Optimization of micropillar sequences for fluid flow sculpting

TL;DR

This paper presents an automated optimization framework using matrix-based modeling and genetic algorithms to design micropillar sequences for complex fluid flow sculpting in microchannels, validated through experiments.

Contribution

It introduces a fast, matrix-based forward model combined with genetic algorithms for automated design of micropillar sequences, enabling discovery of complex flow transformations.

Findings

Automated designs outperform manual sequences in complexity and length.

The framework enables rapid exploration of large design spaces.

Experimental validation confirms the accuracy of computational predictions.

Abstract

Inertial fluid flow deformation around pillars in a microchannel is a new method for controlling fluid flow. Sequences of pillars have been shown to produce a rich phase space with a wide variety of flow transformations. Previous work has successfully demonstrated manual design of pillar sequences to achieve desired transformations of the flow cross-section, with experimental validation. However, such a method is not ideal for seeking out complex sculpted shapes as the search space quickly becomes too large for efficient manual discovery. We explore fast, automated optimization methods to solve this problem. We formulate the inertial flow physics in microchannels with different micropillar configurations as a set of state transition matrix operations. These state transition matrices are constructed from experimentally validated streamtraces. This facilitates modeling the effect of a sequence of micropillars as nested matrix-matrix products, which have very efficient numerical implementations. With this new forward model, arbitrary micropillar sequences can be rapidly simulated with various inlet configurations, allowing optimization routines quick access to a large search space. We integrate this framework with the genetic algorithm and showcase its applicability by designing micropillar sequences for various useful transformations. We computationally discover micropillar sequences for complex transformations that are substantially shorter than manually designed sequences. We also determine sequences for novel transformations that were difficult to manually design. Finally, we experimentally validate these computational designs by fabricating devices and comparing predictions with the results from confocal microscopy.