Ribbing patterns in inertial rotary drag-out

TL;DR

This study investigates the formation of ribbing patterns in high-speed rotary liquid entrainment, proposing a primary instability mechanism driven by pressure gradients, supported by experiments and numerical simulations that validate the model.

Contribution

It introduces a mechanistic model linking rib spacing to capillary length and pressure-driven instability, supported by experiments and simulations.

Findings

Rib spacing varies slightly with speed and viscosity.

The proposed model accurately predicts rib spacing.

Numerical simulations reproduce experimental ribbing patterns.

Abstract

We report pattern formation in an otherwise non-uniform and unsteady flow arising in high-speed liquid entrainment conditions on the outer wall of a wide rotating drum. We show that the coating flow in this rotary drag-out undergoes axial modulations to form an array of roughly vertical thin liquid sheets which slowly drift from the middle of the drum towards its side walls. Thus, the number of sheets fluctuate in time such that the most probable rib spacing varies ever so slightly with the speed, and a little less weakly with the viscosity. We propose that these axial patterns are generated due to a primary instability driven by an adverse pressure gradient in the meniscus region of the rotary drag-out flow, similar to the directional Saffman-Taylor instability, as is well-known for ribbing in film-splitting flows. Rib spacing based on this mechanistic model turns out to be proportional to the capillary length, wherein the scaling factor can be determined based on existing models for film entrainment at both low and large Capillary numbers. In addition, we performed Direct Numerical Simulations, which reproduce the experimental phenomenology and the associated wavelength. We further include two numerical cases wherein either the liquid density, or the liquid surface tension are quadrupled while keeping all other parameters identical with experiments. The rib spacings of these cases are in agreement with the predictions of our model.