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Grain Reynolds Number Scale Effects in Dry Granular Slides
Author(s) -
Kesseler Matthew,
Heller Valentin,
Turnbull Barbara
Publication year - 2020
Publication title -
journal of geophysical research: earth surface
Language(s) - English
Resource type - Journals
eISSN - 2169-9011
pISSN - 2169-9003
DOI - 10.1029/2019jf005347
Subject(s) - froude number , reynolds number , scale (ratio) , discrete element method , mechanics , mathematics , range (aeronautics) , scale model , environmental science , geotechnical engineering , statistics , geology , engineering , materials science , physics , composite material , breakup , quantum mechanics , turbulence , aerospace engineering
Scale effects are differences in physical behavior that manifest between a large event and a geometrically scaled laboratory model and may cause misleading predictions. This study focuses on scale effects in granular slides, important in the environment and to industry. A versatile 6 m long laboratory setup has been built following Froude similarity to investigate dry granular slides at scales varied by a factor of 4, with grain Reynolds numbers Re in the range of 102to 103 . To provide further comparison, discrete element method simulations have also been conducted. Significant scale effects were identified; the nondimensional surface velocity increased by up to 35%, while the deposit runout distance increased by up to 26% from the smallest to the largest model. These scale effects are strongly correlated with Re, suggesting that interactions between grains and air are primarily responsible for the observed scale effects. This is supported by the discrete element method data, which did not show these scale effects in the absence of air. Furthermore, the particle drag force accounted for a significant part of the observed scale effects. Cauchy number scale effects caused by unscaled particle stiffness resulting in varying dust formation with scale are found to be of secondary importance. Comparisons of the laboratory data to that of other studies and of natural events show that data normalization with Re is an effective method of quantitatively comparing laboratory results to natural events. This upscaling technique can improve hazard assessment in nature and is potentially useful for modeling industrial flows.