Clustering and entrainment effects on the evaporation of dilute droplets in a turbulent jet

Abstract

The evaporation of droplets within turbulent sprays involves unsteady, multiscale, and multiphase processes which make its comprehension and modeling capabilities still limited. The present work aims to investigate the dynamics of droplet vaporization within a turbulent spatial developing jet in dilute, nonreacting conditions. We address the problem considering a turbulent jet laden with acetone droplets and using the direct numerical simulation framework based on a hybrid Eulerian-Lagrangian approach and the point droplet approximation. A detailed statistical analysis of both phases is presented. In particular, we show how crucial is the preferential sampling of the vapor phase induced by the inhomogeneous localization of the droplets through the flow. Strong droplet preferential segregation develops suddenly downstream from the inflow section both within the turbulent core and the jet mixing layer. Two distinct mechanisms have been found to drive this phenomenon: the inertial small-scale clustering in the jet core and the intermittent dynamics of droplets across the turbulent-nonturbulent interface in the mixing layer, where dry air entrainment occurs. These phenomenologies strongly affect the overall vaporization process and lead to an impressive widening of the droplet size and vaporization rate distributions in the downstream evolution of the turbulent spray.

A model to predict the short-term turbulent indoor dispersion of small droplets and droplet nuclei released from coughs and sneezes

“…According to Fabregat et al, 6 the overall characteristics of the cloud constituted by small particles (4, 8, 16 and 32 μm) were essentially independent of the evaporation of particles. Evaporation was found to effectively decrease the diameter of smaller particles, as reported elsewhere, 7,47–51 but the shape of the cloud of small droplets and droplets nuclei (<20–30 μm) was insensitive to the evaporation (see, for example, Figure 8 and 9 in Fabregat et al). 6…”

Uncertainty Quantification Analysis of RANS of Spray Jets

“…These are then employed to calculate the mean values and the variance of the QoIs, respectively, and the QoIs confidence interval, defined as the interval of values containing the 90% of the QoI probability. The reference DNS data [13,14] are also used as a reference. We compare and discuss two quantities of interest , namely the mean axial velocity scaled by the bulk inflow velocity / 0 , and the mean vapor concentration scaled by the saturated vapor concentration / , .…”

“…The computations are based on the test case inquired via DNS by Dalla Barba et al [13,14]. The test case consists of a cylindrical domain filled with air in non-reactive conditions where a multi-phase acetone-air jet flows out of an orifice at the center of one base and acetone droplets are randomly dispersed through the inflow section.…”

“…The average profiles of the DNS of Dalla Barba et al [13] are imposed as inflow conditions of velocity and turbulent kinetic energy while the reference value of the inflow turbulent kinetic energy dissipation rate 0 is evaluated at each point of the inflow section as 0 = 3/4 3/2 / , where is the nominal = 0.07 * 2 and is the same coefficient used to evaluate the eddy viscosity in the two-equation closure model. A preliminary mesh sensitivity study, as well as a parametric campaign, varying the number of droplets per parcel, was performed.…”

“…We perform this uncertainty propagation in a prototypical flow configuration, which is a saturated acetone/air aerosol stream flowing in a dry air environment. The test case was already characterized via direct numerical simulation (DNS) [13,14] and is here replicated through a RANS approach coupled with a realizable k-epsilon model. Two parameters are considered as uncertain: the inflow turbulent kinetic energy dissipation rate 0 and a coefficient that blends two well-established dispersion models, namely the discrete random walk and the gradient-based dispersion models.…”