Modeling the Influence of Inertial Clustering on the Evolution and Evaporation of Droplet Clouds

It is known that in a turbulent environment and under certain conditions, inertial particles that were initially randomly distributed tend to cluster. This phenomenon is commonly neglected in stochastic droplet simulations, in spite of being relevant in many applications where e.g. clustering significantly reduces the evaporation or condensation rate of droplets.

In this work we study statistical properties of the gas flow seen by dispersed evaporating droplets in homogeneous, isotropic, stationary turbulence. An accurate description of properties seen by particles is essential in any numerical method relying on the point-particle assumption, whether it is in the context of Reynolds Averaged Navier-Stokes or Large Eddy Simulations.

Guided by data from Direct Numerical Simulations (Weiss et. al, Physics of Fluids 30, 2018) at different flow conditions, we present a hierarchy of models ranging from simple evaporation models based on mean seen quantities to detailed Lagrangian models that account for droplet clustering. The latter incorporate in addition to droplet dynamics the joint statistics of droplet diameter and seen temperature/saturation leading to predictions of superior accuracy.