标题： 用于大规模DEM模拟的自适应粗粒化 显示英文标题

标题： Adaptive coarse-graining for large-scale DEM simulations

摘要： 大规模的时间和长度尺度以及工业规模模拟中涉及的大量粒子，使离散元方法（DEM）的计算成本过高。 粗粒模型可以显著降低计算需求。 然而，对于那些本质上依赖于颗粒尺寸的现象，粗粒模型无法正确预测颗粒系统的行为。 为了解决这个问题，我们开发了一种新技术，该技术基于细尺度和粗粒DEM模型的有效结合。 该方法旨在捕捉空间受限子区域中颗粒系统的细节，同时在分辨率要求较低的地方保持粗粒模型的计算优势。 为此，我们的方法通过边界条件的体积传递，在解析部分和粗粒部分之间建立双向耦合。 此外，可以结合多级粗粒化以在精度和加速之间达到最佳平衡。 这种方法使我们能够在保留关键区域特性的前提下达到大的时间和长度尺度。 此外，所提出的模型可以扩展到耦合的CFD-DEM模拟，其中CFD网格的分辨率也可以自适应地改变。 显示英文摘要

摘要： The large time and length scales and, not least, the vast number of particles involved in industrial-scale simulations inflate the computational costs of the Discrete Element Method (DEM) excessively. Coarse grain models can help to lower the computational demands significantly. However, for effects that intrinsically depend on particle size, coarse grain models fail to correctly predict the behaviour of the granular system. To solve this problem we have developed a new technique based on the efficient combination of fine-scale and coarse grain DEM models. The method is designed to capture the details of the granular system in spatially confined sub-regions while keeping the computational benefits of the coarse grain model where a lower resolution is sufficient. To this end, our method establishes two-way coupling between resolved and coarse grain parts of the system by volumetric passing of boundary conditions. Even more, multiple levels of coarse-graining may be combined to achieve an optimal balance between accuracy and speedup. This approach enables us to reach large time and length scales while retaining specifics of crucial regions. Furthermore, the presented model can be extended to coupled CFD-DEM simulations, where the resolution of the CFD mesh may be changed adaptively as well.