Haywood-Alexander, M., Dervilis, N. orcid.org/0000-0002-5712-7323, Worden, K. et al. (3 more authors) (Submitted: 2022) A Bayesian method for material identification of composite plates via dispersion curves. arXiv. (Submitted)
Abstract
Ultrasonic guided waves offer a convenient and practical approach to structural health monitoring and non-destructive evaluation. A key property of guided waves is the fully-defined relationship between central frequency and propagation characteristics (phase velocity, group velocity and wavenumber) -- which is described using dispersion curves. For many guided wave-based strategies, accurate dispersion curve information is invaluable, such as group velocity for localisation. From experimental observations of dispersion curves, a system identification procedure can be used to determine the governing material properties. As well as returning an estimated value, it is useful to determine the distribution of these properties based on measured data. A method of simulating samples from these distributions is to use the iterative Markov-Chain Monte Carlo procedure, which allows for freedom in the shape of the posterior. In this work, a scanning-laser doppler vibrometer is used to record the propagation of Lamb waves in a unidirectional-glass-fibre composite plate, and dispersion curve data for various propagation angles are extracted. Using these measured dispersion curve data, the MCMC sampling procedure is performed to provide a Bayesian approach to determining the dispersion curve information for an arbitrary plate.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2022 The Author(s). For reuse permissions, please contact the Author(s). Repeat on Review-comments page |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 13 Oct 2022 12:49 |
Last Modified: | 13 Oct 2022 12:53 |
Status: | Submitted |
Identification Number: | 10.48550/arXiv.2209.03706 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:191141 |