Review and implementation of CFD-DEM applied to chemical process systems

Golshan, Shahab; Sotudeh‐Gharebagh, Rahmat; Zarghami, Reza; Mostoufi, Navid; Blais, Bruno; Kuipers, J.A.M.

doi:10.1016/j.ces.2020.115646

Cited by 140 publications

(66 citation statements)

References 154 publications

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“…Indeed, this was also confirmed in the analysis of scaling in simulated experiments of uniaxial compression tests (stress-strain relationship) by Thakur et al [74]. They reported correct scaling of the results with a linear up-scaling of the normal stiffness with the coarse graining degree, which is the case here, as proved by the constant E eq and the linear dependence on f of both R eq and δ n in Equation (10).…”

Section: Contact Interaction: Hertz-based Modellingsupporting

confidence: 86%

“…At the other extreme, i.e., the small-scale interaction between the fluid and individual particles, discrete-continuum (or Eulerian-Lagrangian) simulations based on the discrete element method appeared, thanks to the progress in the computational power, giving rise to the CFD-DEM (Computational Fluid Dynamics-Discrete Element Method) approach [6]. The recognition of the discrete nature of the granular medium proved to be a key ingredient and allowed important new features of fluidized systems to be captured, as discussed in several reviews (e.g., [7][8][9][10][11][12]).…”

Section: Introductionmentioning

confidence: 99%

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Coarse-Grain DEM Modelling in Fluidized Bed Simulation: A Review

2021

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In the last decade, a few of the early attempts to bring CFD-DEM of fluidized beds beyond the limits of small, lab-scale units to larger scale systems have become popular. The simulation capabilities of the Discrete Element Method in multiphase flow and fluidized beds have largely benefitted by the improvements offered by coarse graining approaches. In fact, the number of real particles that can be simulated increases to the point that pilot-scale and some industrially relevant systems become approachable. Methodologically, coarse graining procedures have been introduced by various groups, resting on different physical backgrounds. The present review collects the most relevant contributions, critically proposing them within a unique, consistent framework for the derivations and nomenclature. Scaling for the contact forces, with the linear and Hertz-based approaches, for the hydrodynamic and cohesive forces is illustrated and discussed. The orders of magnitude computational savings are quantified as a function of the coarse graining degree. An overview of the recent applications in bubbling, spouted beds and circulating fluidized bed reactors is presented. Finally, new scaling, recent extensions and promising future directions are discussed in perspective. In addition to providing a compact compendium of the essential aspects, the review aims at stimulating further efforts in this promising field.

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Section: Contact Interaction: Hertz-based Modellingsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Coarse-Grain DEM Modelling in Fluidized Bed Simulation: A Review

2021

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“…Furthermore, modelling time‐progressive phenomena, like agglomeration, are straight forward in the soft‐sphere formulation. [ 20 ] The translational and rotational velocities of individual particles are calculated from Newton's second law of motion [ 21 ] :

m_{i} \frac{d {\overset{⇀}{v}}_{i}}{dt} = - ε_{s} \nabla p + m_{i} \overset{g}{true} + {\overset{⇀}{F}}_{d, i} + {\overset{⇀}{F}}_{c . i} + {\overset{⇀}{F}}_{e . i}

I_{i} \frac{d {\overset{⇀}{ω}}_{i}}{dt} = {\overset{⇀}{T}}_{i}

in which m i ,

{\overset{⇀}{v}}_{i}

{\overset{⇀}{ω}}_{i}

, and I i are mass, translational velocity, rotational velocity, and moment of inertia of particle i , respectively. In Equation ), − ε s ∇ p is the pressure gradient force,

{\overset{⇀}{F}}_{d, i}

is the fluid drag force,

m_{i} \overset{g}{true}

is the gravity force,

{\overset{⇀}{F}}_{c . i}

is the collision force, and

{\overset{⇀}{F}}_{e . i}

is the electrostatic force.…”

Section: Modellingmentioning

confidence: 99%

CFD‐DEM simulation of wall sheeting and particles charge in fluidized beds

et al. 2021

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The effect of the electrostatic charge of particles was investigated in fluidized beds with conductive and nonconductive walls. For this purpose, changes in the wall surface charge density and particle contact time near the wall were examined. Computational fluid dynamics/discrete element method (CFD‐DEM) was utilized to simulate the accumulation of particle and wall‐induced electrostatic charges in a quasi‐two‐dimensional fluidized bed. Particles and walls were initially considered neutral and gained charge with time. The simulations showed that aggregated particles are formed due to the presence of the opposite electrostatic charges. Agglomerates are produced by joining the aggregated particles together. Agglomerates have a layered‐like structure and are made of positive and negative particles. Accumulation of these agglomerates and aggregated particles on the wall forms wall sheeting, which has a layered‐like structure. In other words, particles do not accumulate on the wall individually. A significant accumulation of particles near the wall was seen in the presence of electrostatic charges compared with the neutral bed. Also, the contact time of particles on the wall in the conductive case was more prolonged than in the nonconductive case. The results indicate that the CFD‐DEM approach is a helpful technique for predicting particles behaviour in the presence of the electrostatic charge.

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“…The soft sphere model of the DEM method was used to study the movement of particles in the high-pressure tank [21][22][23]. The model of indirect contact force between particles was represented by the spring damper friction device.…”

Section: Physicalmentioning

confidence: 99%

Investigation into the Mechanism of the Steel Particle Feeding Process: A Numerical and Experimental Study

Xing

Wang³

et al. 2021

Geofluids

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The particle feeding system is a prerequisite for the realization of particle impact drilling technology. Because of the high density, the storage and flow of the steel particle are different from those of the other nonviscous particles. The differential equation of the particle movement was built with the liquid bridge force model and the discrete element method. The dynamic movement process and the distribution state of particles in the high-pressure tank were analyzed. For 1 mm steel particles, the mass flow rate decreased with the increase in water content. For 2 mm and 3 mm steel particles, the water content of 15% and 20% was the dividing point of the mass flow rate from increasing to decreasing. When the water content was 10% and 20%, the mass flow rate increased with the steel particle size. But when the water content was 30% and 40%, the mass flow rate decreased with the steel particle size. The study of the control mechanism of the uniformity and stability of particles showed that the funnel flow was the major reason causing the instability and blocking of the feeding process. This research results can provide a basis for the further improvement of the differential pressure feeding system.

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Review and implementation of CFD-DEM applied to chemical process systems

Cited by 140 publications

References 154 publications

Coarse-Grain DEM Modelling in Fluidized Bed Simulation: A Review

Coarse-Grain DEM Modelling in Fluidized Bed Simulation: A Review

CFD‐DEM simulation of wall sheeting and particles charge in fluidized beds

Investigation into the Mechanism of the Steel Particle Feeding Process: A Numerical and Experimental Study

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