CFD–DEM modeling of gas fluidization of fine ellipsoidal particles

Gan, Jieqing; Zhou, Zongyan; Yu, Aibing

doi:10.1002/aic.15050

Cited by 73 publications

(33 citation statements)

References 70 publications

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“…In CFD–DEM simulations, the traditional approach to obtaining the solid volume fraction‐dependent drag force for nonspherical particles is to combine the correlation for a single particle and the voidage correction factor developed by Di Felice . Here, the correlation developed by Holzer and Sommerfeld for a single particle is used to calculate the drag force on each individual particles in different random configurations.…”

Section: Resultsmentioning

confidence: 99%

“…Also, the correlation does not quantify the effect of mean orientations on the drag force. In CFD–DEM simulations, the drag force on nonspherical particles with different orientations at various solid volume fractions are required. The most commonly used approach to obtaining the solid volume fraction‐dependent drag force for nonspherical particles is to combine the correlation for single particle and the voidage correction factor developed by Di Felice .…”

Section: Introductionmentioning

confidence: 99%

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Effect of particle orientation on the drag force in random arrays of prolate ellipsoids in low‐Reynolds‐number flows

Jiang

Huang

et al. 2019

AIChE Journal

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Direct numerical simulations are performed to study the effect of particle orientation on flows through fixed random arrays of prolate ellipsoids at low Reynolds numbers.The Hermans orientation factor and Beta distribution are introduced to quantify the mean orientation and orientation deviation of the particles. The simulation resultsshow that the effect of particle orientation is profound especially when the solid volume fraction and the aspect ratio are large. With the increase of Hermans orientation factors, the drag force decreases when the flow follows a reference direction defined by the average direction of all particles' semi-major axes, while increases when the flow is perpendicular to the reference direction. Comparisons show that the traditional drag force correlations for ellipsoidal particles significantly under-predict the drag force. Based on current simulation results, new drag relations are proposed for prolate ellipsoidal particles at arbitrary aspect ratios, Hermans orientation factors and solid volume fractions. K E Y W O R D S ellipsoids, fluidization, Hermans orientation factors, multiphase flow, the drag force

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effect of particle orientation on the drag force in random arrays of prolate ellipsoids in low‐Reynolds‐number flows

Jiang

Huang

et al. 2019

AIChE Journal

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“…In particular, particle cohesion can be straightforwardly considered through directly computing the interparticle forces such as the van der Waals force, [15][16][17][18] liquid bridge force, [19][20][21][22][23] and electrostatic force. [24][25][26][27][28] Hence, CFD-DEM approach was widely used in previous studies [29][30][31][32][33][34][35][36] for the microscopic understanding of the fluidization of cohesive particles. However, the detailed particle scale simulation of CFD-DEM will result in a prohibitively high computational cost for the modeling of an industrial system by current conventional computational infrastructures.…”

Section: Introductionmentioning

confidence: 99%

Linking discrete particle simulation to continuum properties of the gas fluidization of cohesive particles

Hou

2020

AIChE Journal

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Discrete particle simulation can explicitly consider interparticle forces and obtain microscopic properties of the fluidized cohesive particles, but it is computationally expensive. It is thus pivotal to link the microscopic discrete properties to the macroscopic continuum description of the system for large scale applications. This work studies the fluidization of cohesive particles through the coupled computational fluid dynamics and discrete element method (CFD‐DEM). First, discrete CFD‐DEM results show the increased particle cohesion leads to the severe particle agglomeration which affects the fluidization quality significantly. Then, continuum properties are attained by a weighted time‐volume averaging method, showing that tensile pressure becomes significant as particle cohesion increases. By incorporating Rumpf correlation into the solid pressure equation, the tensile pressure could be predicted consistently with the averaged CFD‐DEM results for different particle cohesion. Finally, those overall steady averaged properties of the bed are obtained for understanding the general macroscopic properties of the system.

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“…In some reports, a population balance equation (PBE) was employed as a method for predicting particle and crystal evolution, which could calculate the nucleation, growth, aggregation, and breakage process appropriately. Computational fluid dynamics (CFD) has been applied to describe the particle phenomena and to simulate complex fluid behaviors in recent years . In this work, by taking respective advantages into consideration, a CFD‐PBM (population balance model) coupled model was established to describe PtNPs formation in the microfluidic chip.…”

Section: Introductionmentioning

confidence: 99%

Biological synthesis of Pt nanoparticles in a microfluidic chip and modeling of the formation process withPBMandCFD

Liu

Zhang

Wang

et al. 2017

J of Chemical Tech & Biotech

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BACKGROUND Biological synthesis of platinum nanoparticles (PtNPs) with Cacumen platycladi (C. platycladi) in a microfluidic chip and numerical simulation of formation processes were studied. The effect of volumetric flow rate on PtNPs' size distribution was investigated in detail not only by experimental methods but also by means of numerical simulation. Computational fluid dynamics (CFD) and a population balance model (PBM) combined with reactive kinetics were employed to simulate PtNPs' formation and to calculate the particle size distributions (PSD). RESULTS The experimental results confirmed the validity of the model and its practicality in predicting the formation of PtNPs and PSD evolution in the microfluidic chip. In addition, the numerical simulation results also tested and verified the accuracy of the results and conclusions acquired from the experiment. CONCLUSION It is concluded that PSD was influenced not only by mixing efficiency but also by residence time in the microfluidic chip, and that the two factors were determined by volumetric flow rate. There is a specific volumetric flow rate to achieve equilibrium (optimal mixing and residence time) that results in the average particle size reaching its maximum value. This work provides useful insight into the microfluidic biological synthesis of PtNPs and the influence of volumetric flow rate on size distribution in a microfluidic chip. © 2017 Society of Chemical Industry

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CFD–DEM modeling of gas fluidization of fine ellipsoidal particles

Cited by 73 publications

References 70 publications

Effect of particle orientation on the drag force in random arrays of prolate ellipsoids in low‐Reynolds‐number flows

Effect of particle orientation on the drag force in random arrays of prolate ellipsoids in low‐Reynolds‐number flows

Linking discrete particle simulation to continuum properties of the gas fluidization of cohesive particles

Biological synthesis of Pt nanoparticles in a microfluidic chip and modeling of the formation process withPBMandCFD

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