Computational methods and software for the design of inertial microfluidic flow sculpting devices

The ability to sculpt inertially flowing fluid via bluff body obstacles has enormous promise for applications in bioengineering, chemistry, and manufacturing within microfluidic devices. However, the computational difficulty inherent to full scale 3-dimensional fluid flow simulations makes designing and optimizing such systems tedious, costly, and generally tasked to computational experts with access to high performance resources. The goal of this work is to construct efficient models for the design of inertial microfluidic flow sculpting devices, and implement these models in freely available, user-friendly software for the broader microfluidics community. Two software packages were developed to accomplish this: uFlow and FlowSculpt . uFlow solves the forward problem in flow sculpting, that of predicting the net deformation from an arbitrary sequence of obstacles (pillars), and includes estimations of transverse mass diffusion and particles formed by optical lithography. FlowSculpt solves the more difficult inverse problem in flow sculpting, which is to design a flow sculpting device which produces a target flow shape. Each piece of software uses efficient, experimentally validated forward models developed within this work, which are applied to deep learning techniques to explore other routes to solving the inverse problem. The models are also highly modular, capable of incorporating new microfluidic components and flow physics to the design process. It is anticipated that the microfluidics community will integrate the tools developed here into their own research, and bring new designs, components, and applications to the inertial flow sculpting platform

Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learning

Microfluidic devices are utilized to control and direct flow behavior in a wide variety of applications, particularly in medical diagnostics. A particularly popular form of microfluidics -- called inertial microfluidic flow sculpting -- involves placing a sequence of pillars to controllably deform an initial flow field into a desired one. Inertial flow sculpting can be formally defined as an inverse problem, where one identifies a sequence of pillars (chosen, with replacement, from a finite set of pillars, each of which produce a specific transformation) whose composite transformation results in a user-defined desired transformation. Endemic to most such problems in engineering, inverse problems are usually quite computationally intractable, with most traditional approaches based on search and optimization strategies. In this paper, we pose this inverse problem as a Reinforcement Learning (RL) problem. We train a DoubleDQN agent to learn from this environment. The results suggest that learning is possible using a DoubleDQN model with the success frequency reaching 90% in 200,000 episodes and the rewards converging. While most of the results are obtained by fixing a particular target flow shape to simplify the learning problem, we later demonstrate how to transfer the learning of an agent based on one target shape to another, i.e. from one design to another and thus be useful for a generic design of a flow shape.Comment: Neurips 2018 Deep RL worksho

Optimization of micropillar sequences for fluid flow sculpting

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

Optimized design of obstacle sequences for microfluidic mixing in an inertial regime

Mixing is a basic but challenging step to achieve in high throughput microfluidic applications such as organic synthesis or production of particles. A common approach to improve micromixer performance is to devise a single component that enhances mixing through optimal convection, and then sequence multiple such units back-to-back to enhance overall mixing at the end of the sequence. However, the mixing units are often optimized only for the initial non-mixed fluid composition, which is no longer the input condition for each subsequent unit. Thus, there is no guarantee that simply repeating a single mixing unit will achieve optimally mixed fluid flow at the end of the sequence. In this work, we analyzed sequences of 20 cylindrical obstacles, or pillars, to optimize the mixing in the inertial regime (where mixing is more difficult due to higher Péclet number) by managing their interdependent convection operations on the composition of the fluid. Exploiting a software for microfluidic design optimization called FlowSculpt, we predicted and optimized the interfacial stretching of two co-flowing fluids, neglecting diffusive effects. We were able to quickly design three different optimal pillar sequences through a space of 32^(20) possible combinations of pillars. As proof of concept, we tested the new passive mixer designs using confocal microscopy and full 3D CFD simulations for high Péclet numbers (Pe ≈O(10^(5-6)), observing fluid flow shape and mixing index at several cross-sections, reaching mixing efficiencies around 80%. Furthermore, we investigated the effect of the inter-pillar spacing on the most optimal design, quantifying the tradeoff between mixing performance and hydraulic resistance. These micromixer designs and the framework for the design in inertial regimes can be used for various applications, such as lipid nanoparticle fabrication which has been of great importance in vaccine scale up during the pandemic

