The role of clusters on heat transfer in sedimenting gas-solid flows

Guo, Liejin; Capecelatro, Jesse

doi:10.1016/j.ijheatmasstransfer.2018.12.065

Cited by 28 publications

(26 citation statements)

References 67 publications

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“…Thus, future investigations should try to better avoid such numerical artifacts by adopting a larger domain size. Besides, future ML study will be extended to precisely understand complex heat transport characteristics 62,63 and construct reliable subgrid interphase transfer closure for dense gas–particle flows.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

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

2020

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“…due to dissipation during inelastic collisions (Hopkins & Louge 1991;Goldhirsch & Zanetti 1993), viscous damping by the fluid (Wylie & Koch 2000), preferential concentration 0.001 0.0255 0.05 0.001 0.0255 0.05 0.001 0.0225 0.05 of particles by the background turbulence (Eaton & Fessler 1994) and instabilities that arise due to interphase coupling (Glasser, Sundaresan & Kevrekidis 1998;Agrawal et al 2001;Capecelatro, Desjardins & Fox 2014. Such large-scale heterogeneity can effectively 'demix' the underlying flow, reducing contact between the phases and resulting in enormous consequences at scales much larger than the size of individual particles, such as hindered heat and mass transfer (Agrawal et al 2013;Miller et al 2014;Pouransari & Mani 2017;Beetham & Capecelatro 2019;Guo & Capecelatro 2019). The focus of the present work is on modelling disperse multiphase turbulence at length scales much larger than the particle diameter.…”

Section: Introductionmentioning

confidence: 99%

Sparse identification of multiphase turbulence closures for coupled fluid–particle flows

2021

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“…Similar to the sub-filtered viscous stress tensor appearing in (15), in low-Mach number formulations R q is sometimes modeled as an effective thermal conductivity to account for enhanced heat dissipation at the particle scale [11,21,53]. Volume filtering the work due to pressure in…”

Section: Filtered Energymentioning

confidence: 99%

“…which does not result in any residual contributions if the Favre-filtered temperature is used in the filtered heat flux (21). Filtering the thermodynamic relation between pressure and energy results in…”

Section: Filtered Equation Of Statementioning

confidence: 99%

A volume-filtered description of compressible particle-laden flows

Shallcross

Fox

Capecelatro

2020

International Journal of Multiphase Flow

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In this work, we present a rigorous derivation of the volume-filtered viscous compressible Navier-Stokes equations for disperse two-phase flows. Compared to incompressible flows, many new unclosed terms appear. These terms are quantified via a posteriori filtering of two-dimensional direct simulations of shock-particle interactions. We demonstrate that the pseudo-turbulent kinetic energy (PTKE) systematically acts to reduce the local gas-phase pressure and consequently increase the local Mach number. Its magnitude varies with volume fraction and filter size, which can be characterized using a Knudsen number based on the filter size and inter-particle spacing. A transport equation for PTKE is derived and closure models are proposed to accurately capture its evolution. The resulting set of volume-filtered equations are implemented within a highorder Eulerian-Lagrangian framework. An interphase coupling strategy consistent with the volume filtered formulation is employed to ensure grid convergence. Finally PTKE obtained from the volume-filtered Eulerian-Lagrangian simulations are compared to a series of two-and three-dimensional direct simulations of shocks passing through stationary particles.

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The role of clusters on heat transfer in sedimenting gas-solid flows

Cited by 28 publications

References 67 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

Sparse identification of multiphase turbulence closures for coupled fluid–particle flows

A volume-filtered description of compressible particle-laden flows

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