84 research outputs found
Evaporation-triggered microdroplet nucleation and the four life phases of an evaporating Ouzo drop
Evaporating liquid droplets are omnipresent in nature and technology, such as
in inkjet printing, coating, deposition of materials, medical diagnostics,
agriculture, food industry, cosmetics, or spills of liquids. While the
evaporation of pure liquids, liquids with dispersed particles, or even liquid
mixtures has intensively been studied over the last two decades, the
evaporation of ternary mixtures of liquids with different volatilities and
mutual solubilities has not yet been explored. Here we show that the
evaporation of such ternary mixtures can trigger a phase transition and the
nucleation of microdroplets of one of the components of the mixture. As model
system we pick a sessile Ouzo droplet (as known from daily life - a transparent
mixture of water, ethanol, and anise oil) and reveal and theoretically explain
its four life phases: In phase I, the spherical cap-shaped droplet remains
transparent, while the more volatile ethanol is evaporating, preferentially at
the rim of the drop due to the singularity there. This leads to a local ethanol
concentration reduction and correspondingly to oil droplet nucleation there.
This is the beginning of phase II, in which oil microdroplets quickly nucleate
in the whole drop, leading to its milky color which typifies the so-called
'Ouzo-effect'. Once all ethanol has evaporated, the drop, which now has a
characteristic non-spherical-cap shape, has become clear again, with a water
drop sitting on an oil-ring (phase III), finalizing the phase inversion.
Finally, in phase IV, also all water has evaporated, leaving behind a tiny
spherical cap-shaped oil drop.Comment: 40 pages, 12 figure
Evaporating pure, binary and ternary droplets: thermal effects and axial symmetry breaking
The Greek aperitif Ouzo is not only famous for its specific anise-flavored
taste, but also for its ability to turn from a transparent miscible liquid to a
milky-white colored emulsion when water is added. Recently, it has been shown
that this so-called Ouzo effect, i.e. the spontaneous emulsification of oil
microdroplets, can also be triggered by the preferential evaporation of ethanol
in an evaporating sessile Ouzo drop, leading to an amazingly rich drying
process with multiple phase transitions [H. Tan et al., Proc. Natl. Acad. Sci.
USA 113(31) (2016) 8642]. Due to the enhanced evaporation near the contact
line, the nucleation of oil droplets starts at the rim which results in an oil
ring encircling the drop. Furthermore, the oil droplets are advected through
the Ouzo drop by a fast solutal Marangoni flow. In this article, we investigate
the evaporation of mixture droplets in more detail, by successively increasing
the mixture complexity from pure water over a binary water-ethanol mixture to
the ternary Ouzo mixture (water, ethanol and anise oil). In particular,
axisymmetric and full three-dimensional finite element method simulations have
been performed on these droplets to discuss thermal effects and the complicated
flow in the droplet driven by an interplay of preferential evaporation,
evaporative cooling and solutal and thermal Marangoni flow. By using image
analysis techniques and micro-PIV measurements, we are able to compare the
numerically predicted volume evolutions and velocity fields with experimental
data. The Ouzo droplet is furthermore investigated by confocal microscopy. It
is shown that the oil ring predominantly emerges due to coalescence
Network analysis of Reynolds number scaling in wall-bounded Lagrangian mixing
The dispersion and mixing of passive particles in a turbulent channel flow is
investigated by means of a network-based representation of their motion. We
employ direct numerical simulations at five different Reynolds numbers, from
= 180 up to = 950, and obtain sets of
particle trajectories via numerical integration. By dividing the channel domain
into wall-normal levels, the motion of particles across these levels is used to
build a time-varying complex network, which is able to capture the transient
phase of the wall-normal mixing process and its dependence on the Reynolds
number, . Using network metrics, we observe that the
dispersion of clouds of tracers depends highly on both their wall-normal
starting position and the time elapsed from their release. We identify two main
mechanisms that contribute to the long lasting inhibition of the dispersion of
particles released near the walls. We also show how the relative importance of
these mechanisms varies with the Reynolds number. In particular, at low
the weaker velocity fluctuations appear dominant in
inhibiting dispersion, while at higher Reynolds numbers a larger role is played
by cyclic patterns of motion. At the higher Reynolds numbers employed in this
work, we find that most network properties are Reynolds-independent when scaled
with outer flow variables. Instead, at lower , the
aforementioned scaling is not observed. We explore the meaning of the emergence
of this scaling in relation to the features of dispersion and to the network
definition.Comment: 18 pages, 7 figure
Spatial characterization of turbulent channel flow via complex networks
A network-based analysis of a turbulent channel flow numerically solved at
is proposed as an innovative perspective for the spatial
characterization of the flow field. Two spatial networks corresponding to the
streamwise and wall-normal velocity components are built, where nodes represent
portions of volume of the physical domain. For each network, links are active
if the correlation coefficient of the corresponding velocity component between
pairs of nodes is sufficiently high, thus unveiling the strongest kinematic
relations. Several network measures are studied in order to explore the
interrelations between nodes and their neighbors. Specifically, long-range
links are localized between near-wall regions and associated with the temporal
persistence of coherent patterns, namely high and low speed streaks.
Furthermore, long-range links play a crucial role as intermediary for the
kinematic information flow, as emerges from the analysis of indirect
connections between nodes. The proposed approach provides a framework to
investigate spatial structures of the turbulent dynamics, showing the full
potential of complex networks. Although the network analysis is based on the
two-point correlation, it is able to advance the level of information, by
exploiting the texture created by active links in all directions. Based on the
observed findings, the current approach can pave the way for an enhanced
spatial interpretation of the turbulence dynamics
Lagrangian network analysis of turbulent mixing
A temporal complex network-based approach is proposed as a novel formulation
to investigate turbulent mixing from a Lagrangian viewpoint. By exploiting a
spatial proximity criterion, the dynamics of a set of fluid particles is
geometrized into a time-varying weighted network. Specifically, a numerically
solved turbulent channel flow is employed as an exemplifying case. We show that
the time-varying network is able to clearly describe the particle swarm
dynamics, in a parametrically robust and computationally inexpensive way. The
network formalism enables to straightforwardly identify transient and long-term
flow regimes, the interplay between turbulent mixing and mean flow advection,
and the occurrence of proximity events among particles. Thanks to their
versatility and ability to highlight significant flow features, complex
networks represent a suitable tool for Lagrangian investigations of turbulence
mixing. The present application of complex networks offers a powerful resource
for Lagrangian analysis of turbulent flows, thus providing a further step in
building bridges between turbulence research and network science
Two- and Four-Way Coupled Euler–Lagrangian Large-Eddy Simulation of Turbulent Particle-Laden Channel Flow
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