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• W3037970129 abstract "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 Nk22/Nk21 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)." @default.
• W3037970129 created "2020-07-02" @default.
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• W3037970129 date "2021-04-01" @default.
• W3037970129 modified "2023-10-16" @default.
• W3037970129 title "Computational fluid dynamic–discrete element method coupling analysis of particle transport in branched networks" @default.
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• W3037970129 doi "https://doi.org/10.1016/j.partic.2020.05.005" @default.
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