A well-posed multilayer model for granular avalanches: Comparisons with laboratory experiments

L. Sarno, Y. Wang, Y. C. Tai, M. N. Papa, P. Villani, M. Oberlack

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Granular avalanches are dangerous phenomena characterized by the rapid gravity-driven motion of granular solids. The complex dynamics of these flows can be effectively modeled by a multilayer approach, which, however, requires particular attention to the derivation of the model equations in order to allow stable solutions. In this work, we use a well-posed multilayer model, in which the μ(I)-rheology is employed and a dilatancy law, depending on the inertial number I, is also taken into account, and systematically compare it with various laboratory experiments. The model, whose well-posedness is guaranteed by a physically based viscous regularization, describes the evolution of a preset number of superimposed granular layers. As the sidewall friction is relevant under most experimental conditions, the model is fitted here with suitable resistance terms. Moreover, non-trivial closures for the mass exchanges are introduced to avoid any unrealistic partitioning of the flow domain during the avalanche evolution, and, hence, guarantee a regular spatial discretization along the normal to flow direction. The velocity fields are compared with different experiments in unsteady state, and comparisons of both velocity and volume fraction profiles are provided with steady uniform flow experiments. The results confirm the good capabilities of the multilayer model and the underlying μ(I)-rheology in capturing the granular flow dynamics. The experimental volume fraction profiles are qualitatively well reproduced by the proposed dilatancy law, while an overestimation is observed only in the upper, more dilute flow region with a thickness of a few grain diameters.

Original languageEnglish
Article number113307
JournalPhysics of Fluids
Volume34
Issue number11
DOIs
Publication statusPublished - 2022 Nov 1

All Science Journal Classification (ASJC) codes

  • Computational Mechanics
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering
  • Fluid Flow and Transfer Processes

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