Identification of particle-laden flow features from wavelet decomposition

PHYSICA D-NONLINEAR PHENOMENA

卷:361

Article

Jackson, A.^1,2  Turnbull, B.¹ 

[1] Univ Nottingham, Fac Engn, Geohazards & Earth Proc Res Grp, Nottingham NG7 2RD, England

[2] Hlth & Safety Lab, Harpur Hill, Buxton SK17 9JN, England

关键词: Wavelet;    Particle-laden gravity current;    Filtering;    Signal processing;   

DOI  :  10.1016/j.physd.2017.09.009

来源: Elsevier

【 摘 要 】

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. (C) 2017 Elsevier B.V. All rights reserved.

【 授权许可】

Free