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 $\mathit{Bo}⩽0.1$ 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 $t_r$ 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.

颗粒排放流的研究在工业过程中具有重要意义，尤其是湿粘性颗粒。本文采用离散元法（DEM）、黏聚力模型和粘滞力模型，研究了三维填充床黏性体系排出流动特性的影响参数。颗粒之间的内聚力由液桥模型描述，键数Bo和液体含量W用于研究内聚行为。放电效率随着颗粒粘度的增加而降低。什么时候$\mathit{博}⩽0.1$ 或者 $宽⩽6\%$，料斗内颗粒沿重力方向的均匀性与非粘性颗粒基本相同，但粘性颗粒在中心区域有较大的水平速度波动。停留时间${吨}_r$粒子数随着内聚力的增加而增加。研究结果有助于更好地了解粘性粒子的放电特性，为反应堆堆芯的放电行为提供参考，特别是在不利破坏条件下。基于仿真数据，提出了停留时间的深度神经网络（DNN）预测模型。提出了DNN预测模型来训练模拟数据并根据每个粒子的初始坐标预测粒子在料斗中的停留时间。经过超过60万条数据的训练验证，准确率达到98%以上，满足工程计算需要。