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Related Experiment Videos

Stress results from two-dimensional granular shear flow simulations using various collision models.

William R Ketterhagen1, Jennifer S Curtis, Carl R Wassgren

  • 1School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|August 11, 2005
PubMed
Summary

This study compares discrete element method collision models for granular flows. Soft-particle and hybrid models accurately predict stresses across various solid fractions, provided sufficient stiffness is used.

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Area of Science:

  • Computational physics
  • Granular mechanics
  • Discrete element methods

Background:

  • Collision resolution is crucial for discrete element method (DEM) simulations of granular flows.
  • Hard-particle models suit dilute systems, while soft-particle models are better for dense systems with multiple, enduring collisions.

Purpose of the Study:

  • To explore the boundary between dilute and dense systems for DEM granular flow modeling.
  • To evaluate the suitability of different collision models across a range of solid fractions.

Main Methods:

  • Simulated two-dimensional shear flow using discrete element methods.
  • Compared an event-driven hard-particle model, a hysteretic spring soft-particle model (Walton and Braun, 1986), and a hybrid hard-particle-with-overlap model (Hopkins and Louge, 1991).

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Main Results:

  • Both hard-particle-with-overlap and soft-particle models accurately predict stresses for various solid fractions, coefficients of restitution, and friction coefficients.
  • Accurate stress prediction with soft-particle models requires a sufficiently large loading stiffness.
  • Investigated model accuracy and collisional overlap as functions of simulation time step and parameters.

Conclusions:

  • Hybrid and soft-particle collision models are effective for DEM granular flow simulations across a spectrum of solid fractions.
  • Model parameter selection, particularly loading stiffness in soft-particle models, is critical for accurate stress prediction.