Drone blade aerodynamics and aeroacoustics
In drone propulsion research, understanding both the flow structures around the rotor blades and the resulting acoustic emissions is essential for achieving optimal aerodynamic performance and reduced noise output. While previous studies have largely focused on overall thrust and blade efficiency, recent advances in diagnostics enable more detailed investigation into wake dynamics and noise mechanisms.
This research investigates the aerodynamic and aeroacoustic mechanisms near the trailing edge of rotating blades through integrated experiments. Particle image velocimetry (PIV) is used to quantify turbulent kinetic energy, Reynolds shear stress, velocity fluctuations, and flow-based acoustic source intensity, while anechoic noise and thrust measurements evaluate acoustic and aerodynamic performance. The study focuses on reducing broadband noise radiated from the trailing edge by controlling the local flow structures responsible for its generation. It also explores controllable metastructures integrated into the trailing edge to achieve optimal noise reduction and aerodynamic performance under different operating conditions. By correlating flow, acoustic, and thrust data, this work aims to clarify the fluid-dynamic mechanisms governing rotor noise generation and control.
This research investigates the aerodynamic and aeroacoustic mechanisms near the trailing edge of rotating blades through integrated experiments. Particle image velocimetry (PIV) is used to quantify turbulent kinetic energy, Reynolds shear stress, velocity fluctuations, and flow-based acoustic source intensity, while anechoic noise and thrust measurements evaluate acoustic and aerodynamic performance. The study focuses on reducing broadband noise radiated from the trailing edge by controlling the local flow structures responsible for its generation. It also explores controllable metastructures integrated into the trailing edge to achieve optimal noise reduction and aerodynamic performance under different operating conditions. By correlating flow, acoustic, and thrust data, this work aims to clarify the fluid-dynamic mechanisms governing rotor noise generation and control.
Figure 1. Experimental setup for (a) TR-PIV and (b) noise measurement
Figure 2. Experimental results. (a) Phase averaged vorticity, (b) Acoustic source intensity contour near the TE, (c) Noise spectra.