In past literature, most simulations of lukewarm clouds assumed static and homogeneous conditions. We are interested in simulating more realistic regimes of warm clouds that actually are systems which live in perpetual transitional situations. These time evolutions highly depend on the turbulent air flow hosting the cloud, and on transport phenomena taking place through the complex surfaces that bound the cloud with respect to the clear air surrounding it.
In our simulations, cloud boundaries (called interfaces in the text) are modelled through the shear-less turbulent mixing, matching two interacting flow regions - a small portion of cloud, and an adjacent clear air portion of equivalent volume - at different turbulent intensities. An initial condition reproduces local stable or unstable stratification in density and temperature. The droplets model includes evaporation, condensation, collision and coalescence. The typical water content inside a warm cloud parcel of about 500 m^3, when associated to an initial condition, where drops are 30 microns in diameter, leads to an initial number of drops of the order of 10^11. A simulation grid up to 4092x2048x2048 points is sought after, which leads to a Taylor's microscale Reynolds number of 500. The governing equations are the Navier-Stokes equations under the Boussinesq‘s approximation, and are coupled to the transport equation for the water vapour represented as a passive scalar, and for drops seen as inertia particles, transported by background turbulence and gravity. The code uses a slab parallelization. The system contains a huge number of discrete elements, i.e. the water droplets, which undergo an intense clustering due to turbulent fluctuations. Turbulent clustering is not predictable, and in turn produces an imbalance on the communication rate among different cores. As a consequence, the computational burden among the cores in the cluster is not evenly distributed. This, per se, highly limits performance and binds the parallelization organization to that of a slab structure. Furthermore, clustering increases in time, and induces an inhomogeneous enhancement of the local droplets collision rate, as well as a concomitant depression of the growth in size of water droplets.
The long-term evolution of many kinds of transients must be considered in order to understand the above processes. This, in association to the variation of a quite large set of control parameters, will be the main motivation t...
In our simulations, cloud boundaries (called interfaces in the text) are modelled through the shear-less turbulent mixing, matching two interacting flow regions - a small portion of cloud, and an adjacent clear air portion of equivalent volume - at different turbulent intensities. An initial condition reproduces local stable or unstable stratification in density and temperature. The droplets model includes evaporation, condensation, collision and coalescence. The typical water content inside a warm cloud parcel of about 500 m^3, when associated to an initial condition, where drops are 30 microns in diameter, leads to an initial number of drops of the order of 10^11. A simulation grid up to 4092x2048x2048 points is sought after, which leads to a Taylor's microscale Reynolds number of 500. The governing equations are the Navier-Stokes equations under the Boussinesq‘s approximation, and are coupled to the transport equation for the water vapour represented as a passive scalar, and for drops seen as inertia particles, transported by background turbulence and gravity. The code uses a slab parallelization. The system contains a huge number of discrete elements, i.e. the water droplets, which undergo an intense clustering due to turbulent fluctuations. Turbulent clustering is not predictable, and in turn produces an imbalance on the communication rate among different cores. As a consequence, the computational burden among the cores in the cluster is not evenly distributed. This, per se, highly limits performance and binds the parallelization organization to that of a slab structure. Furthermore, clustering increases in time, and induces an inhomogeneous enhancement of the local droplets collision rate, as well as a concomitant depression of the growth in size of water droplets.
The long-term evolution of many kinds of transients must be considered in order to understand the above processes. This, in association to the variation of a quite large set of control parameters, will be the main motivation t...