Journal article

Numerical investigation of hydrodynamics and heat transfer of elongated bubbles during flow boiling in a microchannel

Flow boiling within microchannels has been explored intensively in the last decade due to their capability to remove high heat fluxes from microelectronic devices. However, the contribution of experiments to the understanding of the local features of the flow is still severely limited by the small scales involved. Instead, multiphase CFD simulations with appropriate modeling of interfacial effects overcome the current limitations in experimental techniques. Presently, numerical simulations of single elongated bubbles in flow boiling conditions within circular microchannels were performed. The numerical framework is the commercial CFD code ANSYS Fluent 12 with a Volume Of Fluid interface capturing method, which was improved here by implementing, as external functions, a Height Function method to better estimate the local capillary effects and an evaporation model to compute the local rates of mass and energy exchange at the interface. A detailed insight on bubble dynamics and local patterns enhancing the wall heat transfer is achievable utilizing this improved solver. The numerical results show that, under operating conditions typical for flow boiling experiments in microchannels, the bubble accelerates downstream following an exponential time-law, in good agreement with theoretical models. Thin-film evaporation is proved to be the dominant heat transfer mechanism in the liquid film region between the wall and the elongated bubble, while transient heat convection is found to strongly enhance the heat transfer performance in the bubble wake in the liquid slug between two bubbles. A transient-heat-conduction-based boiling heat transfer model for the liquid film region, which is an extension of a widely quoted mechanistic model, is proposed here. It provides estimations of the local heat transfer coefficient that are in excellent agreement with simulations and it might be included in next-generation predictive methods.


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