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Title: Dynamics of random packings in granular flow

Journal Article · · Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
 [1];  [1];  [2];  [3]
  1. Massachusetts Institute of Technology (MIT), Cambridge, MA (United States)
  2. Sandia National Laboratory (SNL-NM), Albuquerque, NM (United States)
  3. Massachusetts Institute of Technology (MIT), Lexington, MA (United States). Lincoln Laboratory

Here we present a multiscale simulation algorithm for amorphous materials, which we illustrate and validate in a canonical case of dense granular flow. Our algorithm is based on the recently proposed Spot Model, where particles in a dense random packing undergo chain-like collective displacements in response to diffusing “spots” of influence, carrying a slight excess of interstitial free volume. We reconstruct the microscopic dynamics of particles from the “coarse grained” dynamics of spots by introducing a localized particle relaxation step after each spot-induced block displacement, simply to enforce packing constraints with a (fairly arbitrary) soft-core repulsion. To test the model, we study to what extent it can describe the dynamics of up to 135,000 frictional, viscoelastic spheres in granular drainage simulated by the discrete-element method (DEM). With only five fitting parameters (the radius, volume, diffusivity, drift velocity, and injection rate of spots), we find that the spot simulations are able to largely reproduce not only the mean flow and diffusion, but also some subtle statistics of the flowing packings, such as spatial velocity correlations and many-body structural correlations. The spot simulations run over 100 times faster than DEM and demonstrate the possibility of multiscale modeling for amorphous materials, whenever a suitable model can be devised for the coarse-grained spot dynamics.

Research Organization:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE); Norbert Weiner Research Fund; USDOE National Nuclear Security Administration (NNSA); Massachusetts Institute of Technology (MIT)
Grant/Contract Number:
FG02-02ER25530; AC04-94AL85000
OSTI ID:
882197
Report Number(s):
DOE/ER/25530-4
Journal Information:
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, Vol. 73, Issue 5; ISSN 1539-3755
Publisher:
American Physical Society (APS)Copyright Statement
Country of Publication:
United States
Language:
English

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Cited By (3)

Nonlinear elasto-plastic model for dense granular flow journal February 2010
The stochastic flow rule: a multi-scale model for granular plasticity journal May 2007
Data-driven model order reduction for granular media text January 2020

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