Toward Constitutive Models for Momentum, Species, and Energy Transport in Gas–Particle Flows

Sundaresan, Sankaran; Özel, Ali; Kolehmainen, Jari

doi:10.1146/annurev-chembioeng-060817-084025

Cited by 147 publications

(66 citation statements)

References 147 publications

(206 reference statements)

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“…In recent years the issue of the effect of the cell size on the numerical solution of fluidized bed has been addressed (Agrawal et al, 2001;Heynderickx et al, 2004;Igci et al, 2008;Parmentier et al, 2012;Ozel et al, 2013;Sundaresan et al, 2013). As discussed by Sundaresan et al (2013) the appropriate length scale for the grid resolution is is still an open issue and seems to be dependent on the given gas-solid flow configuration. However, Parmentier et al (2008) carried out an analysis of the effect of the grid resolution on dense fluidized beds with flow conditions roughly similar to the present study.…”

Section: Geometry and Meshmentioning

confidence: 99%

3D numerical simulation of a lab-scale pressurized dense fluidized bed focussing on the effect of the particle-particle restitution coefficient and particle–wall boundary conditions

Fede

Simonin

Ingram

2016

Chemical Engineering Science

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Section: Geometry and Meshmentioning

confidence: 99%

3D numerical simulation of a lab-scale pressurized dense fluidized bed focussing on the effect of the particle-particle restitution coefficient and particle–wall boundary conditions

Fede

Simonin

Ingram

2016

Chemical Engineering Science

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“…Unfortunately, inhomogeneous mesoscale flow structures in such reactors are incredibly complex and manifest persistent instabilities in flow properties spanning all sorts of spatiotemporal scales 1‐3 . This dynamic nature is of fundamental importance in essential process features such as the interphase momentum, species, and heat transfer 4 . Development of accurate and reliable modeling methods for capturing such complex dynamics has been the grand challenge for FBR modelers.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Cloete et al 14,30,31 presented the importance to include the physical anisotropic impacts in fTFM development, and they unveiled minor influences of increasing the SGM complexity via introducing additional markers. For more elaborate reviews, interested readers are referred to excellent articles of Schneiderbauer et al 32 and Sundaresan et al 4 Yet, despite multifarious contributions, better understanding subgrid closure modeling related to inhomogeneous structures and accurately predicting gas–particle flow hydrodynamics still pose prodigious challenges. For example, Cloete et al 14 indicated that a functional fitting method is commonly adopted in conventional filtered correlations; however, various model coefficients might have to be adjusted in such an approach.…”

Section: Introductionmentioning

confidence: 99%

Machine learning to assist filtered two‐fluid model development for dense gas–particle flows

2020

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Machine learning (ML) is experiencing an immensely fascinating resurgence in a wide variety of fields. However, applying such powerful ML to construct subgrid interphase closures has been rarely reported. To this end, we develop two data-driven ML strategies (i.e., artificial neural networks and eXtreme gradient boosting) to accurately predict filtered subgrid drag corrections using big data from highly resolved simulations of gas-particle fluidization. Quantitative assessments of effects of various subgrid input markers on training prediction outputs are performed and three-marker choice is demonstrated to be the optimal one for predicting the unseen test set. We then develop a parallel data loader to integrate this predictive ML model into a computational fluid dynamic (CFD) framework. Subsequent coarse-grid simulations agree fairly well with experiments regarding the underlying hydrodynamics in bubbling and turbulent fluidized beds. The present ML approach provides easily extended ways to facilitate the development of predictive models for multiphase flows. K E Y W O R D S coarse-grid simulation, filtered two-fluid model, fine-grid simulation, fluidization, machine learning, subgrid closure model 1 | INTRODUCTION Gas-solid fluidized bed reactors (FBRs) are ubiquitous in process engineering. Unfortunately, inhomogeneous mesoscale flow structures in such reactors are incredibly complex and manifest persistent instabilities in flow properties spanning all sorts of spatiotemporal scales. 1-3 This dynamic nature is of fundamental importance in essential process features such as the interphase momentum, species, and heat transfer. 4 Development of accurate and reliable modeling methods for capturing such complex dynamics has been the grand challenge for FBR modelers. One of the broadly-practiced predictive methods is the kinetic theory of granular flow (KTGF) based two-fluid model (TFM), which treats the fluid and particle phases as interpenetrating continuums. 5 However, the TFM simulation with sufficiently fine grids is necessitated for resolving all relevant scales of flow hydrodynamics. Fullmer and Hrenya 3 indicated that a typical mesh spacing of~3.3d s is required to obtain the grid independency for simulation statistics at intermediate solid phase fraction, and the grid size less than 10d s for dilute clustering flows is demanded for computational accuracy. For dense bubbling FBRs, Wang et al. 6 recommended a grid resolution of 2-4 d s . Such high requirements (10 −5 -10 −3 m) for reactor-scale simulations (10 −1 -10 1 m) are computationally prohibitive.Alternatively, subgrid models (SGMs) for interphase closure corrections such as the energy minimization multi-scale (EMMS), [7][8][9] filtered TFM (fTFM), 2,10-15 spatially-averaged TFM (SA-TFM), 16-18 and gradient-based method, [19][20][21] have been developed to account for effects of unresolved inhomogeneous structures. SGM enables a dramatic reduction of computational overhead by employing coarse grids while still maintaining high accuracy. In the establis...

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“…The fast growth in supercomputing power has made computational fluid dynamics (CFD) a powerful tool for predicting detailed information of the flow and heat fields in gas–solid flows. However, the flow and heat transfer behavior in multiphase flow reactors cross a wide range of length and time scales . For example, mesoscale structures with sizes of 10–100 d p ( d p is the particle diameter), though stemming from the interphase interaction and transport behavior at the microscopic scale (1–5 d p ), significantly affect the overall flow dynamics and transport behavior at reactor scales .…”

Section: Introductionmentioning

confidence: 99%

Development of a filtered interphase heat transfer model based on fine‐grid simulations of gas–solid flows

et al. 2019

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The filtered interphase heat-transfer coefficient for coarse-grid simulations of gas-solid flows can be obtained via a correction (Q) to its microscopic counterpart. The numerical results show that a good linear correlation between Q and the subgrid drift temperature exists at various filtered solid volume fractions, filter sizes and Reynolds numbers, where the subgrid drift temperature is the correlation between the fluctuating temperature of the gas phase and the fluctuation of the gas volume fraction. Since Q can be determined solely by one subgrid quantity, closure for Q is directly pursued. It is found that Q correlates surprisingly well with the product of the filtered solid volume fraction and the filtered temperature difference between the two phases normalized by the filtered heat transfer at a larger scale than the considered coarse grid. A fitting correlation is formulated based on this observation, and its predictability is evaluated in an a priori test.

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Toward Constitutive Models for Momentum, Species, and Energy Transport in Gas–Particle Flows

Cited by 147 publications

References 147 publications

3D numerical simulation of a lab-scale pressurized dense fluidized bed focussing on the effect of the particle-particle restitution coefficient and particle–wall boundary conditions

3D numerical simulation of a lab-scale pressurized dense fluidized bed focussing on the effect of the particle-particle restitution coefficient and particle–wall boundary conditions

Machine learning to assist filtered two‐fluid model development for dense gas–particle flows

Development of a filtered interphase heat transfer model based on fine‐grid simulations of gas–solid flows

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