Turbidity currents propagating down an inclined slope: particle auto-suspension

Abstract

The turbidity current (TC), a ubiquitous fluid–particle coupled phenomenon in the natural environment and engineering, can transport over long distances on an inclined terrain due to the suspension mechanism. A large-eddy simulation and discrete element method coupled model is employed to simulate the particle-laden gravity currents over the inclined slope in order to investigate the auto-suspension mechanism from a Lagrangian perspective. The particle Reynolds number in our TC simulation is $0.01\sim 0.1$ and the slope angle is $1/20 \sim 1/5$ . The influences of initial particle concentration and terrain slope on the particle flow regimes, particle movement patterns, fluid–particle interactions, energy budget and auto-suspension index are explored. The results indicate that the auto-suspension particles predominantly appear near the current head and their number increases and then decreases during the current evolution, which is positively correlated with the coherent structures around the head. When the turbidity current propagates downstream, the average particle Reynolds number of the auto-suspension particles remains basically unchanged, and is higher than that of other transported particles. The average particle Reynolds number of the transported particles exhibits a negative correlation with the Reynolds number of the current. Furthermore, the increase in particle concentration will enhance the particle velocity, which allows the turbidity current to advance faster and improves the perpendicular support, thereby increasing the turbidity current auto-suspension capacity. Increasing slope angle will result in a slightly larger front velocity, while the effect of that on the total force is insignificant.

Particle segregation within bidisperse turbidity current evolution

“…In general, in the TC evolution, the majority of the particle gravitational potential energy is converted into fluid potential energy and dissipated energy, only a small amount of energy is converted into the kinetic energy of the fluid and particle phases, which is consistent with previous understandings (Xie et al. 2022, 2023).

Figure 22.Time evolution of energy components for all cases: ( a ) particle gravitational potential energy ; ( b ) fluid potential energy ; ( c ) available potential energy ; ( d ) fluid kinetic energy ; ( e ) particle kinetic energy ; and ( f ) dissipated energy .

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“…We employed the large-eddy simulation (LES) to simulate the TC in this study and we have previously confirmed its applicability in our previous studies (Xie et al. 2022, 2023). The main reason is that LES produces more accurate flow results than Reynolds-averaged Navier–Stokes simulations and has higher computational efficiency than direct numerical simulations (Meiburg et al.…”

“…We study the bidisperse TCs by a computational fluid dynamics-discrete element method (CFD-DEM) model, which incorporates the benefits of both Eulerian and Lagrangian models into account, with a fast solution for the fluid phase and an accurate tracking of discrete particles. We employed the large-eddy simulation (LES) to simulate the TC in this study and we have previously confirmed its applicability in our previous studies (Xie et al 2022(Xie et al , 2023. The main reason is that LES produces more accurate flow results than Reynolds-averaged Navier-Stokes simulations and has higher computational efficiency than direct numerical simulations (Meiburg et al 2015).…”

“…The contact force, as the key to particle collisions, has showed its non-negligible effect on particle transport in our previous TC study (Xie et al. 2023). Here we employ different contact force thresholds to traverse and retrieve all the transported particles to obtain the contact force magnitude and the corresponding particle's proportion (), as shown in figure 14.…”

“…Under the CFDEMcoupling framework, the simulation of the fluid phase is coded by OpenFOAM, and the discrete phase is implemented by the LIGGGHTS DEM model. In our previous study (Xie et al 2022(Xie et al , 2023, we have validated this TC CFD-DEM model and applied it to explore the autosuspension process and fluid-particle interaction regimes. Note that the contribution of a particle within a given fluid cell to the cell-averaged variables is weighted by the fraction of this particle's volume that is indeed within this cell to this particle's volume.…”