Abstract
To identify sound source locations by using Near-field Acoustical Holography (NAH), a large number of microphone measurements is generally required in order to cover the source region and ensure a sufficiently high spatial sampling rate: it may require hundreds of microphones. As a result, such measurements are costly, a fact which has limited the industrial application of NAH to identify sound source locations. However, recently, it has been shown possible to identify concentrated sound sources with a limited number of microphone measurements based on Compressive Sensing theory. In the present work, sound radiation from the front face of a diesel engine was measured by using one set of measurements from a thirty-five-channel combo-array placed in front of the engine. The locations of significant noise sources were then identified by using two algorithms: i.e., l1-norm minimization and a hybrid approach which combined Wideband Acoustical Holography (WBH) and l1- norm minimization. It was found that both algorithms could successfully localize and visualize the major noise sources over a broad range of frequencies, even when using a relatively small number of microphones. Finally, comments are made on sound field reconstruction differences between the two algorithms.
Keywords
Diesel engine noise, Source visualization, Nearfield acoustical holography, Wide band holography, L1-norm minimization
Subject
Acoustics and Noise Control
Date of this Version
8-22-2022
Comments
Tongyang Shi, J. Stuart Bolton and Frank Eberhardt, “Diesel Engine Noise Source Visualization by Using Compressive Sensing Algorithms,” in Proceedings of InterNoise 2022, 10 pages, 21-24 August, 2022, Glasgow, UK