Wu, Kaiqiao; (2020) Dynamically Structured Flow in Pulsed Fluidised Beds. Doctoral thesis (Ph.D), UCL (University College London).

Abstract

Bubbling fluidised reactors provide good mixing and transport rates for a broad range of gas-solid processes in the industry. Their overall performance relies highly on the macroscopic flow of bubbles, which is usually chaotic and unstable due to the complex dissipative inter-particle collisions and interfacial friction. Applying an oscillatory gas flow, under certain conditions, can induce a recursive flow structure where bubbles positions become perfectly ordered following an array of triangular tessellation. The phenomenon manifests great potential to benefit the reactor design, control and rational scale-up. The thesis combines both experiments and simulations to explore the dynamically structured flows in pulsed fluidised beds. Its reproducibility has been evaluated experimentally with a designed intensity index that quantifies the degree-of-order introduced in the flow pattern. We also identify the operation regimes of structured bubbling flows via the comparison across flow conditions and particulate bed depths. Unlike a steady flow fluidised system, the bubbles in a structured flow were detected to exhibit a narrower size and separation distribution, which is directly correlated to the oscillation applied, and hereby can be controlled and manipulated precisely. In the same work, the dynamics of the structured flows has been investigated using two different modelling strategies, two-fluid models (Euler-Euler approach) and discrete element methods (Euler-Lagrange approach). The simulated flow reveals the dynamic nature that the particulate phase periodically alternates between the viscous and plastic regimes. When solids contract, the system creates temporally locked regions of solids that were found to synchronise the nucleation of bubbles and stabilise the flow structures. Besides, due to inherent assumptions of the two-fluid model, the simulated system fails to find a structured flow. These data, in turn, highlight the power of pattern formation as a robust “fingerprint” for gas-solid multiphase model validation and development.

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