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Cited by 353

The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.

Particle Segregation in Dense Granular Flows

Open access
Gray, John Mark Nicholas Timm·2018·Annual Review of Fluid Mechanics

Abstract

Granular materials composed of particles with differing grain sizes, densities, shapes, or surface properties may experience unexpected segregation during flow. This review focuses on kinetic sieving and squeeze expulsion, whose combined effect produces the dominant gravity-driven segregation mechanism in dense sheared flows. Shallow granular avalanches that form at the surface of more complex industrial flows such as heaps, silos, and rotating drums provide ideal conditions for particles to separate, with large particles rising to the surface and small particles percolating down to the base. When this is combined with erosion and deposition, amazing patterns can form in the underlying substrate. Gravity-driven segregation and velocity shear induce differential lateral transport, which may be thought of as a secondary segregation mechanism. This allows larger particles to accumulate at flow fronts, and if they are more frictional than the fine grains, they can feedback on the bulk flow, causing flow fingering, levee formation, and longer runout of geophysical mass flows.

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Crossref · 10.1146/annurev-fluid-122316-045201

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Metadata

Authors
Gray, J. M. N. T.
Venue
Annual Review of Fluid Mechanics
Published
2018-01-05
Volume
50
Issue
1
Pages
407-433
ISSN
  • 0066-4189 (ISSN-L)
  • 1545-4479
OpenAlex
W2616938422
Cited by
353
References
45
FWCI
2.87
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Particle Segregation in Dense Granular Flows. https://app.cochat.ai/papers/10.1146/annurev-fluid-122316-045201
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Gray, J. M. N. T. (2018). Particle Segregation in Dense Granular Flows. Annual Review of Fluid Mechanics. https://doi.org/10.1146/annurev-fluid-122316-045201

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.