Simulation of Droplet Trains in Microfluidic Networks

TL;DR

This paper models droplet train dynamics in microfluidic networks, revealing that hysteresis effects can cause irreversibility and complex output patterns like chaos, depending on input timing and network geometry.

Contribution

It demonstrates how microfluidic networks can exhibit irreversibility due to hysteresis, with simulations matching experimental setups and exploring pattern formation.

Findings

Output patterns vary with input timing and network geometry.

System can exhibit periodic or chaotic behavior.

Irreversibility arises from hysteresis effects in the network.

Abstract

In this work we show that in a microfluidic network and in low Reynolds numbers a system can be irreversible because of hysteresis effects.The network, which is employed in our simulations, is taken from recent experiments. The network consists of one loop connected to input and output pipes. A train of droplets enter the system at a uniform rate, but they may leave it in different patterns, e.g. periodic or even chaotic. The out put pattern depends on the time interval among the incoming droplets as well as the network geometry and for some parameters the system is not reversible.