Mapping low-Reynolds-number microcavity flows using microfluidic screening devices

Rami Fishler, Molly K. Mulligan, Josué Sznitman

Research output: Contribution to journalArticlepeer-review

Abstract

Low-Reynolds-number flows in cavities, characterized by separating and recirculating flows are increasingly used in microfluidic applications such as mixing and sorting of fluids, cells, or particles. However, there is still a lack of guidelines available for selecting the appropriate or optimized microcavity configuration according to the specific task at hand. In an effort to provide accurate design guidelines, we investigate quantitatively low-Reynolds-number cavity flow phenomena using a microfluidic screening platform featuring rectangular channels lined with cylindrical cavities. Using particle image velocimetry (PIV), supported by computational fluid dynamics (CFD) simulations, we map the entire spectrum of flows that exist in microcavities over a wide range of low-Reynolds numbers (Re = 0.1, 1, and 10) and dimensionless geometric parameters. Comprehensive phase diagrams of the corresponding microcavity flow regimes are summarized, capturing the gradual transition from attached flow to a single vortex and crossing through two- and three-vortex recirculating systems featuring saddle-points. Finally, we provide design insights into maximizing the rotational frequencies of recirculating single-vortex microcavity systems. Overall, our results provide a complete and quantitative framework for selecting cavities in microfluidic-based microcentrifuges and vortex mixers.

Original languageEnglish
Pages (from-to)491-500
Number of pages10
JournalMicrofluidics and Nanofluidics
Volume15
Issue number4
DOIs
StatePublished - Oct 2013

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Materials Chemistry

Fingerprint

Dive into the research topics of 'Mapping low-Reynolds-number microcavity flows using microfluidic screening devices'. Together they form a unique fingerprint.

Cite this