Coupled CFD‐DEM Modeling

Norouzi, Hamid Reza; Zarghami, Reza; Sotudeh‐Gharebagh, Rahmat; Mostoufi, Navid

doi:10.1002/9781119005315

Cited by 125 publications

(68 citation statements)

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“…We calibrate

k_{n, i j}

and

γ_{n, i j}

from rebound experiments and the analytical solution for a single impact or estimate them from the equivalent material properties:

k_{n, i j} goodbreakinfix= 1.2024 {(E_{i j}^{2} \sqrt{m_{i j}} r_{i j} ∥ {bold-italicv}_{n, i j} ∥)}^{2 ∕ 5}

where

E_{i j}

r_{i j}

, and

m_{i j}

are the equivalent Young's modulus, radius and mass for the two colliding particles (

i

and

j

E_{i j} goodbreakinfix= {(\frac{1 - ν_{i}^{2}}{E_{i}} + \frac{1 - ν_{j}^{2}}{E_{j}})}^{- 1}

r_{i j} goodbreakinfix= {(\frac{1}{r_{i}} + \frac{1}{r_{j}})}^{- 1}

m_{i j} goodbreakinfix= {(\frac{1}{m_{i}} + \frac{1}{m_{j}})}^{- 1}

and where

E_{i}

and

ν_{i}

are, respectively, the Young's modulus and Poisson ratio of particle

i

. The damping coefficient is then related to the normal spring stiffness:

γ_{n, i j} goodbreakinfix= ...

…”

Section: Normal Contact Force Modelsmentioning

confidence: 99%

“…However, such models give maximal dissipation at the initial time of impact due to the sole dependence of the dissipative force on the relative velocity. This problem was solved by Kuwabara and Kono and Tsuji et al, by introducing a dependence on normal deformation in the damping term:

f_{n, i j}^{c} goodbreakinfix= k_{n, i j} ∥ δ_{n, i j} ∥^{\frac{1}{2}} δ_{n, i j} goodbreakinfix- γ_{n, i j} ∥ δ_{n, i j} ∥^{\frac{1}{2}} {\dot{bold-italicδ}}_{n, i j}

f_{n, i j}^{c} goodbreakinfix= k_{n, i j} ∥ δ_{n, i j} ∥^{\frac{1}{2}} δ_{n, i j} goodbreakinfix- γ_{n, i j} ∥ δ_{n, i j} ∥^{\frac{1}{4}} {\dot{bold-italicδ}}_{n, i j}

For the second model Equation , Norouzi et al derived an expression for

γ_{n, i j}

γ_{n, i j} goodbreakinfix= \frac{- 2.2664 \ln e_{n} \sqrt{m_{i j} k_{n, i j}}}{\sqrt{{(\ln e_{n})}^{2} + 10.1354}}

in which

k_{n, i j}

is from Equation .…”

Section: Normal Contact Force Modelsmentioning

confidence: 99%

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Experimental Methods in Chemical Engineering: Discrete Element Method—DEM

et al. 2019

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The discrete element method (DEM) is a time-driven simulation technique based on a Lagrangian description of particle motion that predicts the flow of granular matter and fine powders in conveying, mixing, drying, and heterogeneous gas-(liquid)-solids reactors. Powders flowing out of bins form bridges, they segregate in suboptimal pharmaceutical v-blenders, and a stream may split into large gulf streams as they enter fluidized bed reactors from standpipes and diplegs. To reduce the uncertainty in scaling up these and other powder process unit operations, researchers apply DEM. It integrates Newton's second law (acceleration equals the sum of the forces) for each particle and models contacts between the particles with springs and dashpots (dampers). It is computationally intensive since it calculates the trajectory of all particles. The availability of open source codes, commercial software, and parallel computer architectures has accelerated its adoption in pharmaceutical, agro-industrial, and mineral processes, and geophysics. The accuracy of DEM models depends on how well researchers calibrate the contact model expressions and their parameters: friction coefficients and the coefficient of restitution. Systems exceeding 1 × 10 8 particles can require weeks of computational time on large computer clusters. Current research targets non-spherical particle interactions and multiphysics problems including heat transfer, mass transfer, and chemical reactions within the particles. The field has grown to 750 indexed articles in WoS in 2017. A bibliographic analysis recognized four research clusters: granular materials, behaviour, particle shape, and deformation; flows, fluidized beds, and computational fluid dynamics; particles, impact, and validation; and granular flow, dynamics, and segregation.

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“…We calibrate

k_{n, i j}

and

γ_{n, i j}

from rebound experiments and the analytical solution for a single impact or estimate them from the equivalent material properties:

k_{n, i j} goodbreakinfix= 1.2024 {(E_{i j}^{2} \sqrt{m_{i j}} r_{i j} ∥ {bold-italicv}_{n, i j} ∥)}^{2 ∕ 5}

where

E_{i j}

r_{i j}

, and

m_{i j}

are the equivalent Young's modulus, radius and mass for the two colliding particles (

i

and

j

E_{i j} goodbreakinfix= {(\frac{1 - ν_{i}^{2}}{E_{i}} + \frac{1 - ν_{j}^{2}}{E_{j}})}^{- 1}

r_{i j} goodbreakinfix= {(\frac{1}{r_{i}} + \frac{1}{r_{j}})}^{- 1}

m_{i j} goodbreakinfix= {(\frac{1}{m_{i}} + \frac{1}{m_{j}})}^{- 1}

and where

E_{i}

and

ν_{i}

are, respectively, the Young's modulus and Poisson ratio of particle

i

. The damping coefficient is then related to the normal spring stiffness:

γ_{n, i j} goodbreakinfix= ...

