Technical Briefs

Classification of Two-Phase Flow Patterns by Ultrasonic Sensing

[+] Author and Article Information
Devesh K. Jha

e-mail: dkj5042@psu.edu

Asok Ray

Fellow ASME
e-mail: axr2@psu.edu

Kushal Mukherjee

e-mail: kum162@psu.edu
Department of Mechanical Engineering,
Pennsylvania State University,
University Park, PA 16802

Subhadeep Chakraborty

Department of Mechanical Engineering,
University of Tennessee,
Knoxville, TN 37996
e-mail: schakrab@utk.edu

Contributed by the Dynamic Systems Division of ASME for publication in the Journal of Dynamic Systems, Measurement, and Control. Manuscript received December 24, 2011; final manuscript received July 18, 2012; published online November 7, 2012. Assoc. Editor: Douglas Adams.

J. Dyn. Sys., Meas., Control 135(2), 024503 (Nov 07, 2012) (5 pages) Paper No: DS-11-1405; doi: 10.1115/1.4007555 History: Received December 24, 2011; Revised July 18, 2012

This paper presents a methodology for classification of two-phase flow patterns in fluid systems, which takes the measurements of an in situ ultrasonic sensor as inputs. In contrast to the common practice of having an array of ultrasonic detectors, the underlying algorithm requires only a single sensor hardware in combination with an integrated software of signal conditioning, feature extraction, and pattern classification. The proposed method is noninvasive and can be implemented in a variety of industrial applications (e.g., petrochemical processes and nuclear power plants). This concept of flow pattern classification is experimentally validated on a laboratory test apparatus.

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Hewitt, G., Delhaye, J., and Zuber, N., 1986, Multiphase Science and Technology, Vol. 2, Springer, New York, NY.
Bieberle, M., Schleicher, E., and Hampel, U., 2008, “Simulation Study on Electron Beam X-Ray CT Arrangements for Two-Phase Flow Measurements,” Meas. Sci. Technol., 19, p. 094003. [CrossRef]
Schleicher, E., Silva, M. J., Thiele, S., Li, A., Wollrab, E., and Hampel, U., 2008, “Design of an Optical Tomograph for the Investigation of Single- and Two-Phase Pipe Flows,” Meas. Sci. Technol., 19, p. 094006. [CrossRef]
Mi, Y., Ishii, M., and Tsoukalas, B., 2001, “Flow Regime Identification Methodology With Neural Networks and Two-Phase Flow Models,” Nucl. Eng. Des., 204, pp. 87–100. [CrossRef]
Warsito, W., and Fan, L.-S., 2001, “Neural Network Based Multi-Criterion Optimization Image Reconstruction Technique for Imaging Two- and Three-Phase Flow Systems Using Electrical Capacitance Tomography,” Meas. Sci. Technol., 12, pp. 2198–2210. [CrossRef]
Gao, Z., and Jin, N., 2009, “Flow-Pattern Identification and Nonlinear Dynamics of Gas-Liquid Two-Phase Flow in Complex Networks,” Phys. Rev. E: Nonlin. Plasma Phys., Fluid, Dyn., Rel. Topics, 79(Pt 2), p. 066303. [CrossRef]
Chakraborty, S., Keller, E., Talley, J., Srivastav, A., Ray, A., and Kim, S., 2009, “Void Fraction Measurement in Two-Phase Processes via Symbolic Dynamic Filtering of Ultrasonic Signals,” Meas. Sci. Technol., 20, p. 023001. [CrossRef]
Ray, A., 2004, “Symbolic Dynamic Analysis of Complex Systems for Anomaly Detection,” Signal Process., 84, pp. 1115–1130. [CrossRef]
Vidal, E., Thollard, F., de la Higuera, C., Casacuberta, F., and Carrasco, R., 2005, “Probabilistic Finite-State Machines—Part I and Part II,” IEEE Trans. Pattern Anal. Mach. Intell., 27(7), pp. 1013–1039. [CrossRef]
Sarkar, S., Mukherjee, K., Jin, X., Singh, D., and Ray, A., 2012, “Optimization of Symbolic Feature Extraction for Pattern Classification,” Signal Process., 92(3), pp. 625–635. [CrossRef]
Rajagopalan, V., and Ray, A., 2006, “Symbolic Time Series Analysis via Wavelet-Based Partitioning,” Signal Process., 86(11), pp. 3309–3320. [CrossRef]
Freitas, A., 2002, Data Mining and Knowledge Discovery With Evolutionary Algorithms, Springer-Verlag, Berlin.
Bishop, C., 2006, Pattern Recognition and Machine Learning, Springer, New York, NY.
Miettinen, K., 1998, Nonlinear Multiobjective Optimization, Kluwer International, Boston, MA.
Brunk, H., 1975, An Introduction to Mathematical Statistics, Xerox College Publishing, Lexington, MA.


Grahic Jump Location
Fig. 1

Illustration of three different patterns of two-phase gas–liquid flow. From left to right: bubbly, cap-bubbly, and slug flow patterns.

Grahic Jump Location
Fig. 2

Ultasonic reflections for bubbly, cap-bubbly, and slug flow. The x-axis in each plot represents the time corresponding to “duration of a single pulse,” while the y-axis and z-axis represent “consecutive pulses” and “strength of ultrasonic pulses,” respectively.




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