Shape-design for stabilizing micro-particles in inertial microfluidic flows

Design of microparticles which stabilize at the centerline of a channel flow when part of a dilute suspension is examined numerically for moderate Reynolds numbers ($10 \le Re \le 80$). Stability metrics for particles with arbitrary shapes are formulated based on linear-stability theory. Particle shape is parametrized by a compact, Non-Uniform Rational B-Spline (NURBS)-based representation. Shape-design is posed as an optimization problem and solved using adaptive Bayesian optimization. We focus on designing particles for maximal stability at the channel-centerline robust to perturbations. Our results indicate that centerline-focusing particles are families of characteristic "fish"/"bottle"/"dumbbell"-like shapes, exhibiting fore-aft asymmetry. A parametric exploration is then performed to identify stable particle-designs at different k's (particle chord-to-channel width ratio) and Re's ($0.1 \le k \le 0.4, 10 \le Re \le 80$). Particles at high-k's and Re's are highly stabilized when compared to those at low-k's and Re's. A comparison of the modified dumbbell designs from the current framework also shows better performance to perturbations in Fluid-Structure Interaction (FSI) when compared to the rod-disk model reported previously (Uspal & Doyle 2014) for low-Re Hele-Shaw flow. We identify basins of attraction around the centerline, which span larger release-angle-ranges and lateral locations (tending to the channel width) for narrower channels, which effectively standardizes the notion of global focusing in such configurations using the current stability-paradigm. The present framework is illustrated for 2D cases and is potentially generalizable to stability in 3D flow-fields. The current formulation is also agnostic to Re and particle/channel geometry which indicates substantial potential for integration with imaging flow-cytometry tools and microfluidic biosensing-assays.Comment: 27 pages, 18 figures, modified the LaTeX document template, corrected typo

Hierarchical Feature Extraction for Efficient Design of Microfluidic Flow Patterns

Deep neural networks are being widely used for feature representation learning in diverse problem areas ranging from object recognition and speech recognition to robotic perception and human disease prediction. We demonstrate a novel, perhaps the first application of deep learning in mechanical design, specifically to learn complex microfluidic flow patterns in order to solve inverse problems in fluid mechanics. A recent discovery showed the ability to control the fluid deformations in a microfluidic channel by placing a sequence of pillars. This provides a fundamental tool for numerous material science, manufacturing and biological applications. However, designing pillar sequences for user-defined deformations is practically infeasible as the current process requires laborious and time-consuming design iterations in a very large, highly nonlinear design space that can have as large as 1015 possibilities. We demonstrate that hierarchical feature extraction can potentially lead to a scalable design tool via learning semantic representations from a relatively small number of flow pattern examples. The paper compares the performances of pre-trained deep neural networks and deep convolutional neural networks as well as their learnt features. We show that a balanced training data generation process with respect to a metric on the output space improves the feature extraction performance. Overall, the deep learning based design process is shown to expedite the current state-of-the-art design approaches by more than 600 times

Micropillar sequence designs for fundamental inertial flow transformations

The ability to control the shape of a flow in a passive microfluidic device enables potential applications in chemical reaction control, particle separation, and complex material fabrication. Recent work has demonstrated the concept of sculpting fluid streams in a microchannel using a set of pillars or other structures that individually deform a flow in a predictable pre-computed manner. These individual pillars are then placed in a defined sequence within the channel to yield the composition of the individual flow deformations – and ultimately complex user-defined flow shapes. In this way, an elegant mathematical operation can yield the final flow shape for a sequence without an experiment or additional numerical simulation. Although these approaches allow for programming complex flow shapes without understanding the detailed fluid mechanics, the design of an arbitrary flow shape of interest remains difficult, requiring significant design iteration. The development of intuitive basic operations (i.e. higher-level functions that consist of combinations of obstacles) that act on the flow field to create a basis for more complex transformations would be useful in systematically achieving a desired flow shape. Here, we show eight transformations that could serve as a partial basis for more complex transformations. We initially used in-house, freely available custom software (uFlow), which allowed us to arrive at these transformations that include making a fluid stream concave and convex, tilting, stretching, splitting, adding a vertex, shifting, and encapsulating another flow stream. The pillar sequences corresponding to these transformations were subsequently fabricated and optically analyzed using confocal imaging – yielding close agreement with uFlow-predicted shapes. We performed topological analysis on each transformation, characterizing potential sequences leading to these outputs and trends associated with changing diameter and placement of the pillars. We classify operations into four sets of sequence-building concatenations: stacking, recursion, mirroring, and shaping. The developed basis should help in the design of microfluidic systems that have a phenomenal variety of applications, such as optofluidic lensing, enhanced heat transfer, or new polymer fiber design