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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

Professor
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.

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References

Figures

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Fig. 1

Schematic drawing of the vertical circulating water tunnel

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Fig. 2

Schematic drawing of the resistance meter

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Fig. 4

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

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Fig. 7

Distribution of probability density for DR

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Fig. 3

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

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Fig. 5

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

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Fig. 6

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

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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

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