Spatio-temporal analysis of sprays by using Phase Doppler Anemometry data

Abstract

Spray characterization often relies on empirical formulas, statistical distributions, and derived quantities. Deterministic spray behavior originates from physics-governed mechanisms of atomization, \emph{e.g.}, nozzle geometry, boundary conditions, and hydrodynamic instabilities. Due to the stochastic nature of the atomization process, which originates from turbulence, chaotic perturbations, and droplet--droplet interactions, the temporal characteristics of dynamic behavior are seldom investigated. The combination of these processes leads to droplet clustering, which is a spatio-temporal behavior that is the focus of the current paper for an airblast atomizer. The measurement data by Phase Doppler Anemometry include droplet size, velocity, and arrival time. Firstly, the theoretical and experimental interparticle time distributions are compared using a $\chi^2$ hypothesis test, which concluded multimodality. Secondly, \emph{k}-means clustering is applied to determine droplet clusters, whose number was determined by gap statistics. The above analysis was performed using an extensive database of various measurement positions, atomizing pressures, liquid preheating temperatures, and liquid types. It was found that cluster formation affects approximately 30\% of the droplets in a single data set. In conclusion, the unsteadiness in the central region is caused by clustering, while it is caused by mixing and droplet entrainment in the spray periphery. The centroids and the number of cluster values depend on the atomizing pressure and the spray position, and are independent of the liquid temperature. The dynamical behavior of the clusters is compared by their droplet size and velocity distributions, showing no significant difference, suggesting that unsteady spray modeling is necessary if temporal characteristics are critical.