DEM study of flow characteristics of wet cohesive particles in packed bed

Abstract

The study of particle discharge flow is significant in industrial processes,especially wet cohesive particles. In this paper, the discrete element method (DEM), cohesion model and the viscous force model are used to study the influencing parameters of the discharge flow characteristics of cohesive system in three-dimensional packed bed. The cohesion between particles is described by liquid bridge model, the Bond number Bo and liquid content W are used to study the cohesive behaviour. The discharge efficiency decreases with the increase of particle viscosity. When Bo⩽0.1 or W⩽6%, the uniformity of the particles in the hopper along the direction of gravity is basically the same of non-cohesive particle, but cohesive particles have a greater horizontal velocity fluctuations in the central area. The residence time tr of particles increases with the increase of cohesive force. The research results help to better understand the discharge characteristics of cohesive particle and provide a reference for the discharge behavior of the reactor core, especially under adverse damage conditions. Based on the simulation data, a Deep Neural Network (DNN) prediction model of residence time is proposed. The DNN prediction model was proposed to train the simulated data and predict the residence time of the particles in hopper based on the initial coordinates of each particle. After training and verification on more than 600,000 data, it has reached an accuracy rate of over 98% and meets the needs of engineering calculation.