Validation study of a Spatially-Averaged Two-Fluid Model for heat transport in gas-particle flows

Rauchenzauner, Stefanie; Schneiderbauer, Simon

doi:10.1016/j.ijheatmasstransfer.2022.123382

Cited by 5 publications

(3 citation statements)

References 47 publications

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“…The drift flux is then used to estimate the correction of the drag force for fTFM simulations, which are feasible for industrial-scale gas–solid flows. Rauchenzauner and Schneiderbauer used the filtered particle volume fraction, the scalar variance of the subgrid particle volume fraction variation, and the kinetic energy associated with the subgrid velocity fluctuations of the particles to find the drift velocity in the dense flow regime. Both approaches work well when applied to a common data set generated by filtering the results from a dense fluidized bed simulation.…”

Section: Discussionmentioning

confidence: 99%

“…Algebraic models for the drift velocity have been proposed in several studies. 7,8,19 Rauchenzauner and Schneiderbauer 20 expressed the drift velocity in terms of the subgrid turbulent kinetic energy of the gas and the scalar variance of the particle volume fraction, which were determined by solving corresponding dynamic transport equations. In a recent study, Hardy et al 21 found that the drift velocity could be expressed in terms of the scalar variance of the particle volume fraction, with the same model applying to all filter sizes.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Parmentier et al argued that in the presence of subfilter-scale (aka subgrid-scale, as the filter size is usually the same as the grid size in coarse simulations of the fTFM equations) inhomogeneities, the average gas velocity seen by the particles is not the same as the filtered gas velocity and expressed the drag force correction in terms of a subgrid quantity known as the drift velocity. Algebraic models for the drift velocity have been proposed in several studies. ,, Rauchenzauner and Schneiderbauer expressed the drift velocity in terms of the subgrid turbulent kinetic energy of the gas and the scalar variance of the particle volume fraction, which were determined by solving corresponding dynamic transport equations. In a recent study, Hardy et al found that the drift velocity could be expressed in terms of the scalar variance of the particle volume fraction, with the same model applying to all filter sizes.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Approaches to Close the Filtered Two-Fluid Model for Gas–Solid Flows: Models for Subgrid Drag Force and Solid Phase Stress

Hardy,

Rauchenzauner,

Fede

et al. 2024

Ind. Eng. Chem. Res.

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Gas-particle flows are commonly simulated through a twofluid model at the industrial scale. However, these simulations need a very fine grid to have accurate flow predictions, which is prohibitively demanding in terms of computational resources. To circumvent this problem, the filtered two-fluid model has been developed, where the large-scale flow field is numerically resolved and small-scale fluctuations are accounted for through subgrid-scale modeling. In this study, we have performed fine-grid two-fluid simulations of dilute gas-particle flows in periodic domains and applied explicit filtering to generate data sets. Then, these data sets have been used to develop artificial neural network (ANN) models for closures such as the filtered drag force and solid phase stress for the filtered two-fluid model. The set of input variables for the subgrid drag force ANN model that has been found previously to work well for dense flow regimes is found to work as well for the dilute regime. In addition, we present a Galilean invariant tensor basis neural network (TBNN) model for the filtered solid phase stress, which can nicely capture the anisotropic nature of the solid phase stress arising from subgrid-scale velocity fluctuations. Finally, the predictions provided by this new TBNN model are compared to those obtained from a simple eddy-viscosity ANN model.

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Section: Discussionmentioning

confidence: 99%