Fannon_2019_Application.pdf (989.81 kB)
Application of the compressible I-dependent rheology to chute and shear flow instabilities
journal contribution
posted on 2022-10-05, 13:08 authored by James S. Fannon, Iain R. Moyles, Andrew FowlerAndrew FowlerWe consider the instability properties of dense granular flow in inclined plane and plane shear geometries as tests for the compressible inertial-dependent rheology. The model, which is a recent generalisation of the incompressible π
π(I)
rheology, constitutes a hydrodynamical description of dense granular flow which allows for variability in the solids volume fraction. We perform a full linear stability analysis of the model and compare its predictions to existing experimental data for glass beads on an inclined plane and discrete element simulations of plane shear in the absence of gravity. In the case of the former, we demonstrate that the compressible model can quantitatively predict the instability properties observed experimentally, and, in particular, we find that it performs better than its incompressible counterpart. For the latter, the qualitative behaviour of the plane shear instability is also well captured by the compressible model.
History
Publication
Journal of Fluid Mechanics;864, pp. 1026-1057Publisher
Cambridge University PressNote
peer-reviewed The full text of this article will not be available in ULIR until the embargo expires on the 14/08/2019Other Funding information
SFIRights
Material on these pages is copyright Cambridge University Press or reproduced with permission from other copyright owners. It may be downloaded and printed for personal reference, but not otherwise copied, altered in any way or transmitted to others (unless explicitly stated otherwise) without the written permission of Cambridge University Press. Hypertext links to other Web locations are for the convenience of users and do not constitute any endorsement or authorisation by Cambridge University Press.Language
EnglishAlso affiliated with
- MACSI - Mathematics Application Consortium for Science & Industry
External identifier
Department or School
- Mathematics & Statistics