Inverse design of microchannel fluid flow networks using Turing pattern dehomogenization
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BRIEF NOTE
Inverse design of microchannel fluid flow networks using Turing pattern dehomogenization Ercan M. Dede1 · Yuqing Zhou1 · Tsuyoshi Nomura1,2 Received: 12 November 2019 / Revised: 3 March 2020 / Accepted: 13 March 2020 © The Author(s) 2020
Abstract Microchannel reactors are critical in biological plus energy-related applications and require meticulous design of hundredsto-thousands of fluid flow channels. Such systems commonly comprise intricate space-filling microstructures to control the fluid flow distribution for the reaction process. Traditional flow channel design schemes are intuition-based or utilize analytical rule-based optimization strategies that are oversimplified for large-scale domains of arbitrary geometry. Here, a gradient-based optimization method is proposed, where effective porous media and fluid velocity vector design information is exploited and linked to explicit microchannel parameterizations. Reaction-diffusion equations are then utilized to generate space-filling Turing pattern microchannel flow structures from the porous media field. With this computationally efficient and broadly applicable technique, precise control of fluid flow distribution is demonstrated across large numbers (on the order of hundreds) of microchannels. Keywords Laminar flow · Microchannels · Reaction-diffusion · Patterning
1 Introduction Microchannel flow structures are found in a range of important industrial applications involving water purification (Wang et al. 2014), pharmaceuticals (Gutmann et al. 2015), electronics (Chen and Cheng 2002), and green chemistry (Lerou et al. 2010). Such systems require careful handling of fluid species to control reaction processes. Particularly, the flow distribution of fluids is critical, and intricate space-filling channel structures are employed for flow control through single- or multi-layered (e.g., 100–10,000) microchannels.
Responsible Editor: Anton Evgrafov Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00158-020-02580-w) contains supplementary material, which is available to authorized users. Ercan M. Dede
[email protected] 1
Toyota Research Institute of North America, 1555 Woodridge Ave., Ann Arbor, MI 48105, USA
2
Toyota Central R&D Labs, Inc., 41-1 Yokomichi, Nagakute 480-1192, Japan
Design optimization schemes for uniform fluid delivery to arrays of microchannels include approximation techniques, heuristic strategies, bifurcation methods, and gradient-based algorithms; see Rebrov et al. (2011). One approximation technique (Commenge et al. 2002) treats flow friction via a resistive network of ducts to understand pressure drop across a flow distribution chamber (or manifold). Using analytical expressions, the influence of the manifold geometry is understood, and flow uniformity is optimized for simplified (e.g., trapezoidal) geometries. A heuristic technique (Luo et al. 2015) involves perforated baffles upstream of microchannels to homogenize fluid distribution, although added pressure drop is a conce
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