Solving the DLD boundary problem using iterative CFD

Shilun Feng*, Alison M. Skelley, David Inglis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference abstract

Abstract

Deterministic Lateral Displacement (DLD) is a size-based sorting method relying on precise flow patterns. Those flow patterns are disrupted near boundaries, resulting in poor separation performance. In this work, we use a gradient descent method similar to that used in machine learning to evolve the physical geometry for a target flow pattern in 3D. The method is computationally expensive and difficult to implement for large arrays with low row shift fractions. To develop a design rule that can be used for any DLD device, we solve the boundary problem with row shift fractions (ϵ) of 1/6, 1/8, and 1/10, and various depths. All devices have small Re, circular posts, and a gap that is equal to the post diameter. We verify the design rule by using it to draw and simulate a larger array with a row shift fraction of (ϵ) of 1/20. The resulting array has a much more uniform critical particle size than all prior work.

Original languageEnglish
Title of host publicationThe 22nd International Conference on Miniaturized Systems for Chemistry and Life Sciences
PublisherChemical and Biological Microsystems Society
Pages2063-2066
Number of pages4
ISBN (Electronic)9780578405308
Publication statusPublished - 2018
EventInternational Conference on Miniaturized Systems for Chemistry and Life Sciences (22nd : 2018) - Kaohsiung, Taiwan, Province of China
Duration: 11 Nov 201815 Nov 2018
Conference number: 22nd

Conference

ConferenceInternational Conference on Miniaturized Systems for Chemistry and Life Sciences (22nd : 2018)
Abbreviated titleMicroTAS 2018
CountryTaiwan, Province of China
CityKaohsiung
Period11/11/1815/11/18

Keywords

  • DLD
  • Microfluidics
  • Boundary
  • CFD

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