Using the DEM-CFD method to predict Brownian particle deposition in a constricted tube

Chaumeil, Florian; Crapper, Martin

doi:10.1016/j.partic.2013.05.005

Cited by 32 publications

(23 citation statements)

References 34 publications

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“…13 and those obtained by the approximate expression given by Eq. (19), their values are almost exactly equal except for samples 3 and 4, for which there are some deviations as shown in Fig. 14.…”

Section: Preliminary Measurementsmentioning

confidence: 84%

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Numerical sedimentation particle-size analysis using the Discrete Element Method

Bravo

Pérez-Aparicio

Gómez‐Hernández

2015

Advances in Water Resources

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Sedimentation processes are generally modelled using an equivalent continuum approach based on the coupling of the Navier-Stokes and the advection-dispersion equations; the sediment concentration within an elementary fluid volume is the variable of interest. Continuous advances in computational capabilities have brought up a new type of modelling that simulates the individual motion and contact interactions of grains: the Discrete Element Method (DEM). In this work, DEM interacts with the simulation of flow using the well known one-way-coupling method, a computationally affordable approach for the time-consuming numerical simulation of the ASTM-D422, buoyancy and pipette sedimentation tests. These tests are used in the laboratory to determine the particle-size distribution of fine-grained aggregates. Five samples with different particle-size distributions are modelled by about six million rigid spheres projected on two-dimensions, with diameters ranging from 2.5 × 10 −6 m to 70 × 10 −6 m, forming a water suspension in a sedimentation cylinder. DEM simulates the particle's movement considering laminar flow interactions of hydrostatic thrust, drag and lubrication forces. The simulation provides the temporal/spatial distributions of densities and concentrations of the suspension.The numerical simulation cannot replace the laboratory tests since it needs the final granulometry as initial data; but, as the results show, these simulations can identify the strong and weak points of each method and eventually recommend useful variations and draw conclusions on their validity, aspects very difficult to achieve in the laboratory.

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Section: Preliminary Measurementsmentioning

confidence: 84%

“…In this work, flow and particles are coupled using the one-way-coupling method, see [19]. This technique substantially reduces computation, considering that flow does modify particle movement but particles do not substantially modify flow: see [20] for the application of this technique to the analysis of inclined sediment beds.…”

Section: Introductionmentioning

confidence: 99%

Numerical sedimentation particle-size analysis using the Discrete Element Method

Bravo

Pérez-Aparicio

Gómez‐Hernández

2015

Advances in Water Resources

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“…• To use the rolling resistance model by Dominik and Tielens [2,3], Krijt et al [4] (equation (18) to (22)) for reduced particle stiffness simulations, only the term (a/a 0 ) 3/2 should be changed to use modified values. That way, the border between rolling and sticking remains the same.…”

Section: Discussionmentioning

confidence: 99%

“…Common for the approaches is that they rely on a surface energy density γ to describe the adhesive force. For small λ T , the contact independent van der Waals formulation by Hamaker [16] is used in studies such as [8,9,17,18] to account for the adhesive force. For higher λ T (softer particles), the contact dependent adhesive JKR force proposed by Johnson et al [1] is used in studies such as [19,20].…”

Section: Adhesive Contact Between Particles and A Surfacementioning

confidence: 99%

On the adhesive JKR contact and rolling models for reduced particle stiffness discrete element simulations

