Abstract
The characterization of rotating aeroacoustic sources using microphone array methods has been proven to be a useful tool. One technique to identify rotating sources is the virtual rotating array method. The method interpolates the pressure time data signals between the microphones in a stationary array to compensate the motion of the rotating sources. One major drawback of the method is the requirement of ring array geometries that are centred around the rotating axis. This contribution extends the virtual rotating array method to arbitrary microphone configurations. Two different ways to interpolate the time signals between the microphone locations are proposed. The first method constructs a mesh between the microphone positions using Delaunay-triangulation and interpolates over the mesh faces using piecewise linear functions. The second one is a meshless technique which is based on radial basis function interpolation. The methods are tested on synthetic array data from a benchmark test case as well as on experimental data obtained with a spiral array and a five-bladed fan.
Highlights
Beamforming and other array methods for acoustic source localisation have been extensively investigated for multichannel microphone measurements
The modified test case with the spiral array geometry with Delaunay triangulation and barycentric interpolation as well as radial basis function interpolation with cubic basis functions. They are compared to the beamforming results without motion compensation and the virtual rotating array (VRA) method using a ring array and linear interpolation
The effect is stronger for the radial basis function interpolation
Summary
Beamforming and other array methods for acoustic source localisation have been extensively investigated for multichannel microphone measurements. The characterisation of rotating aeroacoustic sources using microphone array methods has been proven to be a useful tool. Various methods for identifying rotating noise source have been proposed. They can be classified as methods in time domain or in frequency domain. Rotating beamforming in time domain with a rotating focus point has been applied using the Rotating Source Identifier (ROSI) [1] for wind turbine and helicopter blades. The method was later combined with deconvolution algorithms as a hybrid time and frequency domain method [2] and applied to rotating sources [3]. An improved way to calculate the point spread function for the deconvolution of rotating sources was derived by Debrouwere et al [4]
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