Publication | Open Access
Multirotor Drone Aerodynamic Interaction Investigation
143
Citations
16
References
2018
Year
Small Size UavsAeronauticsEngineeringAerial RoboticsRotorcraft AerodynamicsAerospace EngineeringRotor SpacingAerodynamicsMany Uav RotorsFlying RobotRotor DynamicKinematicsUnmanned Aerial SystemsAir Vehicle System
Aerodynamic interactions between rotors critically influence the performance of in‑plane multirotor UAVs, particularly at low Reynolds numbers where flow features diverge from traditional rotorcraft models. The study examined side‑by‑side rotors in hover across varying separations and Reynolds numbers using high‑speed stereo particle image velocimetry and performance measurements. SPIV data reveal stronger inter‑rotor wake interactions as spacing and Reynolds number decrease, causing a dip in rotor efficiency at small spacing and low Reynolds number, yet the results largely validate traditional analysis tools with minor adjustments.
Aerodynamic interactions between rotors are important factors affecting the performance of in-plane multirotor Unmanned Air Vehicles (UAVs) or drones, which are the majority of small size UAVs (or mini-drones). Optimal design requires knowledge of the flow features. The low Reynolds number of many UAV rotors raises the question of how these features differ from those expected by traditional analytical methods for rotorcraft. Aerodynamics of a set of side-by-side rotors in hover over a range of rotor separation and Reynolds number is studied using high-speed Stereo Particle Image Velocimetry (SPIV) and performance measurements. The instantaneous and time-averaged SPIV data presented here indicate an increase in inter-rotor wake interactions with decrease in rotor spacing and Reynolds number. A dip in rotor efficiency at small rotor spacing at low Reynolds number is observed through thrust and torque measurements. The basic components of in-plane multirotor wake and velocity profiles are identified and discussed to help generalize the findings to a wide range of drones. However, the data provide confidence in traditional analysis tools, with small modifications.
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