Simulation and modeling of segregating rods in quasi‐2D bounded heap flow

Zhao, Yongzhi; Xiao, Hongyi; Umbanhowar, Paul B.; Lueptow, Richard M.

doi:10.1002/aic.16035

Cited by 36 publications

(29 citation statements)

References 66 publications

(230 reference statements)

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“…The simplest explanation for segregation relies on the idea, for size-disperse mixtures, that small particles fall through voids generated between large particles and accumulate in the lower regions of the flowing layer, while large particles are forced upward by concentrated regions of small particles. Quantitative models of segregation have been developed and have now reached a state where accurate prediction is possible for a range of material properties and flow geometries [7,8,32,[35][36][37][38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Asymmetric concentration dependence of segregation fluxes in granular flows

et al. 2018

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We characterize the local concentration dependence of segregation velocity and segregation flux in both size and density bidisperse gravity-driven free-surface granular flows as a function of the particle size ratio and density ratio, respectively, using discrete element method (DEM) simulations. For a range of particle size ratios and inlet volume flow rates in size-bidisperse flows, the maximum segregation flux occurs at a small particle concentration less than 0.5, which decreases with increasing particle size ratio. The segregation flux increases up to a size ratio of 2.4 but plateaus from there to a size ratio of 3. In density bidisperse flows, the segregation flux is greatest at a heavy particle concentration less than 0.5 which decreases with increasing particle density ratio. The segregation flux increases with increasing density ratio for the extent of density ratios studied, up to 10. We further demonstrate that the simulation results for size driven segregation are in accord with the predictions of the kinetic sieving segregation model of Savage and Lun [1]. arXiv:1806.07993v1 [physics.flu-dyn]

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Section: Introductionmentioning

confidence: 99%

Asymmetric concentration dependence of segregation fluxes in granular flows

et al. 2018

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“…It is likely that these relations can be determined either through experiment or through DEM simulation as is the case for size and density segregation. For example, we have recently determined the percolation velocity characteristics (specifically S ) for rod‐like particles using DEM simulations of super‐ellipsoid particles . Thus, using a percolation velocity based on a kinetic sieving model, thereby incorporating the shear rate, along with a dependence on the linear combination of the local concentration of surrounding particles having a particle‐specific segregation length scale S as represented in Eq.…”

Section: Discussionmentioning

confidence: 99%

Modeling segregation of polydisperse granular materials in developing and transient free‐surface flows

et al. 2019

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Predicting segregation and mixing of polydisperse granular materials in industrial processes remains a challenging problem. Here, we extend the application of a general predictive continuum model that captures the effects of segregation, diffusion, and advection in two ways. First, we consider polydisperse segregating flow in developing steady segregation and in developing unsteady segregation. In both cases, several terms in the model that were zero in the previously examined case of fully developed streamwise‐periodic steady segregation in a chute are now non‐zero, which makes application of the model substantially more challenging. Second, we apply the polydisperse approach to density polydisperse materials with the same particle size. Predictions of the model agree quantitatively with experimentally validated discrete element method (DEM) simulations of both size polydisperse and density polydisperse mixtures having uniform, triangular, and log‐normal distributions. © 2018 American Institute of Chemical Engineers AIChE J, 65: 882–893, 2019

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“…The symplectic Euler integration algorithm [63][64][65] is used to update particle positions and velocities. The simulation time step is chosen to be it is smaller than the critical time step, which is one-tenth of the minimal natural oscillation period of the spring-mass system [41,[66][67][68].…”

Section: Appendix a Discrete Element Methods Simulationsmentioning

confidence: 99%

Continuum modelling of segregating tridisperse granular chute flow

et al. 2018

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Segregation and mixing of size multidisperse granular materials remain challenging problems in many industrial applications. In this paper, we apply a continuum-based model that captures the effects of segregation, diffusion and advection for size tridisperse granular flow in quasi-two-dimensional chute flow. The model uses the kinematics of the flow and other physical parameters such as the diffusion coefficient and the percolation length scale, quantities that can be determined directly from experiment, simulation or theory and that are not arbitrarily adjustable. The predictions from the model are consistent with experimentally validated discrete element method (DEM) simulations over a wide range of flow conditions and particle sizes. The degree of segregation depends on the Péclet number, , defined as the ratio of the segregation rate to the diffusion rate, the relative segregation strength between particle species and, and a characteristic length , which is determined by the strength of segregation between smallest and largest particles. A parametric study of particle size, , and demonstrates how particle segregation patterns depend on the interplay of advection, segregation and diffusion. Finally, the segregation pattern is also affected by the velocity profile and the degree of basal slip at the chute surface. The model is applicable to different flow geometries, and should be easily adapted to segregation driven by other particle properties such as density and shape.

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Simulation and modeling of segregating rods in quasi‐2D bounded heap flow

Cited by 36 publications

References 66 publications

Asymmetric concentration dependence of segregation fluxes in granular flows

Asymmetric concentration dependence of segregation fluxes in granular flows

Modeling segregation of polydisperse granular materials in developing and transient free‐surface flows

Continuum modelling of segregating tridisperse granular chute flow

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