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

Schneiderbauer, Simon

doi:10.1002/aic.16142

Cited by 37 publications

(34 citation statements)

References 75 publications

(241 reference statements)

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“…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%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2020

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“…In previous studies , a different approach for deriving constitutive relations for the unresolved terms appearing in fTFM has been presented. On the one hand, a spatially averaged two‐fluid model (SA‐TFM) was advanced, which is based on the concepts of turbulence modeling .…”

Section: Introductionmentioning

confidence: 99%

Verification and Validation of Spatially Averaged Models for Fluidized Gas‐Particle Suspensions

Schneiderbauer

2020

Chem Eng & Technol

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Two different methods for closure modeling of the unresolved terms appearing in the filtered two-fluid model are discussed and compared. The spatially averaged two-fluid model is based on generalizing the concepts of large eddy simulation to gas-particle flows. In the approximate deconvolution method-two-fluid model approach, the unresolved terms are modeled by an approximate deconvolution method, where an approximation of the unfiltered solution is obtained by repeated filtering. Finally, these models are applied to a lab-scale and a pilot-scale fluidized bed. Both approaches yield fairly good agreement with a highly resolved reference simulation as well as with experimental data. Additionally, both methods deliver reasonable grid-independent solutions up to a grid resolution of 2 cm in the case of Geldart type A particles.

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“…A lot of efforts have been made to develop and validate mesoscopic (e.g., filtered model) TFM and macroscopic (e.g., averaged model) TFM . Heterogeneous drag development and validation are one of the most critical parts of coarse‐grid simulation and it is the focus of many previous studies .…”

Section: Introductionmentioning

confidence: 99%

Assessment of mesoscale solid stress in coarse‐grid TFM simulation of Geldart A particles in all fluidization regimes

Gao

Rogers

2018

AIChE Journal

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This study focused on assessing the effect of mesoscale solid stress in the coarse grid two‐fluid model (TFM) simulation of gas–solid fluidized beds of Geldart Group A particles over a broad range of flow regimes, including bubbling, turbulent, fast, and pneumatic transport fluidization regimes. Particularly, the impact of mesoscale solid pressure, mesoscale solid viscosity, and mesoscale solid stress anisotropy were investigated by comparing six different coarse‐grid TFM settings. Compared with the available experimental data, it is found that both the kinetic theory‐based TFM with only drag correction and the filtered TFM can predict the flow behavior in all fluidization regimes. Mesoscale solid pressure and viscosity have the opposite impact on flow hydrodynamics; they compete and offset each other, which confirms the assumption employed in many previous studies that the mesoscale solid stress could be neglected in coarse‐grid TFM simulation. Published 2018. This article is a U.S. Government work and is in the public domain in the USA. AIChE J, 64: 3565–3581, 2018

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

Cited by 37 publications

References 75 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

Verification and Validation of Spatially Averaged Models for Fluidized Gas‐Particle Suspensions

Assessment of mesoscale solid stress in coarse‐grid TFM simulation of Geldart A particles in all fluidization regimes

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