(385c) Modeling of Granular Mixing Using a Dem-Based Markov Process Method

This work is concerned with the modeling of granular mixing using Markov chain theory. Previous papers on this topic are either based on restrictive underlying assumptions about the flow structure, or are limited to a rather small number of states. In this paper, a generalized approach for the construction of a multidimensional state space Markov chain that represents the flow of particles is introduced. The transition probability matrix is computed directly using results obtained from discrete element models. Several parameters are investigated such as the discrete time step of the chain, the learning time and the dimension of the state space. This work shows that, if an accurate measure of the state of the system is available, the evolution of a complex N-body dynamics can be approximated by a simple linear map.