# GPF

## Seminar

### A multiscale simulation technique for granular flow

Seminar Room 1, Newton Institute

#### Abstract

Due to an incomplete picture of the underlying physics, the simulation of dense granular flow remains a difficult computational challenge. Currently, modeling in practical and industrial situations would typically be carried out by using the Discrete-Element Method (DEM), individually simulating particles according to Newton’s Laws. The contact models in these simulations are stiff and require very small timesteps to integrate accurately, meaning that even relatively small problems require days or weeks to run on a parallel computer [2]. These brute-force approaches often provide scant insight into the relevant collective physics, and they are infeasible for applications in real-time process control, or in optimisation, where there is a need to run many different configurations much more rapidly. In previous work, a multiscale simulation was demonstrated that was able to correctly capture flow fields, diffusion and mixing in hopper drainage [1]. The key observation behind this simulation was that particles in dense granular flows are strongly geometrically constrained. Since they form an amorphous random packing, a single particle is confined by its neighbors, and in order to flow, it must do so cooperatively with its neighbors. Thus this simulation was based on breaking down the flow into correlated group displacements on a mesoscopic length scale. In a related study, it was shown that continuum variables, while intractable at the level of a single particle, can successfully be interpreted at the same scale [3], and this information can be used to directly test and develop a continuum theory of granular materials. Drawing on these concepts, a multiscale simulation technique will be presented, that couples a macroscopic continuum theory of granular flow to a discrete microscopic mechanism for particle motion. The technique can be applied to arbitrary slow, dense granular flows, and can reproduce similar flow fields and microscopic packing structure estimates as in DEM. Since forces and stress are coarse-grained, the simulation technique runs two to three orders of magnitude faster than conventional DEM. A particular strength is the ability to capture particle diffusion, allowing for the optimization of granular mixing, by running an ensemble of different possible configurations. References [1] Chris H. Rycroft, Martin Z. Bazant, Gary S. Grest, and James W. Landry. Dynamics of random packings in granular flow. Phys. Rev. E, 73:051306, 2006. [2] Chris H. Rycroft, Gary S. Grest, James W. Landry, and Martin Z. Bazant. Analysis of granular flow in a pebble-bed nuclear reactor. Phys. Rev. E, 74:021306, 2006. [3] Chris H. Rycroft, Ken Kamrin, and Martin Z. Bazant. Assessing continuum postulates in simulations of granular flow. submitted. Preprint available at http://math.berkeley.edu/chr/publish/rycroft08.html.#### Video

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