Computational characterization of rotor aero-acoustics for wind turbines and drones

Abstract

There is a resurgence of rotating blades in the twenty-first century. They are being used to harvest renewable energy from the wind and to provide green mobility transportation in the form of drones. However, these applications, with greater deployment and closer proximity to society, will require noise mitigation to minimize the impact on the community’s well-being. The present studies present the computational characterization of the aero-acoustics of a small-scale wind turbine and a commercial drone propeller in conical operating conditions (i.e., zero wind speed and hover, respectively). Validated computational methodologies that can characterize the aero-acoustics of rotating blades can then be used to investigate low-noise and efficient rotor blade designs before proceeding to more expensive experimental campaigns. In both studies, compressible Unsteady Reynolds Average Navier-Stokes (URANS) simulations utilizing Direct Noise Computation (DNC) approach are employed. In the first study, an experimental small-scale wind turbine model is numerically replicated and simulated. The simulated aerodynamic performance (i.e., thrust and torque) are compared against the experiment and are in fair agreement. Furthermore, through comparison against the experiment noise emission, both the simulated tonal noise frequency and amplitude are within an acceptable range of tolerance. In the second study, a commercial drone propeller is numerically simulated. The aerodynamic thrust and flow field are compared against the relevant literature, and a general agreement is shown. Furthermore, the rotor tonal noise frequency could be accurately predicted.

Date of Award	2023
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology
Supervisor	Stephane REDONNET (Supervisor)