A numerical study of the droplet impact dynamics on a two-dimensional random rough surface

Abstract

Considerable efforts had been devoted to investigating numerically the droplet impact dynamics on a superhydrophobic surface. Whereas most of these numerical simulations were restricted to the two-dimension (2-D) axisymmetric coordinate system with the one-dimension (1-D) substrate surface. In this work, a three-dimension (3-D) computational fluid dynamics (CFD) model, which intergrew a 2-D random rough surface, was proposed to investigate the droplet impact dynamics, and the multi-phase flow issue was solved by the Navier-Stokes equations. It is remarkable that the 3-D CFD model revealed several significant dynamic details that were not easily captured in a 2-D axisymmetric coordinate system or practical experiments. For instance, the 3-D CFD model provided a unique perspective to understand the varying dynamic behaviors of impinged droplet in terms of the velocity streamline and dynamic viscosity analyses. Herein, the dynamic viscosity diagram revealed that the sprawl droplet on the 2-D random rough surface was classified as Cassie state, while as Wenzel state for smooth surface, which also explained the better bouncing behaviors of droplet from the random rough surface. Accordingly, we suggested a visual way to evaluate the solid-liquid contact area surrounded by the triple-phase contact line. The effects of finger protrusion and central cavity growth from the sprawl droplet on the vortex generation were further analyzed on the ground of the velocity amplitude distribution and streamline data. The present work can provide early guidance to inquire into the impact dynamics of droplets on the random rough surface.

Predicting Impact Outcomes and Maximum Spreading of Drop Impact on Heated Nanostructures Using Machine Learning

“…These intriguing dynamical drop behaviors stem from a complex interplay among droplet parameters, such as density, viscosity, surface tension, and diameter, − and surface properties, such as hydrophobicity, temperature, ,− and ambient gas pressure. , As such, predicting these drop behaviors and outcomes in a prior way presents a challenge but is crucial for optimizing various technological implementations. With the advent of high-speed imaging techniques, an experiment has been the dominating approach to investigate the ultrafast droplet dynamics upon impact, ,,− ,− including the recent extensions to heterogeneous droplets impacting onto heated surfaces. − …”

“…Over the past decades, the advancements of software development and increased accessibility of high computing power have facilitated computational fluid dynamics (CFD) investigations in the field. ,− , More recently, applications utilizing data-driven machine learning (ML) within the domain of fluid mechanics have emerged. − …”