Computational fluid dynamics coupled with discrete element method (CFD-DEM) has been widely used to understand the complicated fundamentals inside gas–solid fluidized beds. To realize large-scale simulations, CFD-DEM integrated with coarse-grain model (CG CFD-DEM) provides a feasible solution, and has led to a recent upsurge of interest. However, when dealing with large-scale simulations involving irregular-shaped particles such as biomass particles featuring elongated shapes, current CG models cannot function as normal because they are all developed for spherical particles. To address this issue, a CG CFD-DEM for nonspherical particles is proposed in this study, and the morphology of particles is characterized by the super-ellipsoid model. The effectiveness and accuracy of CG CFD-DEM for nonspherical particles are comprehensively evaluated by comparing the hydrodynamic behaviors with the results predicted by traditional CFD-DEM in a gas–solid fluidized bed. It is demonstrated that the proposed model can accurately model gas–solid flow containing nonspherical particles, merely the particle dynamics are somewhat lost due to the scaleup of particle size. Finally, the calculation efficiency of CG CFD-DEM is assessed, and the results show that CG CFD-DEM can largely reduce computational costs mainly by improving the calculation efficiency of DEM. In general, the proposed CG CFD-DEM for nonspherical particles strikes a good balance between efficiency and accuracy, and has shown its prospect as a high-efficiency alternative to traditional CFD-DEM for engineering applications involving nonspherical particles.

中文翻译：

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气固流化床中非球形颗粒粗晶 CFD-DEM 的开发与验证

计算流体动力学与离散元法 (CFD-DEM) 相结合已被广泛用于理解气固流化床内部复杂的基本原理。为了实现大规模模拟，与粗粒度模型集成的 CFD-DEM (CG CFD-DEM) 提供了一种可行的解决方案，并引起了最近的关注热潮。然而，在处理涉及不规则形状颗粒（例如具有细长形状的生物质颗粒）的大规模模拟时，当前的 CG 模型无法正常运行，因为它们都是为球形颗粒开发的。为了解决这个问题，本研究提出了一种非球形粒子的CG CFD-DEM，并通过超椭球模型表征了粒子的形态。通过将流体动力学行为与传统 CFD-DEM 在气固流化床中预测的结果进行比较，综合评估了 CG CFD-DEM 对非球形颗粒的有效性和准确性。结果表明，所提出的模型可以准确地模拟含有非球形颗粒的气固流动，只是由于颗粒尺寸的放大，颗粒动力学有所损失。最后对CG CFD-DEM的计算效率进行了评估，结果表明CG CFD-DEM主要通过提高DEM的计算效率可以大幅度降低计算成本。一般来说，所提出的非球形粒子的 CG CFD-DEM 在效率和准确性之间取得了很好的平衡，