Computing drag and interactions between fluid and polydisperse particles in saturated granular materials
Computing drag and interactions between fluid and polydisperse particles in saturated granular materials
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DOI：
10.1016/j.compgeo.2019.103210
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发表时间：
2020
影响因子：
5.3
通讯作者：
C. Knight;C. O’Sullivan;Berend G. M. van Wachem;D. Dini
中科院分区：
文献类型：
--
作者：
C. Knight;C. O’Sullivan;Berend G. M. van Wachem;D. Dini
渗流引起的岩土不稳定性和过滤过程的基础数值研究取决于对渗透流体施加在土壤颗粒上的力的准确预测。迄今为止，地质力学中记载的耦合离散元法 (DEM) 模拟通常使用计算流体动力学 (CFD) 模型来模拟流体流动，该模型采用包含多个颗粒的流体单元。经验阻力模型用于利用流动雷诺数和流体单元孔隙率来预测流体-颗粒相互作用力。在粒子尺度上对这些模型预测的力进行实验验证并非易事。该贡献使用高分辨率浸入边界方法来模拟多分散球体样品中各个空隙内的流体流动，以准确确定流体-颗粒相互作用力。现有的阻力模型很难捕捉低雷诺数流动样本中单个颗粒上的力。提出了另一种方法，其中应用激进的 Voronoi 曲面细分来估计每个颗粒的局部固体体积分数；可以采用该局部固体分数与现有表达式结合来估计阻力。这种基于曲面细分的方法可以更准确地预测流体粒子相互作用力。
Fundamental numerical studies of seepage induced geotechnical instabilities and filtration processes depends on accurate prediction of the forces imparted on the soil grains by the permeating fluid. Hitherto coupled Discrete Element Method (DEM) simulations documented in geomechanics have most often simulated the fluid flow using computational fluid dynamics (CFD) models employing fluid cells that contain a number of particles. Empirical drag models are used to predict the fluid-particle interaction forces using the flow Reynolds number and fluid cell porosity. Experimental verification of the forces predicted by these models at the particle-scale is non-trivial. This contribution uses a high resolution immersed boundary method to model the fluid flow within individual voids in polydisperse samples of spheres to accurately determine the fluid-particle interaction forces. The existing drag models are shown to poorly capture the forces on individual particles in the samples for flow with low Reynolds number values. An alternative approach is proposed in which a radical Voronoi tesselation is applied to estimate a local solids volume fraction for each particle; this local solids fraction can be adopted in combination with existing expressions to estimate the drag force. This tessellation-based approach gives a more accurate prediction of the fluid particle interaction forces.