3,259 research outputs found
Effect of turbulence on collisional growth of cloud droplets
We investigate the effect of turbulence on the collisional growth of um-sized
droplets through high- resolution numerical simulations with well resolved
Kolmogorov scales, assuming a collision and coalescence efficiency of unity.
The droplet dynamics and collisions are approximated using a superparticle
approach. In the absence of gravity, we show that the time evolution of the
shape of the droplet-size distribution due to turbulence-induced collisions
depends strongly on the turbulent energy-dissipation rate, but only weakly on
the Reynolds number. This can be explained through the energy dissipation rate
dependence of the mean collision rate described by the Saffman-Turner collision
model. Consistent with the Saffman-Turner collision model and its extensions,
the collision rate increases as the square root of the energy dissipation rate
even when coalescence is invoked. The size distribution exhibits power law
behavior with a slope of -3.7 between a maximum at approximately 10 um up to
about 40 um. When gravity is invoked, turbulence is found to dominate the time
evolution of an initially monodisperse droplet distribution at early times. At
later times, however, gravity takes over and dominates the collisional growth.
We find that the formation of large droplets is very sensitive to the turbulent
energy dissipation rate. This is due to the fact that turbulence enhances the
collisional growth between similar sized droplets at the early stage of
raindrop formation. The mean collision rate grows exponentially, which is
consistent with the theoretical prediction of the continuous collisional growth
even when turbulence-generated collisions are invoked. This consistency only
reflects the mean effect of turbulence on collisional growth
Cloud microphysical effects of turbulent mixing and entrainment
Turbulent mixing and entrainment at the boundary of a cloud is studied by
means of direct numerical simulations that couple the Eulerian description of
the turbulent velocity and water vapor fields with a Lagrangian ensemble of
cloud water droplets that can grow and shrink by condensation and evaporation,
respectively. The focus is on detailed analysis of the relaxation process of
the droplet ensemble during the entrainment of subsaturated air, in particular
the dependence on turbulence time scales, droplet number density, initial
droplet radius and particle inertia. We find that the droplet evolution during
the entrainment process is captured best by a phase relaxation time that is
based on the droplet number density with respect to the entire simulation
domain and the initial droplet radius. Even under conditions favoring
homogeneous mixing, the probability density function of supersaturation at
droplet locations exhibits initially strong negative skewness, consistent with
droplets near the cloud boundary being suddenly mixed into clear air, but
rapidly approaches a narrower, symmetric shape. The droplet size distribution,
which is initialized as perfectly monodisperse, broadens and also becomes
somewhat negatively skewed. Particle inertia and gravitational settling lead to
a more rapid initial evaporation, but ultimately only to slight depletion of
both tails of the droplet size distribution. The Reynolds number dependence of
the mixing process remained weak over the parameter range studied, most
probably due to the fact that the inhomogeneous mixing regime could not be
fully accessed when phase relaxation times based on global number density are
considered.Comment: 17 pages, 10 Postscript figures (figures 3,4,6,7,8 and 10 are in
reduced quality), to appear in Theoretical Computational Fluid Dynamic
Eulerian and modified Lagrangian approaches to multi-dimensional condensation and collection
Turbulence is argued to play a crucial role in cloud droplet growth. The
combined problem of turbulence and cloud droplet growth is numerically
challenging. Here, an Eulerian scheme based on the Smoluchowski equation is
compared with two Lagrangian superparticle (or su- perdroplet) schemes in the
presence of condensation and collection. The growth processes are studied
either separately or in combination using either two-dimensional turbulence, a
steady flow, or just gravitational acceleration without gas flow. Good
agreement between the differ- ent schemes for the time evolution of the size
spectra is observed in the presence of gravity or turbulence. Higher moments of
the size spectra are found to be a useful tool to characterize the growth of
the largest drops through collection. Remarkably, the tails of the size spectra
are reasonably well described by a gamma distribution in cases with gravity or
turbulence. The Lagrangian schemes are generally found to be superior over the
Eulerian one in terms of computational performance. However, it is shown that
the use of interpolation schemes such as the cloud-in-cell algorithm is
detrimental in connection with superparticle or superdroplet approaches.
Furthermore, the use of symmetric over asymmetric collection schemes is shown
to reduce the amount of scatter in the results.Comment: 36 pages, 17 figure
Kinematic and Dynamic Pair Collision Statistics of Sedimenting Inertial Particles Relevant to Warm Rain Initiation
In recent years, direct numerical simulation (DNS) approach has become a reliable tool for studying turbulent collision-coalescence of cloud droplets relevant to warm rain development. It has been shown that small-scale turbulent motion can enhance the collision rate of droplets by either enhancing the relative velocity and collision efficiency or by inertia-induced droplet clustering. A hybrid DNS approach incorporating DNS of air turbulence, disturbance flows due to droplets, and droplet equation of motion has been developed to quantify these effects of air turbulence. Due to the computational complexity of the approach, a major challenge is to increase the range of scales or size of the computation domain so that all scales affecting droplet pair statistics are simulated. Here we discuss our on-going work in this direction by improving the parallel scalability of the code, and by studying the effect of large-scale forcing on pair statistics relevant to turbulent collision. New results at higher grid resolutions show a saturation of pair and collision statistics with increasing flow Reynolds number, for given Kolmogorov scales and small droplet sizes. Furthermore, we examine the orientation dependence of pair statistics which reflects an interesting coupling of gravity and droplet clustering
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