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Technical Briefs

Fluid Streaming in Micro/Minibifurcating Networks

[+] Author and Article Information
Z. Zhang1

Department of Mechanical Engineering and Applied Mechanics, University of Rhode Island, Kingston, RI 02881zhang@egr.uri.edu

A. Fadl, C. Liu, D. M. L. Meyer

Department of Mechanical Engineering and Applied Mechanics, University of Rhode Island, Kingston, RI 02881

M. Krafczyk

Department of Architecture, Civil Engineering and Environmental Sciences, Institute for Computational Modeling in Civil Engineering, TU Braunschweig, 38023 Braunschweig, Germany

1

Corresponding author.

J. Fluids Eng 131(8), 084501 (Jul 24, 2009) (8 pages) doi:10.1115/1.3176973 History: Received September 19, 2008; Revised May 19, 2009; Published July 24, 2009

In this study, we investigate the phenomena of flow streaming in micro-/minichannel networks of symmetrical bifurcations using computer simulations with analytical validation. The phenomena of the flow streaming can be found in zero-mean velocity oscillating flows in a wide range of channel geometries. Although there is no net mass flow (zero-mean velocity) passing through the channels, the discrepancy in velocity profiles between the forward flow and backward flow causes fluid particles near the walls to drift toward one end while particles near the centerline to drift toward the opposite end. The unique characteristics of flow streaming could be used for various applications. The advantages include enhanced mixing, pumpless fluid propulsion, multichannel fluid distribution, easy system integration, and cost-effective operation. The results of computer simulations showed that oscillation amplitude is the dominant effect on streaming velocity in channel networks. Streaming velocity was directly proportional to the oscillation frequency and can be used as a cost-effective and reliable convective transport means when the particle diffusivity is less than the fluid kinematic viscosity. A considerable amount of work is needed to further study and understand the flow streaming phenomenon.

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Copyright © 2009 by American Society of Mechanical Engineers
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Figures

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Figure 1

Mechanisms of flow streaming in a bifurcation channel

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Figure 8

Effects of mass diffusivity on concentration profiles under flow streaming. Panel A: Sc=∞, t=6.15 s; panel B: Sc=700, t=6.15 s; panel C: Sc=0.7, t=4.95 s; and panel D: Sc=0.07, t=1.45 s.

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Figure 11

Axial velocity profiles at various cross sections of the bifurcation networks for t=3.025 s (1/4 of an oscillation cycle) and t=3.075 s (3/4 of an oscillation cycle). Oscillation amplitude A=0.6 mm and frequency f=10 Hz were simulated.

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Figure 2

Bifurcation channel networks used in the computer simulation

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Figure 3

Center velocity at x=1.2 mm as function of time. Time steps of 8, 16, 32, and 64 per oscillation cycle were used.

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Figure 4

Effects of Womersley number and oscillation amplitude on streaming velocity. Panel A: channel generation 1, panel B: channel generation 2, and panel C: channel generation 3.

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Figure 5

Effects of oscillation amplitude on flow streaming. Panel A: channel generation 1, panel B: channel generation 2, and panel C: channel generation 3.

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Figure 6

Effects of mean oscillation fluid velocity on streaming flow distribution patterns. Mean oscillation velocity u=0.004 and 0.016 m/s in panels A and B, respectively.

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Figure 7

Effects of mass diffusivity on convective mass transport process under flow streaming

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Figure 9

Comparison of streaming patterns under different entrance boundary conditions: Panel A is parabolic velocity boundary condition and panel B is pressure boundary condition; t=1 s, f=10 Hz, and A=0.414 mm

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Figure 10

Streak lines of air bubbles suspended in oscillation flows: (a) t=0 s (cycle 0); (b) t=0.2 s (cycle 2); (c) t=1 s (cycle 10); and (d) t=5 s (cycle 50). Oscillation amplitude A=0.6 mm and frequency f=10 Hz were simulated.

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