Time domain characterization of nonstationary low-Mach number aeroacoustic sources using principal component analysis.

Abstract

This paper presents the use of principal component analysis (PCA) for time domain microphone array denoising to characterize an impulsive aeroacoustic source, which is illustrated with the aeroacoustic emission caused by a vortex ring/edge interaction. Prior studies have used signal processing approaches that required assumptions about the source directivity or user intervention at low signal-to-noise ratio (SNR) conditions. In this context, PCA, a matrix decomposition tool which identifies the most common features across an ensemble of observations, provides a data-driven (hands-off) approach to signal processing. For microphone array time series, particular attention is paid to the temporal alignment of the signals to facilitate PCA. A time domain approach is necessary when sources are impulsive and nonstationary. Two such signal arrangements are discussed in this work. Results from this method are in good agreement with theory, validated by prior results using an ensemble averaging approach requiring user support. Furthermore, the results of this method are improved when compared to the ensemble averaging approach without user intervention. A SNR limit is identified where PCA becomes less effective for the vortex/edge interaction problem. This SNR limit is intended to aid in the design of similar future experiments.