A pore network model is built to predict pressure drop in packed beds of arbitrary‐shaped particles, using a method that consists of particle packing by the rigid body technique, pore network construction by the maximal sphere algorithm, and numerical calculation of fluid flow. The pore network model is firstly validated by comparing with experiments, Ergun‐type equations, and particle‐resolved computational fluid dynamics (CFD). The pore network model is as accurate as the particle‐resolved CFD, and is remarkably two to three orders of magnitude less computationally intensive. Then, the pore network model is used to calculate the pressure drops in the beds packed with particles of different shapes and sizes, as well as using different flow media. These calculation results prove the versatility of the pore network model. This work provides an accurate yet efficient pore network model for predicting pressure drop, which should be a powerful tool for designing packed beds.

中文翻译：

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计算任意形状颗粒填充床压降的孔网络模型

建立了一个孔隙网络模型，以预测任意形状的颗粒填充床中的压降，该方法包括采用刚体技术填充颗粒，通过最大球体算法构建孔隙网络以及对流体流动进行数值计算。首先通过与实验，Ergun型方程和颗粒解析计算流体动力学（CFD）进行比较来验证孔隙网络模型。孔隙网络模型的精度与粒子解析的CFD一样，并且计算强度显着降低了2-3个数量级。然后，使用孔隙网络模型来计算填充有不同形状和尺寸的颗粒以及使用不同流动介质的床中的压降。这些计算结果证明了孔隙网络模型的多功能性。