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.

References

References
1.
Tutu
,
N. K.
, 1982, “
Pressure Fluctuations and Flow Pattern Recognition in Vertical Two-Phase Gas–Liquid Flows
,”
Int. J. Multiphase Flow
,
8
, pp.
443
447
.
2.
Matsui
,
G.
, 1984, “
Identification of Flow Regimes in Vertical Gas-Liquid Two-Phase Flow Using Differential Pressure Fluctuations
,”
Int. J. Multiphase Flow
,
10
, pp.
711
720
.
3.
Cai
,
S.
,
Toral
,
H.
,
Qiu
J.
, and
Archer
,
J. S.
, 1994, “
Neural Network-Based Objective Flow Regime Identification in Air-Water Two-Phase Flow
,”
Can. J. Chem. Eng.
,
72
, pp.
440
445
.
4.
Mi
,
Y.
,
Ishii
,
M.
, and
Tsoukalas
,
L. H.
, 1998, “
Vertical Two-Phase Flow Identification Using Advanced Instrumentation and Neural Networks
,”
Nucl. Eng. Des.
,
184
, pp.
409
420
.
5.
Mi
,
Y.
,
Ishii
,
M.
, and
Tsoukalas
,
L. H.
, 2001, “
Flow Regime Identification Methodology With Neural Networks and Two-Phase Flow Models
,”
Nucl. Eng. Des.
,
204
, pp.
87
100
.
6.
Lee
,
J. Y.
,
Ishii
,
M.
, and
Kim
,
N. S.
, 2008, “
Instantaneous and Objective Flow Regime Identification Method for the Vertical Upward and Downward Co-Current Two-Phase Flow
,”
Int. J. Heat Mass Transfer
,
51
, pp.
3442
3459
.
7.
Hernandez
,
L.
,
Juliá
,
J. E.
,
Chiva
,
S.
,
Paranjape
,
S.
, and
Ishii
,
M.
, 2006, “
Fast Classification of Two-Phase Flow Regimes Based on Conductivity Signals and Artificial Neural Networks
,”
Meas. Sci. Technol.
,
17
, pp.
1511
1521
.
8.
Julia
,
J. E.
,
Liu
,
Y.
,
Paranjape
S.
, and
Ishii
,
M.
, 2008, “
Local Flow Regimes Analysis in Vertical Upward Two-Phase Flow
,”
Nucl. Eng. Des.
,
238
, pp.
156
169
.
9.
Julia
,
J. E.
,
Ozar
,
B.
,
Dixit
,
A.
,
Jeong
,
J. J.
,
Hibiki
,
T.
, and
Ishii
,
M.
, 2011, “
Flow Regime Development Analysis in Adiabatic Upward Two-Phase Flow in a Vertical Annulus
,”
Int. J. Heat Fluid Fl.
,
32
, pp.
164
175
.
10.
Kelessidis
,
V. C.
, and
Dukler
,
A. E.
, 1989, “
Modeling Flow Pattern Transitions for Upward Gas–Liquid Flow in Vertical Concentric and Eccentric Annuli
,”
Int. J. Multiphase Flow
,
15
, pp.
173
191
.
11.
Das
,
G.
,
Das
,
P. K.
,
Purohit
,
N. K.
, and
Mitra
,
A.K.
, 1999, “
Flow Pattern Transition During Gas Liquid Upflow Through Vertical Concentric Annuli—Part II: Mechanistic mModels
,”
ASME Trans. J. Fluids Eng.
121
, pp.
902
907
.
12.
Julia
,
J. E.
,
Ozar
,
B.
,
Dixit
,
A.
,
Jeong
,
J. J.
,
Hibiki
,
T.
, and
Ishii
,
M.
, 2009, “
Axial Development of Flow Regime in Adiabatic Upward Two-Phase Flow in a Vertical Annulus
,”
ASME Trans. J. Fluids Eng.
,
131
, Paper No. 021302.
13.
Ishii
,
M.
, 1977, “
One-Dimensional Drift-Flux Model and Constitutive Equations for Relative Motion Between Phases in Various Two-Phase Flow Regimes
,” ANL Report No. ANL-77-4.
14.
Sadatomi
,
M.
, and
Sato
,
Y.
, 1982, “
Two-Phase Flow in Vertical Noncircular Channels
,”
Int. J. Multiphase Flow
,
8
, pp.
641
655
.
15.
Furukawa
,
T.
, and
Sekoguchi
,
K.
, 1986, “
Phase Distribution for Air-Water Two-Phase Flow in Annuli
,”
Bull. JSME
,
29
, pp.
3007
3014
.
16.
Das
,
G.
,
Das
,
P. K.
,
Purohit
,
N. K.
, and
Mitra
,
A. K.
, 1999, “
Flow Pattern Transition During Gas Liquid Upflow Through Vertical Concentric Annuli. Part I. Experimental Investigations
,”
ASME Trans. J. Fluids Eng.
