Skip to main content
Log in

A Biomimetic Rotor-configuration Design for Optimal Aerodynamic Performance in Quadrotor Drone

  • Published:
Journal of Bionic Engineering Aims and scope Submit manuscript

Abstract

Motivated by optimal combination of paired wings configuration and troke-plane inclination in biological flapping flights that can achieve high aerodynamic performance, we propose a biomimetic rotor-configuration design to explore optimal aerodynamic performance in multirotor drones. While aerodynamic interactions among propellers in multirotor Unmanned Aerial Vehicles (UAVs) play a crucial role in lift force production and Figure of Merit (FM) efficiency, the rotor-configuration effect remains poorly understood. Here we address a Computational Fluid Dynamics (CFD)-based study on optimal aerodynamic performance of the rotor-configuration in hovering quadrotor drones with a specific focus on the aerodynamic effects of tip distance, height difference and tilt angle of propellers. Our results indicate that the tip distance-induced interactions can most alter lift force production and hence lead to remarked improvement in FM, and the height difference also plays a key role in improving aerodynamic performance, while the tilt angle effect is less important. Furthermore, we carried out an extensive analysis to explore the optimal aerodynamic performance of the rotor-configuration over a broad parameter space, by combining the CFD-based simulations and a novel surrogate model. We find that a rotor-configuration with a large tip distance and some height difference with zero tilt angle is capable of optimizing both lift force production and FM, which could offer a novel optimal design as well as maneuver strategy for multirotor UAVs.

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.

Similar content being viewed by others

References

  1. Cheng B, Deng X Y, Hedrick T L. The mechanics and control of pitching manoeuvres in a freely flying hawkmoth (Manduca sexta). The Journal of Experimental Biology, 2011, 214, 4092–106.

    Article  Google Scholar 

  2. Liu H, Ravi S, Kolomenskiy D, Tanaka H. Biomechanics and biomimetics in insect-inspired flight systems. Philosophical Transactions of the Royal Society B: Biological Sciences, 2016, 371, 20150390.

    Article  Google Scholar 

  3. Liu H, Kolomenskiy D, Nakata T, Li G. Unsteady bio-fluid dynamics in flying and swimming. Acta Mechanica Sinica, 2016, 33, 663–684.

    Article  Google Scholar 

  4. Liu H, Nakata T, Gao N, Maeda M, Aono H, Shyy W. Micro air vehicle-motivated computational biomechanics in bio-flights: Aerodynamics, flight dynamics and maneuvering stability. Acta Mechanica Sinica, 2010, 26, 863–879.

    Article  MathSciNet  MATH  Google Scholar 

  5. Tobalske B W, Warrick D R, Clark C J, Powers D R, Hedrick T L, Hyder G A, Biewener A A. Three-dimensional kinematics of hummingbird flight. Journal of Experimental Biology, 2007, 210, 2368–2382.

    Article  Google Scholar 

  6. Chen D, Kolomenskiy D, Nakata T, Liu H. Forewings match the formation of leading-edge vortices and dominate aerodynamic force production in revolving insect wings. Bioinspiration & Biomimetics, 2018, 13, 016009.

    Article  Google Scholar 

  7. Maeda M, Nakata T, Kitamura I, Tanaka H, Liu H. Quantifying the dynamic wing morphing of hovering hummingbird. Royal Society Open Science, 2017, 4, 170307.

    Article  Google Scholar 

  8. Nakata T, Liu H. Aerodynamic performance of a hovering hawKmoth with flexible wings: A computational approach. Proceedings of the Royal Society B: Biological Sciences, 2012, 279, 722–731.

    Article  Google Scholar 

  9. Cheng B, Tobalske B W, Powers D R, Hedrick T L, Wethington S M, Chiu G T C, Deng X Y. Flight mechanics and control of escape manoeuvres in hummingbirds. I. Flight kinematics. The Journal of Experimental Biology, 2016, 219, 3518–3531.

    Google Scholar 

  10. Altshuler D L, Quicazán-Rubio E M, Segre P S, Middleton K M. Wingbeat kinematics and motor control of yaw turns in Anna’s hummingbirds (Calypte anna). The Journal of Experimental Biology, 2012, 215, 4070–4084.

    Google Scholar 

  11. Liu H. Integrated modeling of insect flight: From morphology, kinematics to aerodynamics. Journal of Computational Physics, 2009, 228, 439–459.

