Numerical study of collisional particle dynamics in cluster-induced turbulence

Capecelatro, Jesse; Desjardins, Olivier; Fox, Rodney O.

doi:10.1017/jfm.2014.194

Cited by 94 publications

(102 citation statements)

References 31 publications

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“…In particular, it was shown in our previous work that three-dimensional EL simulations are capable of accurately reproducing key cluster characteristics, including fall velocity, mean cluster concentration and concentration fluctuations in wall-bounded gravity-driven flows (Capecelatro et al 2014b). The present paper is an extension of our previous work (Capecelatro et al 2014a), in which EL simulations of fully developed gravity-driven CIT were performed. Starting from a random distribution of particles subject to gravity, after an initial transient, the flow becomes statistically stationary with a probability density function (p.d.f.)…”

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confidence: 83%

“…At higher values of mass loading, the relative motion between the phases leads to additional sources On fluid-particle dynamics in cluster-induced turbulence 581 4 7 10 14 0 FIGURE 1. (Colour online) Instantaneous field of fluid vorticity magnitude in gravity-driven CIT generated using data from Capecelatro, Desjardins & Fox (2014a) corresponding to Re p = 1 in this work (refer to table 1). Vorticity is generated by the shear layers at the edge of clusters, denoted by the blue lines showing iso-contours of α p = 3 α p .…”

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confidence: 99%

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On fluid–particle dynamics in fully developed cluster-induced turbulence

2015

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At sufficient mass loading and in the presence of a mean body force (e.g. gravity), an initially random distribution of particles may organize into dense clusters as a result of momentum coupling with the carrier phase. In statistically stationary flows, fluctuations in particle concentration can generate and sustain fluid-phase turbulence, which we refer to as cluster-induced turbulence (CIT). This work aims to explore such flows in order to better understand the fundamental modelling aspects related to multiphase turbulence, including the mechanisms responsible for generating volume-fraction fluctuations, how energy is transferred between the phases, and how the cluster size distribution scales with various flow parameters. To this end, a complete description of the two-phase flow is presented in terms of the exact Reynolds-average (RA) equations, and the relevant unclosed terms that are retained in the context of homogeneous gravity-driven flows are investigated numerically. An Eulerian-Lagrangian computational strategy is used to simulate fully developed CIT for a range of Reynolds numbers, where the production of fluid-phase kinetic energy results entirely from momentum coupling with finite-size inertial particles. The adaptive filtering technique recently introduced in our previous work (Capecelatro et al., J. Fluid Mech., vol. 747, 2014, R2) is used to evaluate the Lagrangian data as Eulerian fields that are consistent with the terms appearing in the RA equations. Results from gravity-driven CIT show that momentum coupling between the two phases leads to significant differences from the behaviour observed in very dilute systems with one-way coupling. In particular, entrainment of the fluid phase by clusters results in an increased mean particle velocity that generates a drag production term for fluid-phase turbulent kinetic energy that is highly anisotropic. Moreover, owing to the compressibility of the particle phase, the uncorrelated components of the particle-phase velocity statistics are highly non-Gaussian, as opposed to systems with one-way coupling, where, in the homogeneous limit, all of the velocity statistics are nearly Gaussian. We also observe that the particle pressure tensor is highly anisotropic, and thus additional transport equations for the separate contributions † Email address for correspondence: jsc359@cornell.edu On fluid-particle dynamics in cluster-induced turbulence 579 to the pressure tensor (as opposed to a single transport equation for the granular temperature) are necessary in formulating a predictive multiphase turbulence model.

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confidence: 83%

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confidence: 99%

On fluid–particle dynamics in fully developed cluster-induced turbulence

2015

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“…We have observed in previous studies that both the effective viscosity and turbulent viscosity contribute very little to the total viscosity in moderately dilute gas-solid flows, and the majority of fluid-phase turbulent kinetic energy is produced by resolved wakes past clusters (Capecelatro et al, 2014a). A detailed study on cluster-induced turbulence in a nonreactive homogeneous riser can be found in our previous work (Capecelatro et al, 2014b).…”

Section: Gas Phase Descriptionmentioning

confidence: 86%

Numerical investigation and modeling of reacting gas-solid flows in the presence of clusters

Capecelatro

Pepiot

2015

Chemical Engineering Science

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“…Also in the absence of adequate knowledge about the mechanism of hydrodynamic interactions that lead to formation of particle clusters as of those observed by Capecelatro et al (2014), we assume that for Geldart A particle the major driving mechanism for formation of clusters is particle cohesion.…”

Section: Drag Model: Transition Between Uniform and Clustered Drag Lawsmentioning

confidence: 99%

Development of a gas–solid drag law for clustered particles using particle-resolved direct numerical simulation

Mehrabadi

Murphy

Subramaniam

2016

Chemical Engineering Science

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Particle-resolved direct numerical simulation (PR-DNS) is used to quantify the drag force on clustered particle configurations over the solid phase volume fraction range of 0.1 ≤ φ ≤ 0.35 and the mean slip Reynolds number range of 0.01 ≤ Re m ≤ 50. The particle configurations and flow parameters correspond to gas-solid suspensions of Geldart A particles in which formation of clusters have been reported. In our PR-DNS, we use clustered particle configurations that match cluster statistics observed in experimental studies.To generate the particle configurations, we perform discrete element method (DEM) simulations of homogeneous cooling gas (HCG) systems with cohesive and inelastic particles in the absence interstitial fluid. Clustered particle subensembles are then extracted from HCG simulations to match the statistics of cluster size distributions observed in experiments. These sub-ensembles are used for PR-DNS. It is found that the mean drag on clustered configurations decreases when compared to the drag laws for uniform particle configura- * tions. The maximum drag reduction belongs to the configuration with low solid-phase volume fraction φ = 0.1 in Stokes flow, and is about 35%. The drag reduction reduces with increase in both φ and Re m . A clustering metric is introduced to explain the behavior of the drag reduction with respect to solid-phase volume fraction. Also the behavior of the drag reduction with mean slip Reynolds number is related to the Brinkman screening length. PR-DNS results are then used to propose a clustered drag model for the range of flow parameters considered in this study. This clustered drag model provides a smooth transition between the uniform and clustered states by means of a weighting function with two model parameters.

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Numerical study of collisional particle dynamics in cluster-induced turbulence

Cited by 94 publications

References 31 publications

On fluid–particle dynamics in fully developed cluster-induced turbulence

On fluid–particle dynamics in fully developed cluster-induced turbulence

Numerical investigation and modeling of reacting gas-solid flows in the presence of clusters

Development of a gas–solid drag law for clustered particles using particle-resolved direct numerical simulation

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