A critical assessment of the Energy Minimization Multi-Scale (EMMS) model

Pakseresht, Pedram; Yao, Yuan; Yi, Fan; Theuerkauf, Jörg; Capecelatro, Jesse

doi:10.1016/j.powtec.2023.118569

Cited by 4 publications

(3 citation statements)

References 66 publications

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“…Timo Dymala et al reported that simulations with the EMMS-based drag model show good agreement with the time-averaged axial solids concentration, circulation rate, and riser pressure drop [24]. Yuelin Yang et al and Pedram Pakseresht et al also carried out simulations using EMMS models [25,26].…”

Section: Simulation Considerationsmentioning

confidence: 99%

Numerical Simulation Study on the Gas–Solid Flow Characteristics of a Large-Scale Dual Fluidized Bed Reactor: Verification and Extension

Lin,

Wang,

Xie

et al. 2024

Energies

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Dual fluidized bed (DFB) reactor systems are widely used in gas–solid two-phase flow applications, whose gas–solid flow characteristics have a significant effect on the performance of many kinds of technologies. A numerical simulation model was established on the basis of a large-scale DFB reactor with a maximum height of 21.6 m, and numerical simulations focused on gas–solid flow characteristics were carried out. The effects of the superficial gas velocity of both beds and the static bed height and particle size on the distribution of the pressure and solid suspension density and the solid circulation rate were studied. The simulation results were in good agreement with the experimental data. With the strong support of the experimental data, the gas–solid flow characteristics of large-scale DFB reactors were innovatively evaluated in this numerical simulation study, which effectively makes up for the shortcomings of the current research. The results showed that the superficial gas velocity of both beds and the static bed height have different degrees of influence on the gas–solid flow characteristics. Specifically, for 282 μm particles, when the superficial gas velocity of both beds and the static bed height were 4.5 m/s, 2.5 m/s, and 0.65 m, respectively, under typical working conditions, the bottom pressure of the two furnaces was 3412.42 Pa and 2812.86 Pa, respectively, and the solid suspension density was 409.44 kg/m3 and 427.89 kg/m3, respectively. Based on the simulation results, the empirical formulas of the solid circulation rate were fitted according to different particle sizes. Under similar conditions, the solid circulation rates of particles with a particle size of 100 μm, 282 μm, 641 μm, and 1000 μm were 2.84–13.28, 0.73–4.91, 0.024–0.216, and 0.0026–0.0095 kg/(m2s), respectively. It can be found that the influence of the particle size on the solid circulation rate is the most significant among all parameters.

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Section: Simulation Considerationsmentioning

confidence: 99%

Numerical Simulation Study on the Gas–Solid Flow Characteristics of a Large-Scale Dual Fluidized Bed Reactor: Verification and Extension

Lin,

Wang,

Xie

et al. 2024

Energies

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“…f is the Liouville equation that describes the change in time to the particle distribution function, β is the EMMS drag model (Dymala, Wytrwat and Heinrich, 2021;Pakseresht et al, 2023), 𝒖 𝒇 − 𝒖 𝒑 is the slip velocity. The equation of momentum for the gas and particle is a connector between the equations in the gas and the particle where to predict the movement of particles by the Lioville equation.…”

Section: Governing Equationsmentioning

confidence: 99%

Computational prediction of green fuels from crude palm oil in fluid catalytic cracking riser

Nuryadi,

Purwanto,

Susmayanti

et al. 2023

Int. J. Renew. Energy Dev.

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Fluid catalytic cracking could convert crude palm oil into valuable green fuels to substitute fossil fuels. This study aimed to predict the phenomenon and green fuels yield in the industrial fluid catalytic cracking riser using computational fluid dynamics. A three-dimensional transient simulation using the Eulerian-Lagrangian with the multiphase particle-in-cell is to investigate reactive gas-particle hydrodynamics and the four-lump kinetic network model with the rare earth-Y catalyst for crude palm oil cracking behaviors. The study results show that the fluid and catalyst velocity profile increase in the middle of the riser reactor because the cracking reaction process that produces OLP and Gas products has a lighter molecular weight. The endothermic reaction causes the temperature profile to decrease because the heat of the reaction comes from the catalyst. This analysis shows that the simulation accurately predicts green fuel products from crude palm oil. As a result, the crude palm oil conversion, organic liquid product yield, and Gas yield correspond to 70 wt%, 28.8 wt%, and 27.5 wt%, respectively. Compared to the experimental study, the computational prediction of yield products showed good agreement and determined the optimal riser dimension. The methodology and results are guidelines for optimizing the FCC riser process using CPO.

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“…The drag correction models accounting for the effects of unresolved drag due to particle clustering at mesoscale in the literature can be classified as follows. The Energy Minimization Multi-Scale (EMMS) model − describes subgrid structures through a heterogeneous index, which is used to estimate the effective drag force. In the framework of fTFM, the explicit correlations were proposed by Igci et al, Milioli et al, Sarkar et al for the filtered drag in terms of the filtered variables and the filter size.…”

Section: Introductionmentioning

confidence: 99%

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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A critical assessment of the Energy Minimization Multi-Scale (EMMS) model

Cited by 4 publications

References 66 publications

Numerical Simulation Study on the Gas–Solid Flow Characteristics of a Large-Scale Dual Fluidized Bed Reactor: Verification and Extension

Numerical Simulation Study on the Gas–Solid Flow Characteristics of a Large-Scale Dual Fluidized Bed Reactor: Verification and Extension

Computational prediction of green fuels from crude palm oil in fluid catalytic cracking riser

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

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