Slip length estimation for flow over lubricant-impregnated surface

TL;DR

This paper develops an improved analytical model for estimating slip length over ribbed lubricant-impregnated surfaces, enhancing accuracy and flexibility in predicting flow behavior for drag reduction and flow steering.

Contribution

It introduces a novel eigenfunction expansion approach that surpasses existing models in accuracy and generality, avoiding restrictive assumptions on geometry and fluid properties.

Findings

Enhanced numerical accuracy over previous models

Flexible predictions for drag reduction and flow steering

No restrictive assumptions on rib or fluid properties

Abstract

Lubricant-impregnated surfaces (LIS) and superhydrophobic surfaces (SHSs) are known to passively reduce drag over a surface, which, with a suitable design such as the ribbed texture, can also steer flows anisotropically. Analytical predictions are developed for ribbed textures using an eigenfunction expansion approach. Compared to currently available analytical predictions, these predictions demonstrate superior numerical accuracy and avoid restrictive assumptions on rib geometry, working fluid and lubricant properties. The predictions provide contrasting design prescriptions depending on whether a lower drag or a larger degree of anisotropic flow deflection is desired.