Probabilistic determination of two-phase flow regimes in horizontal tubes utilizing an automated...

RESEARCH ARTICLE

Probabilistic determination of two-phase flow regimesin horizontal tubes utilizing an automated imagerecognition technique

Emad W. Jassim Æ Ty A. Newell Æ John C. Chato

Received: 31 August 2006 / Revised: 15 December 2006 / Accepted: 16 January 2007 / Published online: 10 February 2007� Springer-Verlag 2007

Abstract Probabilistic two-phase flow map data is

experimentally obtained for R134a at 25.0, 35.0, and

49.7�C, R410A at 25.0�C, mass fluxes from 100 to

600 kg/m2-s, qualities from 0 to1 in 8.00, 5.43, 3.90, and

1.74 mm I.D. single, smooth, adiabatic, horizontal

tubes in order to extend probabilistic two-phase flow

map modeling techniques to single tubes. A new web

camera based flow visualization technique utilizing an

illuminated diffuse striped background was used to

enhance images, detect fine films, and aid in the auto-

mated image recognition process developed in the

present study. This technique has an average time

fraction classification error of less than 0.01.

List of symbols

dP pressure drop (Pa)

dz unit length (m)

F observed time fraction (–)

x flow quality (–)

Greek symbolsa void fraction (–)

Subscripts

ann pertaining to the annular flow regime

liq pertaining to the liquid flow regime

int pertaining to the intermittent flow regime

vap pertaining to the vapor flow regime

1 Introduction

Flow regime maps developed from flow visualization

observations are commonly used or developed in the

literature such as Wojtan et al. (2005a, b), Garimella

(2004), Garimella et al. (2003), Coleman and Garim-

ella et al. (2003), El Hajal et al. (2003), Thome et al.

(2003), Didi et al. (2002), Zurcher et al. (2002a, b),

Dobson and Chato (1998), Mandhane et al. (1974), and

Baker (1954) to aid in the modeling of two-phase flow.

The three main types of two-phase flow regime maps in

the literature Baker/Mandhane, Taitel-Dukler, and the

most commonly used Steiner (1993) type depict

boundaries between flow regimes that are not easily

represented by continuous functions. This is evident

from Fig. 1, which contains a depiction of a typical

Steiner (1993) type flow map. Two-phase flow models

that incorporate these traditional flow maps are com-

plicated in order to eliminate discontinuities at flow

regime boundaries and incorporate the flow regime

information as functions. Furthermore Coleman and

Garimella (2003) and El Hajal et al. (2003) indicate

that more than one flow regime can exist near the

boundaries or within a given flow regime on a Steiner

(1993) type flow map for single tubes.

Recently, efforts have been made in the literature

to develop flow regime maps in a more quantitative

manor than manual classification. Plzak and Shedd

(2003) developed an automated image recognition

E. W. Jassim (&) � T. A. Newell � J. C. ChatoDepartment of Mechanical Science and Engineering,University of Illinois, 1206 W. Green St.,Urbana, IL, USAe-mail: [email protected]

123

Exp Fluids (2007) 42:563–573

DOI 10.1007/s00348-007-0264-8

software to automatically detect the flow regime

present from a series of images at a given flow con-

dition. Wattelett (1994) used statistical and spectral

analysis of pressure traces from high-speed pressure

transducers to determine flow regime transitions in

smooth tubes. Wattelett (1994) found that the stan-

dard deviation divided by the mean differential

pressure drop was the strongest indicator of flow re-

gime transition for their data. Liebenberg et al. (2005)

further developed this technique using power spectral

density distributions of pressure traces to determine

the flow regime present in smooth and microfinned

tubes. Revellin et al. (2006) determined the flow re-

gime present in 0.5 mm microchannels using the time

trace output of photodiodes measuring laser light

transmission through a glass test section. Plzak and

Shedd (2003), Liebenberg et al. (2005), and Revellin

et al. (2006) present their flow regime data on Steiner

style flow regime maps.

Probabilistic two-phase flow regime map data first

obtained by Nino (2002) for refrigerant and air–water

flow in multi-port microchannels is found by Jassim

and Newell (2006) to eliminate the discontinuities

created by traditional flow maps. Probabilistic two

phase flow regime maps have quality on the horizontal

axis and the fraction of time (F) in which a particular

flow regime is observed in a series of pictures taken at

given flow condition on the y axis as seen in Fig. 2.

