Optimization of micropillar sequences for fluid flow sculpting

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 for a fixed channel length per pillar. 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. C 2016 AIP Publishing LLC. [http://dx

Deep Learning for Flow Sculpting: Insights into Efficient Learning using Scientific Simulation Data

“…Each type of pillar is defined by a specific size and location: four diameters ( D / w  = {0.375, 0.5, 0.625, 0.75} for pillar diameter D and channel width w ), and eight different locations in the channel (, where y / w  = 0.0 is the center of the channel). This library is created by the same experimentally validated simulation technique used by Stoecklein et al 1617. and Lore et al 18,.…”

“…The forward model of simulating a fluid flow shape given a sequence of micropillars follows the experimentally validated method used by Stoecklein et al 1617,. which we briefly outline here.…”

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“…This technique utilizes individual deformations imposed on fluid flowing past a single obstacle (pillar) in confined flow to create an overall net deformation from a rationally designed sequence of such pillars. If the pillars are spaced far enough apart (space > 6D, for a pillar diameter D), the individual deformations become independent, and can thus be pre-computed as building blocks for a highly efficient forward model for the prediction of sculpted flow given an input pillar sequence [22] (Fig. 2).…”

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