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

Schneiderbauer, Simon

doi:10.1002/aic.16141

Cited by 35 publications

(35 citation statements)

References 42 publications

(147 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, in the literature, some studies suggested that the free slip 14,49 (e.g., specularity coefficient φ ≤ 0.001) or partial slip 9,18 condition is able to appropriately describe the particle–wall interactions in wall‐bounded FBs. Zhu et al 21,59 unveiled that coarse‐grid simulations with various particle‐wall slip conditions still could predict macroscopic flow characteristics reasonably well.…”

Section: Application Of ML Modelmentioning

confidence: 99%

“…Alternatively, subgrid models (SGMs) for interphase closure corrections such as the energy minimization multi‐scale (EMMS), 7‐9 filtered TFM (fTFM), 2,10‐15 spatially‐averaged TFM (SA‐TFM), 16‐18 and gradient‐based method, 19‐21 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.…”

Section: Introductionmentioning

confidence: 99%

“…Then, the more elaborate two‐marker closure modification 8‐10 reveals the significant slip characteristics classifying the mesoscale structures into changing degrees of heterogeneity. Recently, the third‐class closure has been developed and it requires modeling additional markers (e.g., the drift velocity, 13,24,25 the scalar variance of the solids hold‐up and large‐scale flow fluctuations, 16‐18 etc.) in terms of subgrid quantities directly available in fTFM simulations.…”

Section: Introductionmentioning

confidence: 99%

“…For cluster‐induced turbulence, Capecelatro et al 26,27 derived physically accurate predictive macroscopic stress closures via RANS‐based turbulence modeling 28 . Unlike the above studies, Schneiderbauer 16‐18 followed the concepts of large eddy simulations 29 and developed a third‐class of SA‐TFM that models the cluster‐induced turbulence to formulate the subgrid drag correction using the scalar variance of the solids hold‐up and large‐scale flow fluctuations of two phases as markers. 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.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

2020

View full text Add to dashboard Cite

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...

show abstract

Section: Application Of ML Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

2020

View full text Add to dashboard Cite

show abstract

“…Modeling particle‐laden flows is of major interest in developing a better understanding of these systems and, in an industrial context, improving process optimization, troubleshooting, and scale‐up. There has been a variety of approaches to modeling these systems including direct numerical simulation (DNS), Eulerian–Lagrangian (EL) simulation, and Eulerian–Eulerian (EE) simulation 1‐7 . One major challenge in doing so involves not only successfully resolving the features present but also doing so in a way that is computationally feasible and efficient 8,9 .…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Validation study on spatially averaged two‐fluid model for gas–solid flows: I. A priori analysis of wall bounded flows

Schneiderbauer

2018

AIChE Journal

Self Cite

View full text Add to dashboard Cite

In our prior study (Schneiderbauer, AIChE J, 2017;63(8):3544-3562), we presented a spatially averaged two-fluid model, where closure models for the unresolved terms were derived. These closures require constitutive relations for the turbulent kinetic energies (TKEs) of the gas and solids phase as well as for the sub-filter variance of the solids volume fraction (VVF). In this study, we have performed highly resolved TFM simulations of a set of three-dimensional wall dominated periodic channels. An a priori analysis shows that these closures are able to correctly predict the wall profiles of the sub-grid drag modification, the TKEs, the turbulent viscosities and the VVF without requiring special wall corrections. Solely the mixing lengths, which is required by the closures, has to be adapted in the vicinity of wall similar to single-phase turbulence; in particular, the minimum of the filter size and the distance to the wall should be used.

show abstract

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

Cited by 35 publications

References 42 publications

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

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

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

Validation study on spatially averaged two‐fluid model for gas–solid flows: I. A priori analysis of wall bounded flows

Contact Info

Product

Resources

About