An understanding of the particle transport characteristics in a branched network helps to predict the particle distribution and prevent undesired plugging in various engineering systems. Quantitative analysis of particle flow characteristics is challenging in that experiments are expensive and particle flow is difficult to detect without disturbing the flow. To overcome this difficulty, man-made fractal tree-like branched networks were built, and a coupled computational fluid dynamic and discrete element method model was applied. A series of numerical simulations was carried out to analyze the influence of fractal structure parameters of networks on the particle flow characteristics. The joint influence of inertial, shunt capacity and superposition from upstream branches on particle flow was investigated. The injection position at the inlet determined the particle velocity and its future flow path. The particle density ratio, particle size and bifurcation angle had a greater influence on the shunting of K2 branches than that in the K1 level and N_k22/N_k21 reached a maximum at 60°. Compared with a network with an even number of branches, there was a preferential branch when the branch number was odd. The preferential branch effect or asymmetry degree of the level (K2) branches had a more significant impact on particle shunting than that from the upstream branches (K1).

了解分支网络中的粒子传输特性有助于预测粒子分布并防止在各种工程系统中出现意外堵塞。颗粒流特征的定量分析具有挑战性，因为实验昂贵且难以在不干扰流动的情况下检测颗粒流。为了克服这个困难，建立了人造的分形树状分支网络，并应用了耦合的计算流体动力学和离散元方法模型。进行了一系列数值模拟，以分析网络的分形结构参数对颗粒流动特性的影响。研究了惯性，分流能力和上游分支的叠加对颗粒流的联合影响。入口处的注射位置决定了粒子速度及其未来的流动路径。颗粒密度比，粒径和分叉角对K2分支的分流的影响大于K1水平和N _{k 22} / N _{k 21}在60°达到最大值。与分支数为偶数的网络相比，分支数为奇数时存在优先分支。水平分支（K2）的优先分支效应或不对称程度对颗粒分流的影响比上游分支（K1）更大。