Jassim and Newell (2006) developed curve fit func-

tions to represent Nino’s (2002) 6-port microchannel

time fraction data that are continuous for the entire

quality range with correct physical limits. Jassim and

Newell (2006) then utilized the probabilistic flow re-

gime map time fraction curve fits to predict pressure

drop and void fraction as shown in Eqs. 1 and 2,

respectively.

dP

dz

� �total

¼ FliqdP

dz

� �liq

þ FintdP

dz

� �int

þ FvapdP

dz

� �vap

þ FanndP

dz

� �ann

ð1Þ

atotal ¼ Fliqaliq þ Fintaint þ Fvapavap þ Fannaann: ð2Þ

In this way pressure drop and void fraction models

developed for a particular flow regime are easily and

properly weighted for the entire quality range on a

consistent time fraction basis.

Single tubes of approximately 3 mm in diameter and

larger are found to contain a stratified flow regime

which is absent in the 1.54 mm hydraulic diameter

microchannels of Nino (2002). Diamanides and West-

water (1988) support this observation because they

indicate that the transition from ‘‘microchannel’’

behavior to ‘‘large tube’’ behavior occurs in the 3 mm

tube diameter range. Furthermore, vapor only flow is

not present below a quality of 100% in single tubes.

Consequently, the flow regime maps and two-phase

flow models developed by Jassim and Newell (2006)

are not applicable to single large tubes with hydraulic

diameters above 3 mm, and may not be applicable to

single microchannel tubes.

The difficulty with the probabilistic flow map based

modeling technique is that large numbers of pictures

must be classified for each flow condition in order to

create a large amount of data necessary to generalize

the time fraction functions with respect to refrigerant

properties and flow conditions. Consequently, an

automated image recognition technique similar to that

of Plzak and Shedd (2003) is utilized in the present

study. However, the software developed by Plzak and

Shedd (2003) is suitable for the formulation of tradi-

tional Steiner (1993), Baker/Mandhane, and Taitel-

Dukler type flow maps. The present study develops

image recognition software that determines the flow

regime present in each image for a series of images at

each given flow condition in order to formulate the

time fraction of each flow regime to create a probabi-

listic flow regime map database.

In the present study, probabilistic two-phase flow

map data is experimentally obtained for 8.00, 5.43,

3.90, and 1.74 mm diameter single, smooth, adiabatic,

horizontal tubes for R134a at 25.0, 35.0, and 49.7�C and

R410A at 25.0�C and for a range of mass fluxes and

qualities in order to aid in the generalization of prob-

abilistic two-phase flow regime maps to physical

parameters. A generalized probabilistic two-phase flow

regime map would be useful in the modeling of two-

phase pressure drop, void fraction, and heat transfer. A

Intermittentflow

Annularflow

Mist flow

Stratified wavy flow

Stratified flow

x0 0.5

)s-2^m/gk(

G

x

-

1

Fig. 1 Depiction of a typical Steiner (1993) type flow map

564 Exp Fluids (2007) 42:563–573

123

new optical method is utilized to enhance the images

and aid in the image recognition process. Nearly one

million flow visualization pictures were utilized in the

formulation of the probabilistic two-phase flow regime

map database.

2 Materials and methods

2.1 Two-phase flow loop and test section design

Flow visualization data was obtained from the two-

phase flow loop depicted in Fig. 3. The liquid refrig-

erant is pumped with a gear pump that is driven by a

variable frequency drive from the bottom of a 2 l re-

ceiver tank through a water cooled shell and tube style

subcooler in order to avoid pump cavitation. The liquid

refrigerant then travels through a Coriolis style mass

flow meter with an uncertainty of ±0.1% followed by a

preheater used to reach the desired quality. The pre-

heater consists of a finned tube heat exchanger with

opposing electric resistance heater plates bolted on

either side of the heat exchanger. The electric heaters

are controlled with on/off switches and a variable auto

transformer to provide fine adjustment of quality. The

power supplied to the preheater is measured by a

power transducer. An energy balance was performed

by heating single phase flow in the preheater and

measuring the temperature difference across it along

with the flow rate to determine the uncertainty asso-

ciated with the preheater power input is ±0.8%. This

preheater design has enough thermal mass so that the

heaters do not burn out at a quality of 100% and has a

small enough thermal mass so that steady state condi-

tions can be rapidly attained. The refrigerant is then

directed through 90� bends to remove effects of heat

flux from the preheater such as dryout before it reaches

the test section. Finally, the refrigerant is condensed in

a water cooled brazed plate heat exchanger and is di-

rected back into the receiver tank. The pressure before

the inlet of the preheater is measured by a pressure

transducer with accuracy of ±1.9 kPa. The tempera-

tures before the inlet of the preheater and the test

section are measured with type T thermocouples with

an uncertainty of ±0.1�C (confirmed with an RTD with

an uncertainty of ±0.01�C). The measured pressures

and temperatures at the inlet of the preheater are used

to determine the enthalpy of the subcooled refrigerant

entering the preheater. The enthalpy at the exit of the

preheater is determined by adding the energy input to

the preheater for the measured refrigerant mass flow

rate. The test section inlet quality is then determined

from the saturation temperature measured at the inlet

of the test section and the computed enthalpy.

