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Research Papers: Multiphase Flows

Flow Regime Identification in Boiling Two-Phase Flow in a Vertical Annulus

[+] Author and Article Information
Leonor Hernández1

Departamento de Ingeniería Mecánica y Construcción,  Universitat Jaume I. Campus de Riu Sec, Castellon 12071, Spainlhernand@emc.uji.es

J. Enrique Julia

Departamento de Ingeniería Mecánica y Construcción,  Universitat Jaume I. Campus de Riu Sec, Castellon 12071, Spain

Basar Ozar, Takashi Hibiki, Mamoru Ishii

School of Nuclear Engineering,  Purdue University, 400 Central Dr., West Lafayette, IN 47907-2017

1

Corresponding author.

J. Fluids Eng 133(9), 091304 (Sep 15, 2011) (10 pages) doi:10.1115/1.4004838 History: Received March 15, 2011; Revised August 08, 2011; Published September 15, 2011; Online September 15, 2011

This work describes the application of an artificial neural network to process the signals measured by local conductivity probes and classify them into their corresponding global flow regimes. Experiments were performed in boiling upward two-phase flow in a vertical annulus. The inner and outer diameters of the annulus were 19.1 mm and 38.1 mm, respectively. The hydraulic diameter of the flow channel, DH , was 19.0 mm and the total length is 4.477 m. The test section was composed of an injection port and five instrumentation ports, the first three were in the heated section (z/DH  = 52, 108 and 149 where z represents the axial position) and the upper ones in the unheated sections (z/DH  = 189 and 230). Conductivity measurements were performed in nine radial positions for each of the five ports in order to measure the bubble chord length distribution for each flow condition. The measured experiment matrix comprised test cases at different inlet pressure, ranging from 200 kPa up to 950 kPa. A total number of 42 different flow conditions with superficial liquid velocities from 0.23 m/s to 2.5 m/s and superficial gas velocities from 0.002 m/s to 1.7 m/s and heat flux from 55 kW/m2 to 247 kW/m2 were measured in the five axial ports. The flow regime indicator has been chosen to be statistical parameters from the cumulative probability distribution function of the bubble chord length signals from the conductivity probes. Self-organized neural networks (SONN) have been used as the mapping system. The flow regime has been classified into three categories: bubbly, cap-slug and churn. A SONN has been first developed to map the local flow regime (LFR) of each radial position. The obtained LFR information, conveniently weighted with their corresponding significant area, was used to provide the global flow regime (GFR) classification. These final GFR classifications were then compared with different flow regime transition models.

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Copyright © 2011 by American Society of Mechanical Engineers
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Figures

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Figure 1

Flow regimes definition: (a) bubbly flow, (b) cap-slug flow, (c) churn flow and (d) annular flow

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Figure 2

Installation of the vertical annulus

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Figure 3

Schematic of the test section

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Figure 4

Schematic of the conductivity probe design

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Figure 5

LFR identification methodology

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Figure 6

Comparison of GFR identification results for 200 kPa with published flow regime transition models: (a) Mishima and Ishii, (b) Kelessidis and Dukler, and (c) Das

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Figure 7

Comparison of GFR identification results for 500 kPa with published flow regime transition models: (a) Mishima and Ishii, (b) Kelessidis and Dukler, and (c) Das

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Figure 8

Comparison of GFR identification results for 750 kPa with published flow regime transition models: (a) Mishima and Ishii, (b) Kelessidis and Dukler, and (c) Das

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Figure 9

Comparison of GFR identification results for 950 kPa with published flow regime transition models: (a) Mishima and Ishii, (b) Kelessidis and Dukler, and (c) Das

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Figure 10

Concurrence ratio dependence with z/DH : (a) Mishima and Ishii, (b) Kelessidis and Dukler, and (c) Das

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