Clouds play a critical role in the evolution and change of climate. However, their interaction with turbulent atmosphere is still a source of uncertainty. Direct numerical simulation (DNS) has become an indispensable tool to investigate the dynamics of atmospheric clouds. In this paper, a DNS model is presented that solves the governing equations for the flow of air, temperature, and water vapor mixing ratio in Eulerian fashion, assuming homogeneous and isotropic turbulence. Aerosol particles and cloud droplets are tracked using Lagrangian particle tracking method. Curvature and solute effects are included in our model, and the initial dry aerosol size distribution is assumed to be lognormal. By varying the intensity of flow turbulence, the mutual conversion of aerosol particles and cloud droplets is examined. The results indicate that a higher level of turbulence promotes the deactivation of cloud droplets into aerosol particles.

Direct numerical simulation (DNS) has become an indispensable tool to study turbulence-cloud-aerosol interactions. The DNS approach attempts to resolve the smallest scales of the flow, assuming homogeneous and isotropic turbulence. The governing equations for the flow of air, and two scalars-temperature, and water vapor mixing ratio, are solved in an Eulerian fashion while aerosol particles and cloud droplets are tracked using the Lagrangian method. To sustain turbulence in the scalar fields, forcing is necessary. This paper presents a DNS investigation of two scalar forcing mechanisms, in the spectral and physical space, respectively. We compare the forced and unforced scalar fields in terms of the standard deviation, the spectra, and the probability density functions. Our results show forcing leads to more fluctuations in the scalar fields and broadening of the probability distributions. Also, the rate of condensation decreases with scalar forcing. The scalar spectra for forced and unforced scalar fields are found to be different at small scales.