Experiments and computations of microfluidic liquid–liquid flow patterns

Desir, Pierre; Chen, Tai-Ying; Bracconi, Mauro; Saha, Basudeb; Maestri, Matteo; Vlachos, Dionisios G.

doi:10.1039/C9RE00332K

Title: Experiments and computations of microfluidic liquid–liquid flow patterns

Journal Article · Tue Oct 01 00:00:00 EDT 2019 · Reaction Chemistry & Engineering

DOI:https://doi.org/10.1039/C9RE00332K· OSTI ID:1642397

Desir, Pierre ^[1]; Chen, Tai-Ying ^[1];

^[2];

^[3];

^[2];

^[4]

Univ. of Delaware, Newark, DE (United States)
Laboratory of Catalysis and Catalytic Processes, Milano (Italy)
Catalysis Center for Energy Innovation, Newark DE (United States)
Univ. of Delaware, Newark, DE (United States); Catalysis Center for Energy Innovation, Newark DE (United States)

We study two-phase liquid–liquid flow patterns in a 500 μm capillary microchannel for four biphasic systems: ethyl acetate/water, 2-pentanol/water, methyl isobutyl ketone/water, and heptane/water. Flow visualization experiments using laser induced fluorescence (LIF) reveal a total of 7 different flow patterns for all solvent pairs, namely slug flow, droplet flow, slug-droplet flow, parallel, annular, dispersed, and irregular flow. A map of different flow patterns was built to delineate the origin of their formation. We find conventional dimensionless groups are insufficient to uniquely identify the flow patterns. Computational fluid dynamics (CFD) modeling in OpenFOAM shows agreement with the experimental flow patterns for most of the two-phase flows. Principal component analysis reduces the dimensionality of potential descriptors of flow patterns and, unlike prior work using two dimensionless numbers, determines six important features that describe >95% of the variance of the experimental flow patterns. These include the total flow rate, the flow rate ratio between the two phases, the capillary and Ohnesorge numbers of the aqueous phase, and the Weber number and velocity of the organic phase. Lastly, we build a decision-tree model to further regress the data and identify the critical features and demonstrate an accuracy in predicting the flow patterns of up to 93%.

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Research Organization:: RAPID Manufacturing Institute, New York, NY (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Manufacturing Office

Grant/Contract Number:: EE0007888

OSTI ID:: 1642397

Alternate ID(s):: OSTI ID: 1569773

Journal Information:: Reaction Chemistry & Engineering, Vol. 5, Issue 1; ISSN 2058-9883

Publisher:: Royal Society of ChemistryCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 27 works

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