Effects of Sparger Holes on Gas‐Liquid Hydrodynamics in Bubble Columns

Liu, Yang; Zhang, Li; Yong-ju, Zhang; Zhou, Lixing

doi:10.1002/ceat.201900129

Cited by 11 publications

(7 citation statements)

References 25 publications

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“… 36 A series of the second-order moment gas–particle and gas–liquid turbulent models to simulate the anisotropic dispersions of particle and bubble, and hydrodynamics of gas–particle, and bubble-particle two-phase flows were developed to successfully reveal the anisotropic characteristics, the redistributions of Reynolds stresses of two phases, and the mixing and separation behaviors. 37 − 43 …”

Section: Introductionmentioning

confidence: 99%

Hydrodynamic Modeling of Turbulence Modulation by Particles in a Swirling Gas–Particle Two-Phase Flow

2021

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Turbulence modulations by particles of a swirling gas–particle two-phase flow in an axisymmetric chamber are numerically simulated. To fully consider the preferential concentrations and the anisotropic dispersions of particles, a second-order moment model coupling particle–particle collision model was improved. Experimental validation for the proposed model, algorithm, and in-house codes by acceptable match was carried out. The effects of ultralight-expanded graphite and heavy copper particles with a large span of Stokes number on gas velocities and fluctuations, Reynolds shear stresses and tensor invariants, turbulence kinetic energies, and vortice structures are investigated. The results show that turbulent modulation exhibits strong anisotropic characteristics and remains in a close relationship with the flow structure. Modulation disturbances and vortex evolution are enforced by heavy-large particles with higher Stokes numbers. Preferential accumulations of ultralight particles in shear stress regions at lower vortices are weaker than those of heavy particles. For axial turbulence modulations, a heavy particle plays the primary role in the inhibition action because of larger inertia, and a light particle contributes to the enhancement effect due to excellent followability. The instantaneous flow information and coherent turbulent structure are failed to be acquired due to the limitation of the Reynolds time-averaged algorithm.

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Section: Introductionmentioning

confidence: 99%

Hydrodynamic Modeling of Turbulence Modulation by Particles in a Swirling Gas–Particle Two-Phase Flow

2021

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“…In recent years, computational fluid dynamics (CFD) methods are being utilized substantially compared to hydrodynamic modelling for the single/multicomponent gas-particle two-phase turbulent flow, e.g., the direct numerical simulation (DNS) algorithm [6][7][8][9][10][11], large eddy simulation (LES) SGS model [12][13][14][15][16][17][18], and Reynolds time-averaged Navier-Stokes simulation (RANS) [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Regarding gas turbulence modulations, Gore et al (1989) [35] found that turbulence modifications generally have two kind of effects-that is, the augmentation and attenuation activities-using a dimensionless parameter based on the ratio of particle diameter and length of maximum eddy.…”

Section: Introductionmentioning

confidence: 99%

“…Pakhomov et al (2015) [24] proposed a Reynolds stresses model for gas-particle turbulence flow and revealed that gas turbulent modulation can be attenuated by fine particles, reaching up to 25% for swirling particle dispersions. As for the particle dispersions of swirling gas-particle two-phase flow in a combustor, the isotropic or homogeneous particle dispersion characteristics were denoted by references [13,14,21,24] and isotropic dispersions by references [4,5,[29][30][31][32][33][34]. In addition, the studies regarding particle dispersions of binary-particle mixture turbulent flow in a liquid-fluidized bed are also carried out.…”

Section: Introductionmentioning

confidence: 99%

“…However, these models failed to describe anisotropic particle dispersion behaviors due to the isotropic hypothesis. Zhou et al (2017Zhou et al ( , 2018 [29,30] and [31] established and developed a set of second-order moment gas-particle turbulent models to successfully reveal the anisotropic dispersions of discrete particle and bubble phases, as well as the hydrodynamics of swirling two-phase flows [32][33][34]. The two-phase anisotropic behaviors, Reynolds stresses redistributions, and mixing and separation characteristics were indicated.…”

Section: Introductionmentioning

confidence: 99%

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Investigations of Gas–Particle Two-Phase Flow in Swirling Combustor by the Particle Stokes Numbers

Liu

2021

Processes

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Gas turbulence modulations and particle dispersions of swirling gas–particle two-phase flow in the combustor is investigated under the large spans of the particle Stokes numbers. To fully consider the preferential concentrations and anisotropic dispersions of a particle, a kinetic frictional stress model coupled with a second-order moment two-phase turbulent model and granular temperature equation is improved. The proposed modeling and simulations are in good agreement with the experimental validations. Results show turbulent modulations and particle dispersions exhibit strongly anisotropic characteristics, keeping a close relationship with flow structure. The axial gas velocity and RMS fluctuation velocity of 45.0-μm EGP was approximately 5.0 times and 3.0 times greater than 1000.0 μm Copper particles, and their axial particle velocity was 0.25 times and twice greater than those of 45.0 μm EGP. The degree of modulation in the axial–radial direction is larger than those of radial–tangential and axial–tangential direction. Particle dispersions are sensitive to particle diameter parameters and intensified by higher Stokes number.

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“…CFD models, as the most accurate models, can provide comprehensive modeling for BCRs or SBCRs by considering the fluid dynamics of two or three phases via the Navier‐Stokes equations. Two highly nonlinear approaches, namely, the Euler‐Euler 9–14 and the Euler‐Lagrange 15–17 approaches, were introduced for CFD modeling, which requires high CPU time. In most CFD models, a population balance equation (PBE) is applied to keep track of the bubble size distribution due to coalescence and breakage through a density function 18–20.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Slurry Bubble‐Column Reactors with Emphasis on the Importance of Bubble Size Estimation

2021

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A mathematical model entitled varying‐bubble model (V‐BM) was adapted to simulate a slurry bubble‐column reactor, operating in a churn‐turbulent regime, based on an axial‐dispersion model. This model was theoretically able to estimate the size of forming bubbles at the sparger, variations of each chemical species and catalyst concentration, pressure drop in both gas and liquid phases, change in size and rising velocity of bubbles, as well as gas holdup and specific gas‐liquid interfacial area along the reactor axis. A comparison between the V‐BM and single‐bubble model (S‐BM) indicates that the V‐BM is better compatible with the experimental data. The results demonstrate that the contribution of mass transfer is much more than the pressure drop in increasing the size of the bubble along the reactor.

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Effects of Sparger Holes on Gas‐Liquid Hydrodynamics in Bubble Columns

Cited by 11 publications

References 25 publications

Hydrodynamic Modeling of Turbulence Modulation by Particles in a Swirling Gas–Particle Two-Phase Flow

Hydrodynamic Modeling of Turbulence Modulation by Particles in a Swirling Gas–Particle Two-Phase Flow

Investigations of Gas–Particle Two-Phase Flow in Swirling Combustor by the Particle Stokes Numbers

Modeling of Slurry Bubble‐Column Reactors with Emphasis on the Importance of Bubble Size Estimation

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