Buoyancy driven motion of non-coalescing inertial drops: microstructure modeling with nearest particle statistics
arxiv(2024)
摘要
In this study, we analyze the various arrangements that droplets can form
within dispersed buoyant emulsions, which we refer to as the study of
microstructure. To this end, we have developed a novel algorithm that
effectively prevents numerical coalescence between drops while maintaining a
reasonable computational cost. This algorithm is integrated into the Volume of
Fluid (VoF) method and implemented using the open-source code
http://basilisk.fr. Subsequently, we perform Direct Numerical Simulations (DNS)
of statistically steady state mono-disperse buoyant emulsion over a broad range
of dimensionless parameters, including the particle volume fraction (ϕ),
the Galileo number (Ga) and the viscosity ratio (λ). We make use of
nearest particle statistics to quantify the microstructure properties. As
predicted by Zhang et al. (2023), it is demonstrated that the second moment of
the nearest particle pair distribution can effectively quantify microstructural
features such as particle clusters and layers. Specifically, the findings are:
(1) In moderately inertial flows (Ga = 10), droplets form isotropic clusters.
In high inertial regimes (Ga = 100), non-isotropic clusters, such as
horizontal layers, are more likely to form. (3) The viscosity ratio plays a
significant role in determining the microstructure, with droplets that are less
viscous or equally viscous as the surrounding fluid tending to form layers
preferentially. Overall, our study provides a quantitative measure of the
microstructure in terms of Ga, ϕ and λ.
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