Mass, momentum, and energy transfer in bubbly flows strongly depends on the bubble’s size distribution, which determines the contact area between the interacting phases. Characterization of bubble sizes in polydisperse flows requires empirical modelling of sub-grid physical mechanisms such as break-up and coalescence. In the present work an adaptive multiple size-group (A-MuSiG) method is incorporated into the Eulerian multiphase solver available in Simcenter STAR-CCM+ in order to model polydisperse bubbly flows in horizontal and vertical channels. The disperse phasespace is discretized into multiple size-groups each represented by its own size, number-density, and velocity field. The diameter of the bubbles in each of the size-groups varies in time and space, dynamically adapting to the local flow conditions. The interphase momentum transfer between the continuous phase and polydisperse bubbles is modelled through drag, virtual mass, turbulent dispersion, and lift forces. For modelling sub-grid bubble break-up and coalescence processes, different phenomenological kernels are evaluated. The empirical parameters of the adopted kernels are calibrated in two steps. The initial stage of the analysis considers experimental channel flows at low Reynolds number and zero-gravity conditions, under which the bubble size distribution is solely dependent on coalescence. As part of the second phase of the evaluation, additional parametric simulations in turbulent channel flows are performed in order to calibrate the break-up models, assuming the coalescence scaling constants derived in the previous step. The obtained results demonstrate that in flows with high turbulent mixing the ensuing bubble dynamics are strongly coupled to the internal properties of the population, which in turn influence the developing multiphase interactions in a transient manner.
CITATION STYLE
Papoulias, D., Vichansky, A., & Tandon, M. (2021). Multi-fluid modelling of bubbly channel flows with an adaptive multi-group population balance method. Experimental and Computational Multiphase Flow, 3(3), 171–185. https://doi.org/10.1007/s42757-020-0084-5
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