Identification of particle-laden flow features from wavelet decomposition
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 demons...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2017
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| Online Access: | https://eprints.nottingham.ac.uk/47058/ |
| _version_ | 1848797458101960704 |
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| author | Jackson, Andrew M. Turnbull, Barbara |
| author_facet | Jackson, Andrew M. Turnbull, Barbara |
| author_sort | Jackson, Andrew M. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:04:11Z |
| format | Article |
| id | nottingham-47058 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:04:11Z |
| publishDate | 2017 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-470582018-10-10T04:30:20Z https://eprints.nottingham.ac.uk/47058/ Identification of particle-laden flow features from wavelet decomposition Jackson, Andrew M. Turnbull, Barbara 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. Elsevier 2017-12-15 Article PeerReviewed application/pdf en cc_by_nc_nd https://eprints.nottingham.ac.uk/47058/1/paper_accepted.pdf 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 Wavelet Particle-laden gravity current Filtering Signal processing http://www.sciencedirect.com/science/article/pii/S0167278917302890 doi:10.1016/j.physd.2017.09.009 doi:10.1016/j.physd.2017.09.009 |
| spellingShingle | Wavelet Particle-laden gravity current Filtering Signal processing Jackson, Andrew M. Turnbull, Barbara Identification of particle-laden flow features from wavelet decomposition |
| title | Identification of particle-laden flow features from wavelet decomposition |
| title_full | Identification of particle-laden flow features from wavelet decomposition |
| title_fullStr | Identification of particle-laden flow features from wavelet decomposition |
| title_full_unstemmed | Identification of particle-laden flow features from wavelet decomposition |
| title_short | Identification of particle-laden flow features from wavelet decomposition |
| title_sort | identification of particle-laden flow features from wavelet decomposition |
| topic | Wavelet Particle-laden gravity current Filtering Signal processing |
| url | https://eprints.nottingham.ac.uk/47058/ https://eprints.nottingham.ac.uk/47058/ https://eprints.nottingham.ac.uk/47058/ |