CHARACTERIZATION OF NEAR-WALL PARTICLE DYNAMICS BASED ON DISCRETE ELEMENT METHOD ANDARTIFICIAL NEURAL NETWORK

The interactions between the particles and the walls often have significant effects on the particle flows. In order to study the mechanism of the interactions between the particles and the walls, the discrete element method (DEM) simulations of the particle flow in the rotating drum are carried out. Based on the statistical analysis of the simulation results, the characteristics of the near-wall particle motion are shown. The results indicate that the particle translational and rotational velocity approximately satisfy the normal distribution when the friction coefficient is small. However, due to the wall effects, the axial rotational velocity deviates from the normal distribution when the friction coefficient increasing. The kinetic theory of granular flow should consider the correction of the velocity normal distribution and also the anisotropic of the velocity fluctuation when deriving the wall boundary conditions. An artificial neural network (ANN) is used to construct a function model between dimensionless particle rotational temperature and particle slip velocity and particle translational temperature, and then the influence of particle rotation can be incorporated in the conventional boundary conditions within the two-flow model. Through comprehensive DEM simulation and result analysis could provide basic data and closure models for the theoretical construction and semi-empirical correction of the wall boundary conditions.