Identification of particle-laden flow features from wavelet decomposition

Jackson, Andrew M.; Turnbull, Barbara

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.

Citation

Jackson, A. M., & Turnbull, B. (2017). Identification of particle-laden flow features from wavelet decomposition. Physica D: Nonlinear Phenomena, 361, https://doi.org/10.1016/j.physd.2017.09.009

• Journal Article Type: Article
• Acceptance Date: Sep 28, 2017
• Online Publication Date: Oct 10, 2017
• Publication Date: Dec 15, 2017
• Deposit Date: Oct 9, 2017
• Publicly Available Date: Oct 11, 2018
• Journal: Physica D: Nonlinear Phenomena
• Print ISSN: 0167-2789
• Electronic ISSN: 0167-2789
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 361
• DOI: https://doi.org/10.1016/j.physd.2017.09.009
• Keywords: Wavelet, Particle-laden gravity current, Filtering, Signal processing
• Public URL: https://nottingham-repository.worktribe.com/output/900161
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S0167278917302890