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Experiments in Fluids

, 55:1765 | Cite as

Inducing 3D vortical flow patterns with 2D asymmetric actuation of artificial cilia for high-performance active micromixing

  • Chia-Yuan ChenEmail author
  • Cheng-Yi Lin
  • Ya-Ting Hu
Research Article

Abstract

Driven by the advancement of the “lab-chip” concept, a new beating behavior of artificial cilia was identified to meet the demands on rapid and complete fluid mixing in miniaturized devices. This beating behavior is characterized by an in-plane asymmetric motion along a modified figure-of-eight trajectory. A typically symmetric figure-of-eight motion was also tested for comparison. Results showed that with this new beating behavior, the mixing efficiency for complete mixing is 1.34 times faster than that with the typical figure-of-eight motion. More importantly, the required beating area was only approximately two-thirds of that in the typical figure-of-eight motion, which is beneficial for more compact designs of various “lab-chip” applications. The unique planar asymmetric motion of the artificial cilia, which enhanced the magnitudes of the induced three-dimensional (3D) flow, was identified by micro-particle image velocimetry (µPIV) measurement and numerical modeling as a major contributor in enhancing microscale mixing efficiency. Quantitatively, 3D vortical flow structures induced by the artificial cilia were presented to elucidate the underlying interaction between the artificial cilia and the surrounding flow fields. With the presented quantification methods and mixing performance results, a new insight is provided by the hydrodynamic advantage of the presented micromixing concept on efficiently mixing highly viscous flow streams at microscale, to leverage the attributes of artificial cilia in the aspect of microscale flow manipulation.

Keywords

Vorticity Distribution Main Flow Direction Asymmetric Motion Artificial Cilium Active Micromixer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work would not be possible without the financial support from National Science Council of Taiwan under Contract No. NSC 102-2221-E-006-297-MY3 (to C.-Y. Chen).

Supplementary material

348_2014_1765_MOESM1_ESM.docx (1.1 mb)
Supplementary material 1 (DOCX 1,115 kb)

Supplementary material 2 (WMV 2,977 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringNational Cheng Kung UniversityTainanTaiwan

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