CFD-DEM simulations of riser geometry effect and cluster phenomena

Mu, L.; Buist, Kay A.; Kuipers, J.A.M.; Deen, NG Niels

doi:10.1016/j.apt.2021.07.007

Cited by 11 publications

(6 citation statements)

References 36 publications

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“…Therefore, the new particles in the simulations are injected in a narrow section in the bottom of the riser with a low velocity of {0.0, 0.0, 0.1} m/s. The effect on the global predictions of the riser of selecting this approach over a more realistic lateral feeding of the particles has been found negligible . In the case of overlap of an injected particle with an already present particle in the simulation, the model keeps trying to place new particles without any overlapping maximally 100 times.…”

Section: Methodsmentioning

confidence: 99%

“…Finally, the different implementations of the drag force are assessed on the basis of the predictions in the clustering behavior, which are compared with the experiments in a pseudo 2D riser. 29,30 Although this work studies the same system as Varas et al 29 and Mu et al, 18 the current study focuses on the drag formulations used in the simulations, which neither of them discussed.…”

Section: Introductionmentioning

confidence: 98%

“…In CFD-DEM, each particle is handled individually using Newton’s second law of motion, while the gas phase is treated as a continuous phase by solving the Navier–Stokes and continuity equations. Different authors have evaluated the effect of several system characteristics on the properties of the clusters, such as operational conditions, ,,, collision parameters, ,, lift force, inlet and/or outlet configurations, and equipment dimensions. , Apart from the collision dynamics, the interphase momentum exchange also affects the formation of clusters and the overall operation of the riser. One of the most notable gas–particle interactions is the drag, of which many formulations have been numerically tested. ,,,, One of the challenges in selecting an appropriate drag model is that the magnitude of the predicted drag force among the different drag closures can vary significantly depending on the values of local gas fraction, ε g , and particle Reynolds number, Re .…”

Section: Introductionmentioning

confidence: 99%

“…Afterward these differences are explained by relating the results to the normalized drag force obtained for the different approaches for the averaged conditions in the riser. Finally, the different implementations of the drag force are assessed on the basis of the predictions in the clustering behavior, which are compared with the experiments in a pseudo 2D riser. , Although this work studies the same system as Varas et al and Mu et al, the current study focuses on the drag formulations used in the simulations, which neither of them discussed.…”

Section: Introductionmentioning

confidence: 99%

“…In CFD-DEM, each particle is handled individually using Newton's second law of motion, while the gas phase is treated as a continuous phase by solving the Navier−Stokes and continuity equations. Different authors have evaluated the effect of several system characteristics on the properties of the clusters, such as operational conditions, 18,25,27,32 collision parameters, 11,25,32 lift force, 11 inlet and/or outlet configurations, 18 and equipment dimensions. 11,27 Apart from the collision dynamics, the interphase momentum exchange also affects the formation of clusters and the overall operation of the riser.…”

Section: Introductionmentioning

confidence: 99%

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CFD-DEM Evaluation of the Clustering Behavior in a Riser─the Effect of the Drag Force Model

Ramírez,

de Munck,

Liu

et al. 2023

Ind. Eng. Chem. Res.

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Riser reactors are frequently applied in catalytic processes involving rapid catalyst deactivation. Typically heterogeneous flow structures prevail because of the clustering of particles, which impacts the quality of the gas–solid contact. This phenomenon results as a competition between fluid–particle interaction (i.e., drag) and particle–particle interaction (i.e., collisions). In this study, five drag force correlations were used in a combined computational fluid dynamics–discrete element method Immersed Boundary Model to predict the clustering. The simulation results were compared with experimental data obtained from a pseudo-2D riser in the fast fluidization regime. The clusters were detected on the basis of a core–wake approach using constant thresholds. Although good predictions for the global (solids volume fraction and mass flux) variables and cluster (spatial distribution, size, and number of clusters) variables were obtained with two of the approaches in most of the simulations, all the correlations show significant deviations in the onset of a pneumatic transport regime. However, the correlations of Felice (Int. J. Multiphase Flow199420153159) and Tang et al. [AIChE J.201561688698] show the closest correspondence for the time-averaged quantities and the clustering behavior in the fast fluidization regime.

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Section: Methodsmentioning

confidence: 99%