(87d) Structured Bubbling Fluidized Beds: Nucleation and Self-Arrangement Under Pulsation

Fluidized beds offer good mixing and transport rates in a wide range of gas-solid operations. The structure of the solids determines the transport resistances at a microscale, but the overall performance is often driven by the macroscopic flow patterns. The balance between the energy input due to drag and its dissipation in inelastic collisions originates very complex flows. Homogenous expansion, bubbling or channeling appear from the interplay of particle-particle interactions and the action of drag and gravity. The analysis of the momentum equations reveals the appearance of hydrodynamic instabilities [1], but not how they degenerate into chaotic hydrodynamics for which a numerical framework must be used. Kinetic models have had an undeniable success in describing the behavior of large scale granular systems, but they still rely on a simplified description of particle interactions and struggle to deal with systems dominated by dense flows [2].

While fluidized beds provide good mixing, their hydrodynamics are difficult to predictable and hard to scale. Alternatives reactors, such as fixed beds are easier to scale, however they suffer from heterogeneity, cannot deal with highly exothermic reactions and are difficult to control. In recent years, more complex designs have tried to incorporate new degrees of freedom to combine both attributes: good mixing with predictability and control [3,4]. Among them, the use of an oscillating gas is a promising route. Pulsation has been used for decades to improve the fluidization of cohesive powders. This work discusses how it can also be used to form a reproducible flow and control the dynamics of bubbling beds. An oscillatory flow introduces a temporal dynamic in the drag that, when properly balanced, synchronizes the nucleation of a bubble with the formation of intermittent granular structures that are repeated in every pulsation, hereby forming a recursive flow structure. Its reproducibility has been analysed with a pattern intensity index Λ that quantifies the level of order introduced in the bubble dynamic. Comparison across operating conditions, particle size and various bed geometries allows us to identify two operation regimes: unstructured and structured bubble flow. In the earlier, the oscillation does not suppress the hydrodynamic instability and the disposition of the bubbles remains largely chaotic and comparable to that of a constant flow. In the latter, the bubbles become perfectly ordered in a triangular tessellation; thus, they exhibit a narrower size and separation distribution, which is directly correlated to the frequency f and amplitude A of the oscillation used, and hereby may be controlled.

In this contribution, experimental data are compared with numerical studies under different flows, inter-particle friction factors, m, and heights. Comparison of the nucleation process predicted by continuous and discrete formulations showcases the limitations of the current closures of kinetic models (KTGF/TFM). In contrast, Eulerian-Lagrangian frameworks (CFD-DEM) explicitly solve the local granular rheology and reproduce the experimental bubble pattern [5], but also raise interesting questions in relation to the prediction of the energy dissipation. The energy dissipated through friction appears to be key in the formation of clustered structures between the gas bubbles and in their wake. The flow around these regions generate compressive stresses that maintain particles together and lock in frictional contact. These regions act as “pivots” that restrict the mobility of the solids across the wakes and prevent any long-range solid circulation. As the flow becomes compartmentalized in small regions between neighboring wakes, the nucleation in a subsequent pulse becomes a tighter function of the position of the existing bubbles. A continuous numerical framework based on the Kinetic Theory of Granular Flow KTFG is unable to track this phenomenon, because the closures for the frictional stress cannot deal with such rapid changes in rheology, due to clustering, and they overpredict the solids mobility. In contrast, a CFD-DEM frame is able to track correctly the nucleation mechanism. Some interesting discrepancies however remain between CFD-DEM and the experimental bubble nucleation in the history of the bubble shape and its rising velocity that stress the importance of other numerical factors in the stabilization of a pattern in DEM, such as the calibration of mechanical properties or the effect of polydispersity.

References

[1] Batchelor G.K. Secondary instability of a gas-fluidized bed, J. Fluid Mech. 251 (1993), 359-311.

[2] Chialvo S., Sun J., Sundaresan S. Bridging the rheology of granular flows in three regimes Physical Review E 85, 021305 (2012) 1-8.

[3] Coppens M.O., Ommen J.R.van, Structuring chaotic fluidized beds, Chem. Eng. J. 96 (2003) 117-124.

[4] Niyogi K., Torregrosa M.M., Pantzal N., Heynderickx G.J., Marin G.B. (2017). Experimentally validated numerical study of gas-solid vortex unit hydrodynamics. Powder Technology. 305, 794-808.

[5] Wu K, De Martin L., Coppens M-O. (2017). Pattern formation in pulsed gas-solid fluidized beds – The role of granular solid mechanics. Chemical Engineering Journal, 329, 4-14.