Identification of particle-laden flow features from wavelet decomposition

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Jackson, Andrew M. and Turnbull, Barbara (2017) Identification of particle-laden flow features from wavelet decomposition. Physica D: Nonlinear Phenomena, 361 . pp. 12-27. ISSN 0167-2789

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Abstract

A wavelet decomposition based technique is applied to air pressure data obtained from laboratory-scale powder snow avalanches. This technique is shown to be a powerful tool for identifying both repeatable and chaotic features at any frequency within the signal. Additionally, this technique is demonstrated to be a robust method for the removal of noise from the signal as well as being capable of removing other contaminants from the signal. Whilst powder snow avalanches are the focus of the experiments analysed here, the features identified can provide insight to other particle-laden gravity currents and the technique described is applicable to a wide variety of experimental signals.

Item Type: Article
Keywords: Wavelet, Particle-laden gravity current, Filtering, Signal processing
Schools/Departments: University of Nottingham, UK > Faculty of Engineering
Identification Number: https://doi.org/10.1016/j.physd.2017.09.009
Depositing User: Eprints, Support
Date Deposited: 09 Oct 2017 09:37
Last Modified: 10 Oct 2018 04:30
URI: https://eprints.nottingham.ac.uk/id/eprint/47058

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