Dynamic behavior of impinging drops on water repellent surfaces: Machine learning-assisted approach to predict maximum spreading

Abstract

The study of drop dynamic undergoing collision with solid surfaces seems quite necessary due to its practical applications ranging from coating industries to anti-icing and self-cleaning surfaces. Therefore, we experimentally studied the dynamic of impinging drop on water-repellent surfaces for a wide range of drop properties and initial velocities in terms of weber number (We). We considered the maximum spreading diameter to quantify the spreading dynamic. We modified one of the existing energy-balance models to analytically predict the observed maximum spreading diameters. We showed that above a critical We number (roughly 60–80), the maximum spreading diameter of superhydrophobic surfaces starts to deviate from those of hydrophobic surfaces. Therefore, we incorporated an adjusting factor into the energy-balance model to consider the transition from hydrophobicity to superhydrophobicity. Moreover, we developed a machine learning approach to predict the maximum spreading diameter as a function of drop properties and surface characteristics. Using the machine learning approach, it was found that beyond a critical contact angle (CAadv ∼ 150°–160°) the maximum spreading diameter does not depend on the contact angle anymore. Moreover, for low We numbers, the maximum spreading diameter decrease with increasing the contact angle, while for high We numbers they are directly proportional.