Microscopic mechanism of particle detachment in granular materials subjected to suffusion in anisotropic stress states

Abstract

Suffusion refers to a special form of internal erosion characterized by the selective erosion of the finest particles of a soil under the action of an internal fluid flow. In this work, the microscopic mechanism of particle detachment in binary mixtures subjected to suffusion under different flow directions is analyzed. We use the coupled lattice Boltzmann method (LBM) and discrete element method (DEM) to simulate the suffusion process in a granular sample subjected to an anisotropic stress state. When the macro flow direction is aligned with the principal direction of compression, it is found that the fluid flow is more intense, which increases erosion.The stress anisotropy also influences the detachment direction that is not necessarily correlated with the macroscopic flow direction. The sample's anisotropic stress state is responsible for directional variations in microstructural properties during the suffusion under different flow directions. From a micro scale point of view, a contact sliding index P and a particle detachment index Δ are defined to demonstrate that fluid-induced sliding dominates for particles about to detach.

A resolved CFD–DEM investigation into the onset of suffusion: effect of confining pressure and stress anisotropy

“…where p is an intermediate pressure; û𝑓 and 𝐮 𝑛−1 𝑓 are the interim fluid velocities at the current and the previous steps, respectively. Thereafter, the interim fluid velocity û𝑓 intersecting with particles is corrected to be proportional to the corresponding particle velocity ū𝑓 according to the void fraction of fluid cells, which is equivalent to adding an excess force term F e to the left side of Equation (12):…”

“…In recent years, several coupling methods have been developed to simulate particle-fluid interaction problems, such as the coupled computational fluid dynamics and DEM (CFD-DEM), coupled Lattice Boltzmann Method and DEM (LBM-DEM), and coupled direct numerical simulation and DEM (DNS-DEM). [11][12][13] Among them, CFD-DEM method can be categorized into three types based on the resolution of the flow field, namely the un-resolved, 14 semi-resolved, 15 and fully resolved schemes. 16,17 For the unresolved CFD-DEM scheme, the flow field is volume-averaged within a local space by solving the locally averaged Navier-Stokes equation, and the sizes of fluid cells have to be a few times greater than the particle sizes.…”

Effect of particle shape and bedding angle on suffusion in gap‐graded granular soils by coupled CFD‐DEM method

“…With the development of particle scale techniques (e.g., discrete element method [DEM] 15 ), numerical simulations could provide a feasible way to track the movement of every single particle at an elementary scale. Most recently, the multiphase coupled parallel calculation methods incorporating the fluid in DEM, 16 including the coupled lattice Boltzmann-DEM 17,18 (LMB-DEM), coupled pore-scale finite volume-DEM 19 (PFV-DEM), and coupled computational fluid dynamics-DEM 20 (CFD-DEM), have been raised much attention, among which, the unresolved CFD-DEM method has been widely adopted in the study related to internal erosion. [21][22][23][24][25][26][27][28][29] Unlike the fully resolved method where the size of a CFD cell has to be at least 5−10 times smaller than the particle size to precisely calculate the fluid-particle interface, the unresolved coupling allows a much coarser fluid mesh, which is more appropriate to the geotechnical applications.…”

Modeling suffusion of ideally gap‐graded soil

“…The coupling simulation of DEM‐CFD, in which the fluid flow is still simulated by the continuum method, is recently being used to describe the suffusion mechanism, especially in small‐scale tests 20,21 . Moreover, the DEM‐LBM has recently been developed for the further investigation of the suffusion behavior such that the high resolution of the fluid flow and the fluid‐particle flow interaction could be better illustrated 22 . Nevertheless, some difficulty remains in the scaling up of the simulation due to the calculation method, which requires a higher computational cost due to the increase in the number of distinct particles.…”