    Article  MathSciNet  MATH  Google Scholar 

  12. Nakata T, Liu H, Bomphrey R J. A CFD-informed quasi-steady model of flapping-wing aerodynamics. The Journal of Fluid Mechanics, 2015, 783, 323–343.

    Article  MathSciNet  MATH  Google Scholar 

  13. Nakata T, Liu H. A fluid-structure interaction model of insect flight with flexible wings. Journal of Computational Physics, 2012, 231, 1822–1847.

    Article  MathSciNet  MATH  Google Scholar 

  14. Yu X, Sun M. A computational study of the wing-wing and wing-body interactions of a model insect. Acta Mechanica Sinica, 2009, 25, 421–431.

    Article  MATH  Google Scholar 

  15. Aono H, Liang F Y, Liu H. Near- and far-field aerodynamics in insect hovering flight: An integrated computational study. The Journal of Experimental Biology, 2008, 211, 239–257.

    Article  Google Scholar 

  16. Lehmann F O, Sane S P, Dickinson M. The aerodynamic effects of wing-wing interaction in flapping insect wings. The Journal of Experimental Biology, 2005, 208, 3075–3092.

    Article  Google Scholar 

  17. Gao N, Aono H, Liu H. Perturbation analysis of 6DoF flight dynamics and passive dynamic stability of hovering fruit fly Drosophila melanogaster. Journal of Theoretical Biology, 2011, 270, 98–111.

    Article  MATH  Google Scholar 

  18. Gao N, Aono H, Liu H. A numerical analysis of dynamic flight stability of hawkmoth hovering. Journal of Biomechanical Science and Engineering, 2009, 4, 105–116.

    Article  Google Scholar 

  19. Floreano D, Wood R J. Science, technology and the future of small autonomous drones. Nature, 2015, 460–466.

  20. Hoffmann G M, Huang H, Waslander S L, Tomlin C J. Quadrotor helicopter flight dynamics and control: Theory and experiment. AIAA Guidance, Navigation and Control Conference and Exhibit, Hilton Head, South Carolina, 2007.

  21. Dief T N, Yoshida S. Review: Modeling and classical controller of quad-rotor. IRACST — International Journal of Computer Science and Information Technology & Security, 2015, 5, 314–319.

    Google Scholar 

  22. Winslow J, Benedict M, Hrishikeshavan V, Chopra I. Design, development, and flight testing of a high endurance micro quadrotor helicopter. International Journal of Micro Air Vehicles, 2016, 8, 155–169.

    Article  Google Scholar 

  23. Dai X H, Quan Q, Cai K Y. Design automation and optimization methodology for electric multicopter UAVs. arXiv: 1908.06301v1 [eess.SY], 2019, 1–27.

  24. Mintchev S, Floreano D. Adaptive morphology: A design principle for multimodal and multifunctional robots. IEEE Robotics & Automation Magazine, 2016, 23, 42–54..

    Article  Google Scholar 

  25. Efraim H, Shapiro A, Weiss G. Quadrotor with a dihedral angle: On the effects of tilting the rotors inwards. The Journal of Intelligent and Robotic Systems, 2015, 80, 313–324.

    Article  Google Scholar 

  26. Elfeky M, Elshafei M, Saif A W A, Al-Malki M F. Modeling and simulation of quadrotor UAV with tilting rotors. International Journal of Control, Automation and Systems, 2016, 14, 1047–1055.

    Article  Google Scholar 

  27. Junaid A B, Sanchez A D D C, Bosch J B, Vitzilaios N, Zweiri Y. Design and implementation of a dual-axis tilting quadcopter. Robotics, 2018, 7, 1–20.

    Article  Google Scholar 

  28. Diógenes H B, dos Santos D A. Modelling, design and simulation of a quadrotor with tilting rotors actuated by a memory shape wire. Conference: Congresso Brasileiro de Engenharia Mecânica (CONEM), agosto de, Fortaleza-Cearâ, 2016.

  29. Aleksandrov D, Penkov I. Optimal gap distance between rotors of mini quadrotor helicopter. 8th International DAAAM Baltic Conference, Tallinn, Estonia, 2012.