et al. 2017

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Discrete Element Method (DEM) simulations are a promising approach to accurately predict agglomeration and deposition of micron-sized adhesive particles. However, the mechanistic models in DEM combined with high particle stiffness for most common materials require time step sizes in the order of nano seconds, which makes DEM simulations impractical for more complex applications. In this study, analytically derived guidelines on how to reduce computational time by using a reduced particle stiffness are given. The guidelines are validated by comparing simulations of particles with and without reduced particle stiffness to experimental data. Then two well-defined test cases are investigated to show the applicability of the guidelines. When introducing a reduced particle stiffness in DEM simulations by reducing the effective Young's modulus from E to E mod , the surface energy density γ in the adhesive Johnson-Kendall-Roberts (JKR) model by Johnson et al. [1] should be modified as γ mod = γ (E mod /E) 2/5. Using this relation, the stick/rebound threshold remains the same but the collision process takes place over a longer time period, which allows for a higher time step size. When rolling motion is important, the commonly used adhesive rolling resistance torque model proposed by Dominik and Tielens [2, 3], Krijt et al. [4] can be used by modifying the contact radius ratio (a/a 0) 3/2 to (a mod /a 0,mod) 3/2 , whilst keeping the other terms unaltered in the description of the rolling resistance torque M r,mod = −4F C (a/a 0) 3/2 ξ. Furthermore, as the particle stiffness is reduced from E to E mod , the time period for collisions (or oscillations when particles stick upon impact) ∆t col is found to vary as ∆t col,mod = ∆t col (E/E mod) 2/5. As the collision duration and the collision time step size are directly related, this criterion can be used to estimate how much the time step size can be changed as a reduced particle stiffness is introduced. Introducing particles with a reduced particle stiffness has some limitations when strong external forces are acting to break-up formed agglomerates or re-entrain particles deposited on a surface out into the free stream. Therefore, care should be taken in flows with high local shear to make sure that an external force, such as a fluid drag force, acting to separate agglomerated particles, is several orders of magnitude lower than the critical force required to separate particles.

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“…Currently, the calculation methods of fluid-solid coupling are divided into three categories: the Euler-Eulerian method, the Euler-Lagrangian method, and the Lagrangian-Lagrangian method. Because the Euler-Lagrangian method can accurately reflect the airflow-dust coupling characteristics [19][20][21][22], this method is increasingly widely used in the simulation of fluid-solid two-phase flow. Torañod et al [23] studied the migration behavior of airflow and air-dust flow through a 14.7-m 2 cross section on a fully mechanized excavation surface under strong exhaust ventilation conditions by the Euler-Lagrangian method.…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation of the Tar Mist and Dust Movement Process in a Low-Temperature Dry Distillation Furnace

Zhang

Hui

Liang

et al. 2020

Journal of Chemistry

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In the low-temperature dry distillation of low-rank coal, the important liquid product of coal tar is produced, but its quality and utilization rate are degraded by entrained dust. e movement of coal tar and dust in the furnace is a key factor in causing particles such as dust to mix with coal tar. erefore, the Euler-Lagrangian method is used to simulate the two-phase motion process of gas, tar, and dust in a furnace. By considering the effects of tar particle size, dust particle size, gas velocity, tar density, and dust density, the motion process mechanism is revealed, enabling the dust content in coal tar to be reduced and the quality improved. e results indicate that tar particles with sizes less than 0.20 mm can be removed from the furnace by gas, and the smaller the particle size is, the shorter the time required for removal. Dust particles greater than 0.18 mm in size cannot be completely removed from the furnace. As the gas velocity increases, the time required for complete removal of the tar mist and dust gradually decreases. When the speed is 0.70 m/s, all tar mist is removed, although some particles remain. Tar mist with a density of more than 900 kg/m 3 can be extensively removed, but dust with a density of more than 1400 kg/m 3 is difficult to remove and remains in the furnace. Finally, particle size distribution experiments in the product were conducted to verify the accuracy of the numerical simulation.

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Using the DEM-CFD method to predict Brownian particle deposition in a constricted tube

Cited by 32 publications

References 34 publications

Numerical sedimentation particle-size analysis using the Discrete Element Method

Numerical sedimentation particle-size analysis using the Discrete Element Method

On the adhesive JKR contact and rolling models for reduced particle stiffness discrete element simulations

Numerical Simulation of the Tar Mist and Dust Movement Process in a Low-Temperature Dry Distillation Furnace

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