Thin gap approximations for microfluidic device design

TL;DR

This paper derives and validates a systematic thin gap approximation for microfluidic device flows, extending classical Hele-Shaw theory to include higher-order effects for improved modeling accuracy.

Contribution

It introduces a new, shorter derivation of the Hele-Shaw approximation for microfluidics and extends it with higher-order corrections to capture complex flow profiles.

Findings

The leading-order model matches previous 2D approximations.

Higher-order corrections capture non-parabolic velocity profiles.

Numerical evidence confirms the model's effectiveness for real microfluidic geometries.

Abstract

Over 125 years ago, Henry Selby Hele-Shaw realized that the depth-averaged flow in thin gap geometries can be closely approximated by two-dimensional (2D) potential flow, in a surprising marriage between the theories of viscous-dominated and inviscid flows. Hele-Shaw approximation allows visualization of potential flows over 2D airfoils and also undergirds important discoveries in the dynamics of interfacial instabilities and convection, yet it has found little use in modeling flows in microfluidic devices, although these devices often have thin gap geometries. Here, we derive a Hele-Shaw approximation for the flow in the kinds of thin gap geometries created within microfluidic devices. Using the Method of Weighted Residuals (MWR), we reinterpret the Hele-Shaw approximation as the leading term of an orthogonal polynomial expansion that can be systematically extended to higher-order corrections. The resulting leading-order equation coincides with the previously derived 2D approximations, but our derivation is shorter and more direct. By extending the expansion beyond leading order, we obtain a new reduced model that captures non-parabolic gap-wise velocity profiles and out-of-plane flow effects. We provide substantial numerical evidence showing that approximate equations can successfully model real microfluidic and inertial-microfluidic device geometries. By reducing three-dimensional (3D) flows to 2D models, our validated model will allow for accelerated device modeling and design.