  30. Shukla D, Komerath N. Multirotor drone aerodynamic interaction investigation. Drones, 2018, 2, 1–13.

    Article  Google Scholar 

  31. Yoon S, Lee H C, Pulliam T H. Computational analysis of multi-rotor flows. 54th AIAA Aerospace Science Meeting, San Diego, California, USA, 2016.

  32. Theys B, Dimitriadis G, Hendrick P, De Schutter J. Influence of propeller configuration on propulsion system efficiency of multi-rotor Unmanned Aerial Vehicles. International Conference on Unmanned Aircraft Systems (ICUAS), Arlington, VA USA, 2016.

  33. Lei Y, Wang J L. Aerodynamic performance of quadrotor UAV with non-planar rotors. Applied Science, 2019, 9, 2779.

    Article  Google Scholar 

  34. Nguyen H D, Liu Y, Mori K. Experimental study for aerodynamic performance of quadrotor helicopter. Transactions of the Japan Society for Aeronautical and Space Sciences, 2018, 61, 29–39.

    Article  Google Scholar 

  35. Nguyen H D, Liu Y, Mori K. Unsteady aerodynamic parameter estimation for multirotor helicopters. Transactions of the Japan Society for Aeronautical and Space Sciences, 2019, 62, 32–40.

    Article  Google Scholar 

  36. Noda R, Nakata T, Ikeda T, Chen D, Yoshinaga Y, Ishibashi K, Rao C, Liu H. Development of bio-inspired low-noise propeller for a drone. Journal of Robotics and Mechatronics, 2018, 30, 337–343.

    Article  Google Scholar 

  37. Ikeda T, Tanaka H, Yoshimura R, Noda R, Fujii T, Liu H. A robust biomimetic blade design for micro wind turbines. Renew Energy, 2018, 125, 155–165.

    Article  Google Scholar 

  38. Ellington C P. The aerodynamics of hovering insect flight. V. A vortex theory. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 1984, 305, 115–144.

    Google Scholar 

  39. Usherwood J R, Ellington C P. The aerodynamics of revolving wings I. Model hawkmoth wings. The Journal of Experimental Biology, 2002, 205, 1547–1564.

    Article  Google Scholar 

  40. Forrester A I J, Keane A J. Recent advances in surrogate-based optimization. Progress in Aerospace Science, 2009, 45, 50–79.

    Article  Google Scholar 

  41. Bouhlel M A, Hwang J T, Bartoli N, Lafage R, Morlier J, Martins J.R.R.A. A Python surrogate modeling framework with derivatives. Advance in Engineering Software, 2019, 135, 1–27.

    Article  Google Scholar 

  42. Lee H M, Hur N K, Kwon O J. Aerodynamic design optimization of UAV rotor blades using a genetic algorithm. The World Congress on Aeronautics, Nano, Bio, Robotics, and Energy (ANBRE15), Incheon, Korea, 2015.

  43. Zhong H H, Zhang K S. Surrogate-based optimization. In: Olympia R ed., Real-World Applications of Genetic Algorithms, Institute of Technology, Australia, 2012.

    Google Scholar 

  44. Fornberg B, Flyer N. Solving PDEs with radial basis functions. Acta Numerica, 2015, 24, 215–258.

    Article  MathSciNet  MATH  Google Scholar 

  45. Bhatia G S, Arora G. Radial basis function methods for solving partial differential equations-A review. Indian Journal of Science and Technology, 2016, 9, 1–16.

    Google Scholar 

  46. Koushki M, Jabbari E, Ahmadinia M. Evaluating RBF methods for solving PDEs using Padua points distribution. Alexandria Engineering Journal, 2020, 59, 2999–3018.

    Article  Google Scholar 

  47. Bangura M, Melega M, Naldi R, Mahony R. Aerodynamics of Rotor Blades for Quadrotors. arXiv: 1601.00733v1 [physics.flu-dyn], 2016.

Download references

Acknowledgment

This work was partly supported by the Grant-in-Aid for Scientific Research of KAKENHI No. 19H02060, 19H00750, JSPS and a Global Prominent Research Program, Chiba University.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hao Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Yonezawa, K., Xu, R. et al. A Biomimetic Rotor-configuration Design for Optimal Aerodynamic Performance in Quadrotor Drone. J Bionic Eng 18, 824–839 (2021). https://doi.org/10.1007/s42235-021-0069-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42235-021-0069-0

Keywords

Navigation