Influence of governing dimensionless parameters on droplet impact dynamics

Droplet impacts on solid surfaces are a key element of a wide variety of phenomena encountered in technical applications, such as ink-jet printing, spray painting and coating, plasma spraying, and crop spraying, etc. The impact may result in the droplet spreading over the solid surface, receding, or rebounding which depends on numerous factors such as impact velocity, droplet size, etc. In this study, we numerically analyzed the impingement of a single droplet on a stationary surface in the regimes of deposition or rebound. We considered the effects of droplet to surface distance, Reynold number, and Weber number on the droplet dynamics, impingement outcome, spreading ratio, wetted length, maximum rebound height etc. We used Volume of Fluid approach available within commercial CFD package Ansys Fluent to conduct our numerical simulations and compared our numerical results with the literature data and found good agreement between them. Finally, using the simulation data, we developed machine learning based predictive model algorithm which can predict the impingement outcome (deposition or rebound) as well as the maximum spreading ratio based on the governing dimensionless parameters.