Research Papers: Multiphase Flows

Optimum Parameter Design of Microbubble Drag Reduction in a Turbulent Flow by the Taguchi Method Combined With Artificial Neural Networks

[+] Author and Article Information
Kwan Ouyang

Associate Professor
Department of Marine Engineering,
Taipei College of Maritime Technology,
No. 212, Sec. 9, Yanping N. Road,
Taipei 111, Taiwan
e-mail: f0898@mail.tcmt.edu.tw

Sheng-Ju Wu

e-mail: wusj@ndu.edu.tw

Huang-Hsin Huang

e-mail: double_plp@hotmail.com
Department of Power Vehicle and Systems Engineering,
Chung Cheng Institute of Technology,
National Defense University,
No. 75, Shiyuan Road, Daxi Township,
Taoyuan County 33551, Taiwan

1Corresponding author.

Contributed by the Fluids Engineering Division of ASME for publication in the Journal of Fluids Engineering. Manuscript received August 16, 2012; final manuscript received June 27, 2013; published online August 7, 2013. Assoc. Editor: Mark R. Duignan.

J. Fluids Eng 135(11), 111301 (Aug 07, 2013) (11 pages) Paper No: FE-12-1396; doi: 10.1115/1.4024930 History: Received August 16, 2012; Revised June 27, 2013

This study attempts to optimize parameters for the microbubble drag reduction in a turbulent flow based on experimental measurements. Five parameters were investigated: three are control factors (the area of air injection, bubble size, and the rate of air injection) and two are indicative factors (flow speed and the measured position of local shear stress). An integrated approach of combining the Taguchi method with artificial neural networks (ANN) is proposed, implementing the optimum parameter design in this study. Based on the experimental results, analysis of variance concluded that, among the control factors, the rate of air injection has the greatest influence on microbubble drag reduction, while bubble size has the least. The investigation of drag reduction characteristics revealed that the drag ratio decreases with an increasing rate of air injection. However, if the rate of air supplied exceeds a certain value, the efficiency of drag reduction can drop. In the case of optimum parameter design, a 21% drag reduction and an S/N ratio of 1.976 dB were obtained.

Copyright © 2013 by ASME
Your Session has timed out. Please sign back in to continue.


McCormick, M. E., and Bhattacharyya, R., 1973, “Drag Reduction of a Submersible Hull by Electrolysis,” Nav. Eng. J., 85, pp. 11–16. [CrossRef]
Madavan, N. K., Deutsch, S., and Merkle, C. L., 1984, “Reduction of Turbulent Skin Friction by Microbubbles,” Phys. Fluids, 27, pp. 356–363. [CrossRef]
Kitagawa, A., Sugiyama, K., Ashihara, M., Hishida, K., and Kodama, Y., 2003, “Measurement of Turbulence Modification by Microbubbles Causing Frictional Drag Reduction,” 4th ASME JSME Joint Fluids Engineering Conference, Honolulu, HI, Paper No. FEDSM2003-45648.
Dubnishchev, Y. N., Evseev, A. R., Sobolev, V. S., and Utkin, E. N., 1975, “Study of Gas-Saturated Turbulent Streams Using a Laser Doppler Velocity Meter,” J. Appl. Mech. Tech. Phys., 16(1), pp. 114–119. [CrossRef]
Merkle, C. L., and Deutsch, S., 1986, “Drag Reduction. Frontiers in Experimental Fluid Mechanics,” Lect. Notes Eng., 46, pp. 291–335. [CrossRef]
Kato, H., Iwashina, T., Miyanaga, M., and Yamaguchi, H., 1999, “Effect of Microbubbles on the Structure of Turbulence in a Turbulent Boundary Layer,” J. Mar. Sci. Technol., 4, pp. 155–162. [CrossRef]
Wu, S.-J., Hsu, C.-H., and Lin, T.-T., 2007, “Model Test of the Surface and Submerged Vehicles With the Micro-bubble Drag Reduction,” Ocean Eng., 34, pp. 83–93. [CrossRef]
Kawamura, T., Fujiwara, A., Takahashi, T., Kato, H., Matsumoto, Y., and Kodama, Y., 2004, “The Effects of the Bubble Size on the Bubble Dispersion and Skin Friction Reduction,” Proceeding of the 5th Symposium on Smart Control of Turbulence, Tokyo, pp. 145–151.
Lattorre, R., 1997, “Ship Hull Drag Reduction Using Bottom Air Injection,” Ocean Eng., 24, pp. 161–175. [CrossRef]
Latorre, R., Miller, A., and Phillips, R., 2003, “Micro-bubble Resistance Reduction on a Model SES Catamaran,” Ocean Eng., 30, pp. 2297–2309. [CrossRef]
Kato, H., and Kodama, Y., 2001, “Microbubbles as a Skin Friction Reduction Device—A Midterm Review of the Research,” 4th Symposium on Smart Control of Turbulence, University of Tokyo, National Maritime Research Institute, Tokyo. Available at http://www.turbulence-control.gr.jp/PDF/symposium/FY2002/Kato.pdf
Wu, S.-J., Ouyang, K., and Shiah, S.-W., 2008, “Drag Reduction on a Submerged Body by Electrochemistry Generated Micro-Bubbles in Turbulent Boundary Layer Flow,” J. Chung Cheng Inst. Tech., 36(2), pp. 1–14 (in Chinese). Available at http://jccit.ccit.ndu.edu.tw/ezfiles/7/1007/img/28/6(No.267).pdf
Montogomery, D. C., 2005, Design and Analysis of Experiment, 6th ed., Wiley, New York.
Kim, D. K., Choi, D. W., Choa, Y. H., and Kim, H. T., 2007, “Optimization of Parameters for the Synthesis of Zinc Oxide Nanoparticles by Taguchi Robust Design Method,” Colloids Surf., A, 331, pp. 170–173. [CrossRef]
Nikbakht, R., Sadrzadeh, M., and Mohammadi, T., 2007, “Effect of Operating Parameters on Concentration of Citric Acid Using Electrodialysis,” J. Food Eng., 83, pp. 596–604. [CrossRef]
Ross, P. J., 1996, Taguchi Techniques for Quality Engineering, McGraw-Hill, New York.
Mackay, D. J. C., 1992, “Bayesian Interpolation,” Neural Comput., 4, pp. 415–447. [CrossRef]
Foresee, D. F., and Hagan, M. T., 1997, “Gauss-Newton Approximation to Bayesian Learning,” International Conference on Neural Network, Vol. 3, pp. 1930–1935.


Grahic Jump Location
Fig. 1

Schematic drawing of the vertical circulating water tunnel

Grahic Jump Location
Fig. 2

Schematic drawing of the resistance meter

Grahic Jump Location
Fig. 3

Factorial level response graph: (a) the level effect on DR and (b) the level effect on S/N ratio

Grahic Jump Location
Fig. 4

The interaction between factor A and factor C on S/N ratio

Grahic Jump Location
Fig. 5

The response surface of S/N ratio and corresponding contours for factor B (d=5 μm)

Grahic Jump Location
Fig. 6

The simulation of A × C on S/N ratio obtained by ANN (d = 5 μm)

Grahic Jump Location
Fig. 7

Distribution of probability density for DR

Grahic Jump Location
Fig. 8

Effect of air void fraction on drag ratio for various flow speeds: (a) characteristic curves for optimum design and (b) characteristic curves for original design



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In