Modeling collective dynamics of particulate systems under time-varying operating conditions based on Markov chains

This paper develops an approach to modeling and analyzing the overall dynamics of monodisperse particulate systems in a horizontal rotating drum under time-varying drum rotational speeds. This approach captures the above collective dynamics using stochastic models in the form of Markov chains. The characteristics of such dynamics can be obtained from the Markov chain operator. It provides a systematic way to the analysis of features of collective particle movements, which is in contrast to the existing qualitative analysis. In this paper, Markov chains models are developed based on DEM simulation results to show the effectiveness of the proposed approach. The obtained operators are used to estimate the spatial particle distribution and particulate mixing as examples of collective dynamic features of particulate systems.

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► The collective dynamics of a particulate system is modeled using Markov chains.
► The model can be constructed using small number of tracer trajectories.
► The particulate mixing under time-varying operating conditions can be estimated.
► This approach can be applied to large scale particulate systems.