Despite the wide range of applications, the traditional computational fluid dynamics-discrete element method (CFD-DEM) simulations have been running into bottlenecks when dealing with large scale systems. To overcome this issue, an augmented coarse-grained CFD-DEM approach which combines the reduced particle stiffness model and the coarse-grained CFD-DEM is developed to multi-dimensionally reduce the computation load. In spatial scale, several original particles are lumped into a computational parcel based on the coarse-grained ratio while the time step of solid phase is enlarged in temporal scale according to the reduced particle stiffness ratio. The accuracy and efficiency of the augmented coarse-grained CFD-DEM are quantitatively evaluated in fluidized beds, and different gas–solid characteristics are obtained via comparing with experimental measurements and traditional CFD-DEM. The results show that the augmented method contributes a significant improvement in computational efficiency while a little loss in numerical accuracy. Generally, the computational efficiency of the augmented coarse-grained CFD-DEM increases with the increase of coarse-grained ratio while it enhances with the decrease of the reduced particle stiffness ratio. This augmented coarse-grained CFD-DEM is expected to be a promising tool to optimize gas–solid flow dynamics in large-scale dense particulate systems.

尽管应用范围很广，但是在处理大型系统时，传统的计算流体动力学离散元方法（CFD-DEM）模拟已成为瓶颈。为了克服这个问题，开发了一种结合了降低的粒子刚度模型和粗糙的CFD-DEM的增强的粗粒度CFD-DEM方法，以多维方式减少计算量。在空间尺度上，几个原始粒子基于粗粒度比被集中到一个计算包中，而固相的时间步长则根据减小的粒子刚度比在时间尺度上增大。在流化床中定量评估了增强型粗粒CFD-DEM的准确性和效率，通过与实验测量值和传统CFD-DEM进行比较，可以获得不同的气固特性。结果表明，增强方法有助于显着提高计算效率，同时在数值精度上损失很小。通常，增强的粗粒度CFD-DEM的计算效率随着粗粒度比的增加而增加，而随着减小的颗粒刚度比的降低而提高。这种增强的粗粒CFD-DEM有望成为优化大规模致密颗粒系统中气固流动动力学的有前途的工具。增大的粗粒度CFD-DEM的计算效率随着粗粒度比的增加而增加，而随着减小的颗粒刚度比的降低而提高。这种增强的粗粒CFD-DEM有望成为优化大规模致密颗粒系统中气固流动动力学的有前途的工具。增大的粗粒度CFD-DEM的计算效率随着粗粒度比的增加而增加，而随着减小的颗粒刚度比的降低而提高。这种增强的粗粒CFD-DEM有望成为优化大规模致密颗粒系统中气固流动动力学的有前途的工具。