017-3743. DOI: 10.2514/6.2017-3743
  6. Hage C., Sophy T., Aglzim E.-H. Investigating UAV Propellers Performances Near Moving Obstacles: CFD Study, Thrust Control, and Battery Energy Management, IEEE Open Journal of Vehicular Technology, 2023, vol. 4, pp. 590-609. DOI: 10.1109/OJVT.2023.3309103
  7. Afari S.O., Mankbadi R.R., Golubev V.V. Towards High-fidelity Analysis of Noise Radiation and Control of Propeller-driven UAV, 25th AIAA/CEAS Aeroacoustics Conference, AIAA 2019-2632, 2019. DOI: 10.2514/6.2019-2632
  8. Kim D.H., Park C.H., Moon Y.J. Aerodynamic Analyses on the Steady and Unsteady Loading-Noise Sources of Drone Propellers, International Journal of Aeronautical and Space Sciences, 2019, vol. 20, pp. 611-619. DOI: 10.1007/s42405-019-00176-3
  9. Thai A., Grace S.M. Prediction of small quadrotor blade induced noise, 25th AIAA/CEAS Aeroacoustics Conference, 2019, AIAA 2019-2684. DOI: 10.2514/6.2019-2684
  10. Wilkins R., Bouferrouk A. Numerical framework for aerodynamic and aeroacoustics of bio-inspired UAV blades, The Aerospace Europe Conference 2023 – 10ᵀᴴ EUCASS – 9ᵀᴴ CEAS, 2023.
  11. Bibeau V., Barbeau L., Boffito D.C., Blais B. Artificial neural network to predict the power number of agitated tanks fed by CFD simulations, The Canadian Journal of Chemical Engineering, 2023, vol. 101, iss. 10, pp. 5992-6002. DOI: 10.1002/cjce.24870
  12. Wilhelm D. Rotating Flow Simulations with OpenFOAM, International Journal of Aeronautical Science & Aerospace Research (IJASAR), 2015.
  13. Mankbadi R.R., Afari S.O., Golubev V.V. Simulations of Broadband Noise of a Small UAV Propeller, AIAA Scitech 2020 Forum, 2020, AIAA 2020-1493. DOI: 10.2514/6.2020-1493
  14. Garofano-Soldado A., Heredia G., Ollero A. Aerodynamic interactions of non-planar rotor pairs and model derivation in ground approach, Aerospace Science and Technology, 2023, vol. 142, part B. DOI: 10.1016/j.ast.2023.108672
  15. Paz C., Suárez E., Gil C., Vence J. Assessment of the methodology for the CFD simulation of the flight of a quadcopter UAV, Journal of Wind Engineering and Industrial Aerodynamics, 2021, vol. 218. DOI: 10.1016/j.jweia.2021.104776
  16. Paz C., Suárez E., Gil C., Vence J. CFD analysis of the aerodynamic effects on the stability of the flight of a quadcopter UAV in the proximity of walls and ground, Journal of Wind Engineering and Industrial Aerodynamics, 2020, vol. 206. DOI: 10.1016/j.jweia.2020.104378
  17. Nikol'skii A.A. Trudy MAI, 2024, no. 134. URL: https://trudymai.ru/eng/published.php?ID=178466
  18. Tarasov A.L. Trudy MAI, 2023, no. 131. URL: https://trudymai.ru/eng/published.php?ID=175919. DOI: 10.34759/trd-2023-131-17
  19. Ignatkin Yu.M., Konstantinov S.G. Trudy MAI, 2012, no. 57. URL: https://trudymai.ru/eng/published.php?ID=30875
  20. Vershkov V.A., Kritskii B.S., Makhnev M.S. et al. Trudy MAI, 2016, no. 89. URL: https://trudymai.ru/eng/published.php?ID=72704
  21. Ignatkin Yu.M., Makeev P.V., Shomov A.I. Trudy MAI, 2016, no. 87. URL: https://trudymai.ru/eng/published.php?ID=65636
  22. Ignatkin Yu.M., Konstantinov S.G. Trudy MAI, 2012, no. 57. URL: https://trudymai.ru/eng/published.php?ID=30874
  23. Simcenter STAR-CCM+ Documentation, Version 2306. Simcenter Digital Industries Software, 2023. DOI: 10.13140/RG.2.2.20194.68808
  24. Wilcox D.C. Turbulence Modeling for CFD. California: DCW Industries, 2006, 522 p.
  25. ANSYS CFX-Solver Theory Guide, Release 23, ANSYS Inc, USA, 2023.
  26. Garbaruk A.B. Techeniya vyazkoi zhidkosti i modeli turbulentnosti: metody rascheta turbulentnykh techenii (Viscous fluid flows and turbulence models: methods for calculating turbulent flows), Saint-Petersburg, Sankt-Peterburgskii gosudarstvennyi politekhnicheskii universitet, 2007, 127 p.


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