,
121
, pp.
895
901
.
17.
Sun
,
X.
,
Kuran
,
S.
, and
Ishii
,
M.
, 2004, “
Cap Bubbly-to-Slug Flow Regime Transition in a Vertical Annulus
,”
Exp. Fluids
,
37
, pp.
458
464
.
18.
Sharma
,
H.
,
Das
,
G.
, and
Samanta
,
A. N.
, 2006, “
ANN-Based Prediction of Two Phase Gas-Liquid Flow Patterns in a Circular Conduit
,”
AIChE J.
,
52
, pp.
3018
3028
.
19.
Jeong
,
J. J.
,
Ozar
,
B.
,
Dixit
,
A.
,
Juliá
,
J. E.
,
Hibiki
,
T.
, and
Ishii
M.
, 2008, “
Interfacial Area Transport of Vertical Upward Air–Water Two-Phase Flow in an Annulus Channel
,”
Int. J. Heat Fluid Flow
,
29
, pp.
178
193
.
20.
Bergles
,
A. E.
,
Lopina
,
R. F.
, and
Fiori
,
M. P.
, 1967, “
Critical-Heat-Flux and Flow Pattern Observations for Low-Pressure Water Flowing in Tubes
,”
J. Heat Transfer
,
89
, pp.
69
74
.
21.
Celata
,
G. P.
,
Cumo
,
M.
,
Farello
,
G. E.
,
Mariani
,
A. N.
, and
Solimo
,
A.
, 1991, “
Flow Pattern Recognition in Heated Vertical Channels: Steady and Transient Conditions
,”
Exp. Therm. Fluid Sci.
4
, pp.
737
746
.
22.
Hosler
,
E. R.
, 1968, “
Flow Patterns in High Pressure Two-Phase (Steam–Water) Flow With Heat Addition
,”
Chem. Eng. Prog., Symp. Ser.
,
64
, pp.
54
66
.
23.
Williams
,
C. L.
, and
Peterson
,
A. C.
, Jr.
, 1978, “
Two Phase Flow Patterns With High-Pressure Water in a Heated Four-Rod Bundle
,”
Nucl. Sci. Eng.
,
68
, pp.
115
168
.
24.
Meftha
,
K.
, and
Ruggles
,
A. E.
, 2006, “
Analysis of the Bubbly-Churn Regime Transition in a Scaled Model Boiling Water Reactor
,”
Nucl. Technol.
,
154
, pp.
328
334
.
25.
Taitel
,
Y.
,
Barnea
,
D.
, and
Dukler
,
A. E.
, 1980, “
Modeling Flow Pattern Transitions for Steady Upward Gas–Liquid Flow in Vertical Tubes
,”
AIChE J.
,
26
, pp.
345
354
.
26.
Mishima
,
K.
, and
Ishii
,
M.
, 1984, “
Flow Regime Transition Criteria for Upward Two-Phase Flow in Vertical Tubes
,”
Int. J. Heat Mass Transfer
,
27
(
5
), pp.
723
737
.
27.
Ozar
,
B.
,
Jeong
,
J. J.
,
Dixit
,
A.
,
Juliá
,
J. E.
,
Hibiki
,
T.
, and
Ishii
,
M.
, 2008, “
Flow Structure of Gas-Liquid Two-Phase Flow in an Annulus
,”
Chem. Eng. Sci.
,
63
, pp.
3998
4011
.
28.
Sekoguchi
,
K.
,
Inoue
,
K.
, and
Imasaka
,
T.
, 1987, “
Void Signal Analysis and Gas-Liquid Two-Phase Flow Regime Determination by a Statistical Pattern Recognition Method
,”
JSME Int. J.
,
30
, pp.
1266
1273
.
29.
Haykin
,
S.
, 1999,
Neural Networks: A Comprehensive Foundation
,
Macmillan
,
London, UK
.
30.
Zurada
,
J. M.
, 1992,
Introduction to Artificial Neural Systems
,
PWS Publishing Company
,
Boston
.
31.
Maren
,
A. J.
,
Herston
,
C. T.
, and
Pap
,
R. M.
, 1990,
Handbook of Neural Computing Applications
,
Academic
,
Orlando
.
32.
Wasserman
,
P. D.
, 1993,
Advanced Methods in Neural Computing
,
Van Nostrand Reinhold
,
New York
.
33.
Kohonen
T.
, 1987,
Self-Organization and Associative Memory
,
Springer-Verlag
,
Berlin
.
34.
Gartner
,
U.
,
Hohenberg
,
G.
,
Daudel
,
H.
, and
Oelschlegel
,
H.
, 2004,
Thermo- and Fluid Dynamic Processes in Diesel Engines
,
Springer
,
Berlin
Vol.
2
.
You do not currently have access to this content.