…”

Section: Normal Contact Force Modelsmentioning

confidence: 99%

f_{n, i j}^{c} goodbreakinfix= k_{n, i j} ∥ δ_{n, i j} ∥^{\frac{1}{2}} δ_{n, i j} goodbreakinfix- γ_{n, i j} ∥ δ_{n, i j} ∥^{\frac{1}{2}} {\dot{bold-italicδ}}_{n, i j}

f_{n, i j}^{c} goodbreakinfix= k_{n, i j} ∥ δ_{n, i j} ∥^{\frac{1}{2}} δ_{n, i j} goodbreakinfix- γ_{n, i j} ∥ δ_{n, i j} ∥^{\frac{1}{4}} {\dot{bold-italicδ}}_{n, i j}

For the second model Equation , Norouzi et al derived an expression for

γ_{n, i j}

γ_{n, i j} goodbreakinfix= \frac{- 2.2664 \ln e_{n} \sqrt{m_{i j} k_{n, i j}}}{\sqrt{{(\ln e_{n})}^{2} + 10.1354}}

in which

k_{n, i j}

is from Equation .…”

Section: Normal Contact Force Modelsmentioning

confidence: 99%

Experimental Methods in Chemical Engineering: Discrete Element Method—DEM

et al. 2019

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show abstract

“…Obviously, the behavior and performance, spatial distribution, and instantaneous state of particles inside the screw conveyor are either difficult or impossible to be obtained through experimental measurement. 12 Meanwhile, a comparison and the applicability of the models are assessed theoretically. It is entirely feasible to obtain some detailed motion information by numerical simulation for the conveying process, which is helpful to understand the process of material transportation in a screw conveyor.…”

Section: Introductionmentioning

confidence: 99%

“…This method is a supplement to the Euler method modeling of multiphase flow, known as the "Euler-Lagrangian" method. 12 Meanwhile, a comparison and the applicability of the models are assessed theoretically. The result is recommended to apply CFD-DEM modeling to complex particle-fluid flow.…”

Section: Introductionmentioning

confidence: 99%

Comparison of computational fluid dynamics–discrete element method and discrete element method simulations for a screw conveyor

Wang

et al. 2019

Asia-Pacific J Chem Eng

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Flow behavior of particles is simulated by means of discrete element method (DEM) in a screw conveyor. The gas flow is considered by the Euler model. The subgrid scale model turbulence model is applied to solve gas turbulence. A detailed description of the model equations applied has been presented.The comparison between computational fluid dynamics (CFD)-DEM (i.e., considering gas flow) and DEM (i.e., not considering gas flow) simulation results is performed. Predicted mass flow rate with CFD-DEM model is in better agreement with experiments by Roberts and Wills comparing with DEM results. The effects of incline angle and filling rate on the flow behavior of particles are predicted. The simulation results show that the gas flow has a major impact on particle flow behavior at a high rotation speed, whereas the velocity of particles increases with an increase of rotation speed. As the filling rate rises, both the translational granular temperature and the rotational granular particle temperature decline, which means that the particles fluctuation turns weak. While with an increase of incline angle, from the horizontal to vertical, the translational granular temperature is increased, but the rotational granular temperature reduces and then goes up. In brief, translational motion is a main mode of motion in a screw conveyor. K E Y W O R D SCFD-DEM, filling rate, gas-solid two-phase flow, incline angle, screw conveyor

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“…Advantages of such reactors include high heat and mass transfer rates, excellent gas‐solid contacting, temperature uniformity, and ease of process control 1. Although studying the hydrodynamics of gas‐solid fluidized beds has drawn great attention for years, most of available reports in literature are focused on their behavior at ambient temperature 2–9.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Hydrodynamics of High‐Temperature Fluidized Beds by Pressure Fluctuations

2016

Self Cite

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Hydrodynamics of a gas-solid fluidized bed at elevated temperatures was investigated by analyzing pressure fluctuations in time and frequency domains. Sand particles were fluidized with air at various bed temperatures. At a constant gas velocity, the standard deviation, power spectrum density function, and wide-band energy of pressure fluctuations reach a maximum at 300°C. Increasing the temperature to this value causes larger bubble sizes and after the bubbles reach their maximum size, they break into smaller bubbles. The Archimedes number decreases with higher temperature and the type of fluidization becomes closer to that of Geldart A boundary at this maximum temperature. Based on estimation of the drag force acting on the emulsion phase, it was concluded that 300°C was a transition temperature at which the drag force reaches a minimum due to a significant change of interparticle and hydrodynamic forces.

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Coupled CFD‐DEM Modeling

Cited by 125 publications

References 0 publications

Experimental Methods in Chemical Engineering: Discrete Element Method—DEM

Experimental Methods in Chemical Engineering: Discrete Element Method—DEM

Comparison of computational fluid dynamics–discrete element method and discrete element method simulations for a screw conveyor

Investigation of Hydrodynamics of High‐Temperature Fluidized Beds by Pressure Fluctuations

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