Guided wave tomography with an improved scattering model

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Title: Guided wave tomography with an improved scattering model
Author(s): Huthwaite, P
Item Type: Journal Article
Abstract: Producing accurate thickness maps of corrosion damage is of great importance for assessing life in the petrochemical industry. Guided wave tomography provides a solution for this, by sending guided waves through the region of interest, then using tomographic imaging techniques to reconstruct the thickness map, importantly eliminating the need to take measurements at all points across the surface. However, to achieve accurate maps, the imaging algorithm must account for the way in which the guided waves interact with corrosion defects, and the complex scattering which occurs. Traditional approaches have exploited the dispersive nature of guided waves: a velocity map is produced from a dataset, then converted to thickness using the dispersion relationship. However, these relationships are derived for plates of constant thickness, which is not the case in the majority of defects, causing significant inaccuracies to exist in the images. This paper develops a more sophisticated inversion solution which accounts for the full-guided wave scattering, enabling more accurate images with resolution better than a wavelength, compared with two wavelengths previously. This is demonstrated with simulated and experimental data. The speed and stability of the algorithm in the presence of random noise and systematic errors is also demonstrated.
Publication Date: 2-Nov-2016
Date of Acceptance: 5-Oct-2016
URI: http://hdl.handle.net/10044/1/43120
DOI: http://dx.doi.org/10.1098/rspa.2016.0643
ISSN: 1471-2946
Publisher: Royal Society, The
Journal / Book Title: Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences
Volume: 472
Copyright Statement: © 2016 The Author(s). Published by the Royal Society. All rights reserved.
Keywords: 01 Mathematical Sciences
02 Physical Sciences
09 Engineering
Publication Status: Published
Article Number: 20160643
Appears in Collections:Faculty of Engineering
Mechanical Engineering



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