Isogeometric Hierarchical Model Reduction for advection-diffusion process simulation in microchannels

TL;DR

This paper demonstrates that Isogeometric Hierarchical Model Reduction effectively simplifies the simulation of advection-diffusion processes in microchannels, maintaining high accuracy while significantly reducing computational complexity.

Contribution

It introduces a HiMod reduction approach tailored for microchannel advection-diffusion modeling, showing its reliability and efficiency in complex geometries.

Findings

HiMod reduction achieves high accuracy compared to high-fidelity models.

Significant reduction in computational resources with minimal loss of precision.

Effective for complex microchannel geometries like S-shaped configurations.

Abstract

Microfluidics proved to be a key technology in various applications, allowing to reproduce large-scale laboratory settings at a more sustainable small-scale. The current effort is focused on enhancing the mixing process of different passive species at the micro-scale, where a laminar flow regime damps turbulence effects. Chaotic advection is often used to improve mixing effects also at very low Reynolds numbers. In particular, we focus on passive micromixers, where chaotic advection is mainly achieved by properly selecting the geometry of microchannels. In such a context, reduced order modeling can play a role, especially in the design of new geometries. In this chapter, we verify the reliability and the computational benefits lead by a Hierarchical Model (HiMod) reduction when modeling the transport of a passive scalar in an S-shaped microchannel. Such a geometric configuration provides an ideal setting where to apply a HiMod approximation, which exploits the presence of a leading dynamics to commute the original three-dimensional model into a system of one-dimensional coupled problems. It can be proved that HiMod reduction guarantees a very good accuracy when compared with a high-fidelity model, despite a drastic reduction in terms of number of unknowns.