Numerical Study of Droplet Generation Process in a Microfluidic Flow Focusing

Document Type : Research Paper


1 Department of Life Science Engineering, University of Tehran, Tehran, Iran

2 Mechanical Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran


Microfluidic flow focusing devices have been utilized for droplet generation on account of its superior control over droplet size. Droplet based microfluidics addressed many scientific issues by providing a novel technological platform for applications such as biology, pharmaceutical industry, biomedical studies and drug delivery. This study numerically investigated the droplet generation process of an aqueous flow in oleic acid oil in a microfluidic flow focusing device. A conservative level set method is conducted to numerically model the droplet generation process. The post processing of the simulation results are done using Canny edge detection image processing method, which is a novel approach. Moreover, the results of the numerical simulation were compared to the experimental data provided by Ten et al. on the same device. This method showed a maximum average deviation from the experimental results of 14.6% and a minimum of 6.96%. Also, by means of altering water and oil flows, the influence of parameters affecting droplet size, which lead to a better understanding of droplet generation phenomenon, was investigated in this study. Therefore, it can be concluded that the flow ratio and capillary number are the two primary parameters that affect droplet size, while capillary number showed more dominance in comparison to flow ratio.


Main Subjects

[1].Zhang, C., et al. (2006). PCR microfluidic devices for DNA amplification. Biotechnology advances, 24(3): p. 243-284.
[2].Zhang, Y., Ozdemir, P. (2009). Microfluidic DNA amplification—a review. Analytica chimica acta, 638(2): p. 115-125.
[3].Chang, Y.H., et al. (2006). Integrated polymerase chain reaction chips utilizing digital microfluidics. Biomedical microdevices, 8(3): p. 215-225.
[4].Granado, K.V.R., et al. (2013). Numerical simulation of droplet formation in a microchannel device. The International Journal of Multiphysics, 7(4): p. 271-286.
[5].Hatch, A.C., et al. (2011). 1-Million droplet array with wide-field fluorescence imaging for digital PCR. Lab Chip, 11(22): p. 3838-3845.
[6].Nie, Z., et al. (2008). Emulsification in a microfluidic flow-focusing device: effect of the viscosities of the liquids. Microfluidics and Nanofluidics, 5(5): p. 585-594.
[7].Lee, W., L.M. Walker, Anna, S.L. (2009). Role of geometry and fluid properties in droplet and thread formation processes in planar flow focusing. Physics of Fluids (1994-present), 21(3): p. 032103.
[8].Christopher, G., Anna, S. (2007). Microfluidic methods for generating continuous droplet streams. Journal of Physics D: Applied Physics, 40(19): p. R319.
[9].Griffiths, A.D., Tawfik, D.S. (2006). Miniaturising the laboratory in emulsion droplets. Trends in biotechnology, 24(9): p. 395-402.
[10]. Anna, S.L., Bontoux, N., Stone, H.A. (2003). Formation of dispersions using “flow focusing” in microchannels. Applied physics letters, 82(3): p. 364-366.
[11]. Garstecki, P., Stone, H.A. Whitesides, G.M. (2005). Mechanism for flow-rate controlled breakup in confined geometries: A route to monodisperse emulsions. Physical Review Letters, 94(16): p. 164501.
[12]. Li, Y., Jain, M., Nandakumar, K. (2012). Numerical study of droplet formation inside a microfluidic flow-focusing device. in COMSOL Conference Proceeding.
[13]. Conneely, M., et al. Computationally Assisted Design and Experimental Validation of a Novel'Flow-Focussed'Microfluidics Chip for Generating Monodisperse Microbubbles.
[14]. Tan, Y.C., Cristini, V. Lee, A.P. (2006). Monodispersed microfluidic droplet generation by shear focusing microfluidic device. Sensors and Actuators B: Chemical, 114(1): p. 350-356.
[15]. Liu, J., Nguyen, N.T. (2010). Numerical simulation of droplet-based microfluidics.
[16]. Olsson, E., Kreiss, G. (2005). A conservative level set method for two phase flow. Journal of computational physics, 210(1): p. 225-246.
[17]. Basu, M. (2002). Gaussian-based edge-detection methods-a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 32(3): p. 252-260.
[18]. Canny, J. (1986). A computational approach to edge detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, (6): p. 679-698.
  • Receive Date: 16 April 2015
  • Revise Date: 18 June 2015
  • Accept Date: 18 June 2015