@Article{CiCP-32-2,
author = {P., N., Karun, Datadien and Staso, Gianluca, Di and Wijshoff, M., Herman, A. and Toschi, Federico},
title = {A Quantitative Comparison of Physical Accuracy and Numerical Stability of Lattice Boltzmann Color Gradient and Pseudopotential Multicomponent Models for Microfluidic Applications},
journal = {Communications in Computational Physics},
year = {2022},
volume = {32},
number = {2},
pages = {450--489},
abstract = {<p style="text-align: justify;">The performances of the Color-Gradient (CG) and the Shan-Chen (SC) multicomponent Lattice Boltzmann models are quantitatively compared side-by-side on
multiple physical flow problems where breakup, coalescence and contraction of fluid
ligaments are important. The flow problems are relevant to microfluidic applications,
jetting of microdroplets as seen in inkjet printing, as well as emulsion dynamics. A
significantly wider range of parameters is shown to be accessible for CG in terms of
density-ratio, viscosity-ratio and surface tension values. Numerical stability for a high
density ratio $\mathcal{O}(1000)$ is required for simulating the drop formation process during
inkjet printing which we show here to be achievable using the CG model but not using
the SC model. Our results show that the CG model is a suitable choice for challenging
simulations of droplet formation, due to a combination of both numerical stability and
physical accuracy. We also present a novel approach to incorporate repulsion forces
between interfaces for CG, with possible applications to the study of stabilized emulsions. Specifically, we show that the CG model can produce similar results to a known
multirange potentials extension of the SC model for modelling a disjoining pressure,
opening up its use for the study of dense stabilized emulsions.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.OA-2021-0204},
url = {https://global-sci.com/article/90821/a-quantitative-comparison-of-physical-accuracy-and-numerical-stability-of-lattice-boltzmann-color-gradient-and-pseudopotential-multicomponent-models-for-microfluidic-applications}
}