A Hybrid Computational Approach for Turbulent Collision-Coalescence of Cloud Droplets

While it has long been speculated that air turbulence could accelerate the collision-coalescence of cloud droplets and as such promotes the formation of warm rain, progress has been very slow in quantifying the turbulence effects. This results from the complexity of the problem and the lack of quantitative research tools. In this talk, we will report on an on-going, systematic effort to quantify various effects of turbulence on the rate of collision-coalescence of small cloud droplets, including (1) the enhanced relative motion due to differential acceleration and shear effects, (2) enhanced average pair density due to local clustering of droplets, and (3) enhanced collision efficiency due to turbulent fluctuations.

Recently, we have developed a Hybrid Direct Numerical Simulation (HDNS) approach to treat the motion and interactions of a large number of particles suspended in a turbulent flow. The HDNS approach integrates an improved superposition method for the disturbance flows due to droplets into a pseudospectral simulation of undisturbed air turbulence. This allows, for the first time, the direct incorporation of hydrodynamic interactions within DNS and computations from first principles of statistical information related to collision-coalescence. We are currently looking into various methods to further improve the HDNS approach in order to account for near-field lubrication forces and non-continuum effects. This talk will present the HDNS approach and its further improvements, as well as results on statistics related to turbulent collision-coalescence of cloud droplets. Results from the HDNS approach will also be compared with some published experimental observations on the motion of hydrodynamically-interacting particles in a turbulent flow.