Computational Acoustic Beamforming for Noise Source Identification for Small Horizontal Axis Wind Turbines

Abstract

This thesis develops a computational acoustic beamforming (CAB) method for identification of sources of small wind turbine noise. The methodology consists of three components: computational fluid dynamic (CFD), acoustic propagation and acoustic beamforming components. Each component of the CAB method is validated on the component level. The numerical results agree well with the experimental data for the validation of each component. The CAB method is then validated on the whole system level using the NACA 0012 airfoil trailing edge noise case. The predicted acoustic maps are in excellent agreement with the corresponding observed acoustic maps obtained from wind-tunnel experiments. It is found that the spatial resolution of the acoustic maps increases with increasing frequency. It is also found that the Archimedean spiral array has a better spatial resolution than the star array at all frequencies of interest. Furthermore, an Archimedean spiral array exhibits better signal to noise ratio (SNR) at frequencies below 1000 Hz, but poorer SNR at frequencies above 1000 Hz when compared to the performance of a star microphone array. Following these validation studies, the CAB methodology was applied to the identification of noise sources generated by a commercial small wind turbine (WINPhase 10 wind turbine). Despite the coarse grid and large time step used in the CFD simulations, the simulated aerodynamic results (wind turbine power output) and the aeroacoustic results (A-weighted SPL spectra) are in good agreement with some field measurements for this wind turbine. The simulated acoustic maps reveal that the blade tower interaction and the wind turbine nacelle are two possible noise generation mechanisms in the range of frequencies between 200 and 630 Hz for this small wind turbine.