A Coupled CFD–DEM Simulation of Slurry Infiltration and Filter Cake Formation during Slurry Shield Tunneling

Abstract

Tunneling in highly permeable ground using a slurry shield machine can be challenging because it is difficult to form the so-called filter cake on the tunnel face to transport the support pressure. Consequently, destructive accidents might happen, such as face instability and water inrush. How to form an efficient filter cake in time is crucial during engineering practice, especially in ground with high permeability. Various theoretical and experimental analyses regarding the formation of filter cakes have been conducted. However, due to the complexity of this problem, which has to incorporate the mechanical and hydraulic behaviors of the fluid-solid mixture system, few numerical simulations are found in the literature. In this paper, with the aid of a newly developed numerical tool, a coupled CFD (computational fluid dynamics)-DEM (discrete element method) simulation is established to study the slurry infiltration and filter cake formation during slurry shield tunneling. The slurry infiltration process is simulated by modelling the scheme of the infiltration column test, in which sedimentation behaviors of slurry particles are captured and compared with experimental results. The results show that the sedimentation behaviors of the slurry particles and filter cake formation phenomenon are well captured by simulations and in accordance with the experiments, which indicates the robustness of the coupled CFD-DEM simulation used in present work.

Fluid-particle interaction regimes during the evolution of turbidity currents from a coupled LES/DEM model

“…The study employs a coupled Large Eddy Simulation -Discrete Element Method (LES-DEM) model, provided and coded by previous studies (e.g., Kloss et al, 2012). The model belongs to CFD-DEM models, which have had considerably improved our understanding of particle-laden flows in recent years (e.g., Tsuji et al, 1993;Peng et al, 2014;Tsuji et al, 2008;Deen et al, 2007;Smuts, 2015;Deshpande et al, 2019;Zhu et al, 2007Zhu et al, , 2008Jing et al, 2016;Jing et al, 2018;Zhang and Yin, 2018), most notably sediment transport (Zhao and Shan, 2013;Li and Tao, 2018;Li and Zhao, 2018;Sun et al, 2018;Xiao, 2016a, 2016b;Furbish and Schmeeckle, 2013;Schmeeckle, 2014;Zhao et al, 2014;Lu et al, 2019;Zhang et al, 2020;Durán et al, 2012;Maurin et al, 2015;Maurin et al, 2018;Pähtz and Durán, 2018a, 2018b, though their application to TCs have been rather limited (Meiburg et al, 2015;Biegert et al, 2017).…”

Predicting the performance of pressure filtration processes by coupling computational fluid dynamics and discrete element methods

“…To investigate the impact of fines on filtration behavior, we performed simulations with bimodal particle distributions comprised of large particles having a fixed, monodisperse diameter d p,pb = 1.8 mm and smaller particles having an adjustable, monodisperse diameter d p,f . Increasingly small fines were introduced by altering the diameter of the small particles such that as dp,pbdp,f=3, 6 or 12 consistent with other studies (Min et al, 2013; Zhang and Yin, 2018) while maintaining the total volumes of the large particles and the small particles constant. Simulations were performed by first allowing the large particles to sediment under the force of gravity to form a relatively porous cake.…”

“…The modified volume-averaged Navier-Stokes equations for incompressible viscous flow were used to account for the effect of a secondary particulate phase mixed on the primary fluid phase (Kloss et al, 2012), ∂ε∂t+∇⋅(εuf)=0 ∂(εuf)∂t+∇⋅(εufuf)=−ε∇pρf+∇⋅τ−Rpf where ρ f and u f are the density and velocity of the fluid, respectively, ε is the void space occupied by the liquid, the quantity εu f is the averaged macroscopic velocity (Sören and Jürgen, 2012), τ is the stress tensor for the fluid phase, and R pf is the particle-fluid momentum exchange term calculated for each computational cell based on particle-based drag forces (Kloss et al, 2012). Many studies stress the importance of proper selection of the mesh scheme to improve accuracy (Peng et al, 2014; Volk et al, 2018; Zhang and Yin, 2018), the cell size was chosen between two to four times the particle Sauter mean diameter. Since the cell size used was larger compared to the particle size, this approach did not fully resolve flow around each individual particle and therefore did not provide insights into the intrinsic flow behavior around complex granular structures.…”

“…The coupled CFD-DEM model was realized in the open-source framework CFDEMcoupling (Goniva et al, 2012), which integrates the CFD solver OpenFOAM® (Open Source Field Operation and Manipulation) (www.openfoam.org) and LIGGGHTS (LAMMPS Improved For General Granular and Granular Heat Transfer Simulations) (Hager et al, 2014). LIGGGHTS generates particles and calculate their motions governed by Newton’s law of motion and OpenFOAM® applies the PISO (Pressure-Implicit with Splitting of Operators) scheme to solve mass and momentum conservation equations in Eulerian form (Zhang and Yin, 2018). CFDEMcoupling exchanges the particle-fluid interaction data between LIGGGHTS and OpenFOAM® at every 100 DEM time steps (Fig.…”

“…Schematic representation of the coupling between the computational fluid dynamics (CFD) and discrete element method (DEM) codes showing initial particle and mesh generation steps. Adapted from Zhang and Yin (2018).…”