4.7 Article

Experimental multimode traveling waves identification in an acoustic waveguide

Journal

MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 153, Issue -, Pages -

Publisher

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2020.107515

Keywords

Model-based identification; Traveling waves; Model order selection; Parameter sensitivity analysis; Acoustic waveguide; NDT of acoustic properties

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This paper introduces a method that expands the concept of traveling wave ratio, which decomposes waves through several schemes and proposes an optimal balance between increasing model order and maintaining low uncertainty. Experimental results demonstrate the importance of including additional propagating modes in nondestructive testing and waveguide experiments, as well as the capability of using the recursive multichannel least-mean-squares method to control wave propagation patterns.
An essential part of nondestructive testing and experimental modeling of waveguides is the decomposition of propagating wave patterns. The traveling wave ratio is a measure of partial reflections assisting in quantifying the pureness of a single traveling wave from a power flow perspective. This paper expands the notion of traveling wave ratio for multimode systems and outlines several schemes capable of decomposing the waves into their different traveling modes while quantifying their traveling and standing proportions individually. A method to strike an optimal balance between increasing model order and to maintain low uncertainty is proposed. An experimental study performed on an acoustic wave tube, which utilizes the various methods while assessing their accuracy and performance, is reported. The results described here emphasize the importance of including additional propagating modes. In addition, the results illustrate the capability of using the recursive multichannel least-mean-squares method for both a fast decomposition and as a basis to formulate closed-loop schemes controlling the wave's propagation patterns. (C) 2020 Elsevier Ltd. All rights reserved.

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