Euler–euler anisotropic gaussian mesoscale simulation of homogeneous cluster‐induced gas–particle turbulence

Kong, Bo; Fox, Rodney O.; Feng, H. J.; Capecelatro, Jesse; Patel, Ravi G.; Desjardins, Olivier

doi:10.1002/aic.15686

Cited by 44 publications

(56 citation statements)

References 48 publications

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“…This section briefly summarizes the equations used in the EL and EE‐AG simulations applied in this study. The EL model is solved in the NGA environment, 3,26 while the EE‐AG model is solved in the OpenFOAM environment 12 …”

Section: Governing Equations and Methodsmentioning

confidence: 99%

“…One practical limitation to the use of EL models is the large computational cost involved in systems containing high concentrations of particles due to the shear number of individual particles that must be tracked. Furthermore, parallelization issues can arise in systems that feature a significant amount of maldistribution in the particle phase such as in CIT 12 . In those cases, the bulk of the computational load is imbalanced toward the particular processors responsible for solving the governing equations in the portions of the system geometry that include the clusters.…”

Section: Introductionmentioning

confidence: 99%

“…Like EL simulations, EE simulations have been successfully implemented to model a variety of particle‐laden flows. A key assumption in previous models with an Eulerian particle phase is that the particle‐phase velocity distribution is nearly Maxwellian 12 . This assumption is most valid for near‐zero Knudsen number systems such as fluidized beds where particles are densely packed and highly collisional.…”

Section: Introductionmentioning

confidence: 99%

“…The assumption makes it so that physical phenomena such as significant particle‐phase velocity anisotropy and particle trajectory crossing cannot be accounted for 20 . Failing to capture this results in the spontaneous development of clusters even in highly dilute systems that are not replicated in EL modeling 12,14 . One method that has been developed to remedy this issue that has been demonstrated in previous work involves considering the particle‐phase velocity fluctuations as an anisotropic Gaussian distribution 12,14 .…”

Section: Introductionmentioning

confidence: 99%

“…In Section 2, we briefly review the EL and EE‐AG models developed in our prior work. More details can be found in the literature 3,12 . Of particular importance are the near‐wall boundary conditions for the particle‐phase moments described in Section 2.3.…”

Section: Introductionmentioning

confidence: 99%

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Direct comparison of Eulerian–Eulerian and Eulerian–Lagrangian simulations for particle‐laden vertical channel flow

et al. 2020

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Particle‐laden flows in a vertical channel were simulated using an Eulerian–Eulerian, Anisotropic Gaussian (EE‐AG) model. Two sets of cases varying the overall mass loading were done using particle sizes corresponding to either a large or small Stokes number. Primary and turbulent statistics were extracted from these results and compared with counterparts collected from Eulerian–Lagrangian (EL) simulations. The statistics collected from the small Stokes number particle cases correspond well between the two models, with the EE‐AG model replicating the transition observed using the EL model from shear‐induced turbulence to relaminarization to cluster‐induced turbulence as the mass loading increased. The EE‐AG model was able to capture the behavior of the EL simulations only at the largest particle concentrations using the large Stokes particles. This is due to the limitations involved with employing a particle‐phase Eulerian model (as opposed to a Lagrangian representation) for a spatially intermittent system that has a low particle number concentration.

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Section: Governing Equations and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Direct comparison of Eulerian–Eulerian and Eulerian–Lagrangian simulations for particle‐laden vertical channel flow

et al. 2020

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Verification of Eulerian–Eulerian and Eulerian–Lagrangian simulations for turbulent fluid–particle flows

et al. 2017

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We present a verification study of three simulation techniques for fluid-particle flows, including an Euler-Lagrange approach (EL) inspired by Jackson's seminal work on fluidized particles, a quadrature-based moment method based on the anisotropic Gaussian closure (AG), and the traditional two-fluid model. We perform simulations of two problems: particles in frozen homogeneous isotropic turbulence (HIT) and cluster-induced turbulence (CIT). For verification, we evaluate various techniques for extracting statistics from EL and study the convergence properties of the three methods under grid refinement. The convergence is found to depend on the simulation method and on the problem, with CIT simulations posing fewer difficulties than HIT. Specifically, EL converges under refinement for both HIT and CIT, but statistics exhibit dependence on the postprocessing parameters. For CIT, AG produces similar results to EL. For HIT, converging both TFM and AG poses challenges. Overall, extracting converged, parameter-independent Eulerian statistics remains a challenge for all methods. V C 2017 American Institute of Chemical Engineers AIChE J, 63: [5396][5397][5398][5399][5400][5401][5402][5403][5404][5405][5406][5407][5408][5409][5410][5411][5412] 2017 In this section, the governing equations of the fluid and particle phases are briefly presented. The behavior of fluid phase Figure 4. St 5 5 particle fields at t 5 1 for TFM, AG, EL (left to right) and increasing resolution (top to bottom) forHIT case. a p shown for EE methods and particle locations for EL method.Statistics are captured using the various density estimation techniques shown in the legend. Fix and Var refer to kernel density estimation using fixed filter width and variable filter width, respectively. In the case of Fix, the number following refers to the filter width, in multiples of particle diameter, h/d p , and in the case of Var, the number refers to the number of particles sampled, N p .Spectra are shown for increasing grid resolution. For EL, Fix 10 was used for density estimation. Darker lines refer to higher resolutions. For TFM and AG, the black dashed lines are from the highest resolution EL simulation for comparison.Particle volume-fraction variance (left) and granular temperature (right). K refers to grid resolution, D, for EE methods and filter width, h, for EL.

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Validation study on spatially averaged two‐fluid model for gas‐solid flows: II. Application to risers and fluidized beds

Schneiderbauer

2018

AIChE Journal

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In our prior study (Schneiderbauer, AIChE J. 2017;63(8):3544-3562), a spatially averaged two-fluid model (SA-TFM) was presented, where closure models for the unresolved terms were derived. These closures require constitutive relations for the turbulent kinetic energies of the gas and solids phase as well as for the subfilter variance of the solids volume fraction. We had ascertained that the filtered model do yield nearly the same time-averaged macroscale flow behavior in bubbling fluidized beds as the underlying kinetic-theory-based two-fluid model, thus verifying the SA-TFM model approach. In the present study, a set of 3D computational simulations for validation of the SA-TFM against the experimental data on riser flow and bubbling fluidized beds is performed. Finally, the SA-TFM predictions are in fairly good agreement with experimental data in the case of Geldart A and B particles even though using very coarse grids.

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Euler–euler anisotropic gaussian mesoscale simulation of homogeneous cluster‐induced gas–particle turbulence

Abstract: This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version record. Please cite this article as

Cited by 44 publications

References 48 publications

Direct comparison of Eulerian–Eulerian and Eulerian–Lagrangian simulations for particle‐laden vertical channel flow

Direct comparison of Eulerian–Eulerian and Eulerian–Lagrangian simulations for particle‐laden vertical channel flow

Verification of Eulerian–Eulerian and Eulerian–Lagrangian simulations for turbulent fluid–particle flows

Validation study on spatially averaged two‐fluid model for gas‐solid flows: II. Application to risers and fluidized beds

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