A dynamic multiphase turbulence model for coarse-grid simulations of fluidized gas-particle suspensions

Rauchenzauner, Stefanie; Schneiderbauer, Simon

doi:10.1016/j.ces.2021.117104

Cited by 15 publications

(7 citation statements)

References 75 publications

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“…Future studies should focus on a‐posteriori tests of the drag correction models in coarse‐grid simulations. It has been noted in the literature 30, 32, 33 that improved models for the filtered particle phase stress could improve the accuracy of coarse‐grid simulations even for non‐cohesive systems. It seems reasonable to expect that the stress model would be even more important in cohesive systems.…”

Section: Discussionmentioning

confidence: 99%

“…They concluded that, for the range of particle volume fractions and cohesion strengths they investigated, there was little difference in the drift velocity models for cohesive and non-cohesive systems. Rauchenzauner and Schneiderbauer [33] and Cloete et al [27,28] studied cohesionless gas-particle flows and concluded that anisotropic drag corrections improved fTFM predictions. Prior to these studies, Parmentier et al [15] postulated anisotropic drag correction but did not test its importance.…”

Section: Supporting Information Available Onlinementioning

confidence: 99%

“…Prior to these studies, Parmentier et al [15] postulated anisotropic drag correction but did not test its importance. Further, Rauchenzauner and Schneiderbauer [33] showed that an advanced stress modeling, among other improvements, positively impacts drag modeling. Zhu et al [30] incorporated a particle phase Reynolds stress-like marker in their drag correction model.…”

Section: Supporting Information Available Onlinementioning

confidence: 99%

“…15 postulated anisotropic drag correction but did not test its importance. Further, Rauchenzauner and Schneiderbauer 33 showed that an advanced stress modeling, among other improvements, positively impacts drag modeling. Zhu et al.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning‐Based Filtered Drag Model for Cohesive Gas‐Particle Flows

et al. 2023

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The accuracy of filtered two-fluid model simulations critically depends on constitutive models for corrections that account for the effects of inhomogeneous structures at the sub-grid level. The complexity of accounting these structures increases with cohesion. In the present study, a dataset from filtered Euler-Lagrange simulations with systematic variations of the cohesion level and the filter length was created to investigate the development of a machine learning-based drag correction model for liquid bridge-induced cohesive gas-particle flows. A-priori tests revealed that these models afford robust and accurate predictions of the drag correction and the actual drag force. Further it was demonstrated that an anisotropic drag correction model is more accurate than an isotropic model.

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Section: Discussionmentioning

confidence: 99%

Section: Supporting Information Available Onlinementioning

confidence: 99%

Section: Supporting Information Available Onlinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning‐Based Filtered Drag Model for Cohesive Gas‐Particle Flows

et al. 2023

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“…In the filtered model, the drag coefficient is obtained by filtering fine-grid simulations. Presumably, the filtered drag accounts for the less resolved grids in the larger computational domain. , A dynamic grid adjustment approach, whereby the impact of unresolved scales is predicted from the large resolved scales, has also been demonstrated; , this still remains within academic realms.…”

Section: Today’s Tools (2005–2020)mentioning

confidence: 99%

Fluidized Bed Scale-Up for Sustainability Challenges. 1. Tomorrow’s Tools

Cocco,

Chew

2024

Ind. Eng. Chem. Res.

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The scaling up of fluidized beds has been purposefully pursued for more than 100 years. Yet, over that time, scale-up tools have not significantly changed. Data analysis is typically a standard analysis of variances statistical exercise, perhaps reinforced with a design of experimental procedure. Flowsheeting and equipment design are based on institutional knowledge, albeit graphical user interface-based process flow models make that job more manageable. Advanced models such as computational fluid dynamics are used but often as a supplement and not a primary driver. As a result, the scale-up process for a fluidized bed can take more than 10 years. Fluidized beds remain at the forefront of the present time-critical sustainability challenges, e.g., carbon capture by particulate sorbents, methane-to-hydrogen, plastic-to-chemicals, etc. In view of the exigency toward net zero, today's scale-up efforts need to be accelerated, leveraging the advanced new tools that have become readily available. The problem is that such tools are often neglected, inadequately implemented, ineffectively resourced, and/or poorly understood. This motivated the current effort, which is targeted at reviewing how scale-up tools have evolved over the years and the promising new tools, addressing some of the barriers of these tools in the design and scale-up of fluidized beds, as well as contemplating what can be done to circumvent these barriers. As a follow up, a companion part 2 (Cocco, R. A.; Chew, J. W. Ind. Eng. Chem. Res., submitted for publication) proposes a new scale-up path leveraging the advanced tools to achieve timely implementation of the new green fluidized bed processes.

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Continuum modeling of gas–particle flows: an overview

Schneiderbauer

2024

Acta Mech

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In this review paper, we provide a brief overview of the recent advances in the continuum modeling of gas–particle flows. First, we focus on the kinetic theory-based two-fluid models, which have become a valuable tool to investigate small-scale moderately dense turbulent gas–particle flows. Second, the continuum description is quite restrictive with respect to the maximum grid spacing, and large-scale simulations usually employ coarse mesh resolutions to keep the analyses practicable. Such coarse-graining inevitably neglects the small unresolved scales, which requires additional modeling. Here, filtered two-fluid models have been applied successfully to a variety gas–solid flow problems. Finally, we give a condensed outline about future research challenges for the continuum modeling of gas–particle flows.

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A dynamic multiphase turbulence model for coarse-grid simulations of fluidized gas-particle suspensions

Cited by 15 publications

References 75 publications

Machine Learning‐Based Filtered Drag Model for Cohesive Gas‐Particle Flows

Machine Learning‐Based Filtered Drag Model for Cohesive Gas‐Particle Flows

Fluidized Bed Scale-Up for Sustainability Challenges. 1. Tomorrow’s Tools

Continuum modeling of gas–particle flows: an overview

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