The test sections consist of glass tubes with dimen-

sions as listed in Table 1. The 8.00 mm I.D. test section

is 0.254 m long since the inner diameter of the

incoming copper pipe was also 8 mm. The 5.43 mm

and 3.90 mm test sections are 1.2 m long and were

transitioned gradually in order to avoid transition ef-

fects. The 1.74 mm I.D. tube is 0.254 m long to avoid

fracture and excessive pressure drop and was gradually

transitioned from the 8 mm ID tube to avoid entrance

effects. Brass compression fittings with nylon ferrules

were used to join the copper pipe of the flow loop to

the glass test section in order to avoid leakage or

fracture of the test sections. The refrigerant is con-

densed after the test section in a flat plate heat ex-

changer with cold water at 5�C. The test section

temperature is controlled by varying the flow rate of

the cold water entering the condenser.

2.2 Flow visualization technique

A web camera (an inexpensive digital ‘‘board camera’’

used to communicate over the World Wide Web)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0

X

F

Liquid Curve FitLiquid DataIntermittent Curve FitIntermittent DataAnnular Curve FitAnnular DataVapor Curve FitVapor Data

0.2 0.4 0.6 0.8 1

Fig. 2 Probabilistic flow map with time fraction curve fits forR410A, 10�C, 300 kg/m2-s in a 6-port 1.54 mm hydraulicdiameter microchannel taken from Jassim and Newell (2006)

PreheaterGlass Test Section

MassFlowmeter

Pump

Subcooler

ReceiverTank

Condenser

P T

T

TPreheater

P TT

T

TT

Fig. 3 Two-phase flow loop schematic

Exp Fluids (2007) 42:563–573 565

123

based flow visualization technique was developed and

used in this study as depicted in the schematic in Fig. 4.

A Logitech Quickcam Pro 3000� CCD type web

camera with 640 · 480 pixel resolution, 30 frame per

second frame rate, and adjustable focal length is used

to capture the images. This frame rate is sufficient since

information between consecutive frames is not re-

quired in the image recognition process described in

Sect. 2.4. The slow frame rate allows for a time average

over a sufficiently large period of time (approximately

30 s) to provide a representative time fraction. A dif-

fuse white film pigmented with evenly spaced black

stripes is placed in the background (behind the glass

tube) and illuminated with a stroboscope directed to-

wards the camera. The stroboscope is also cycled at 30

frames per second with a flash duration less than 50 ls

in order to create clear images. The stripe width,

spacing, and distance from the centerline of the test

sections are indicated in Table 1. These dimensions

were qualitatively determined to best enhance the

detection of fine films for the 8.00 mm diameter tube,

and are varied according to pipe diameter to maintain

approximately the same stripes per pipe diameter

length and the same stripe width and spacing per pipe

diameter length. No information was found in the lit-

erature about this optical technique and should be

further investigated in the future.

The striped background serves to enhance the image

as seen in Fig. 5. Both images in Fig. 5 were taken for

R410A in 3.9 mm I.D. tube, a mass flux of 200 kg/m2-s,

a 0.99 quality, and at a 25.0�C saturation temperature

with the same diffuse paper background. However, the

image on right has evenly spaced black stripes on the

background. The black stripes provide contrast that

enhances the image and allows for the detection of fine

liquid films. Furthermore, these stripes aid in the image

recognition process. The stripes appear to be out of

focus in Fig. 5 because the camera focal length is ad-

justed to the center of the tube instead of the stripes in

the background.

2.3 Flow visualization test matrix

Flow visualization pictures were taken for the follow-

ing test matrix:

• R134a at 25.0, 35.0, and 49.7�C and R410A at

25.0�C

• 8.00, 5.43, 3.90, and 1.74 mm I.D. glass test sections

• mass fluxes of 400, 500, 600 kg/m2-s for the 1.74 mm

ID test sections

• mass fluxes of 100, 200, 300, 400 kg/m2-s for other

tube sizes

• qualities from 0 to 100%

Approximately 30 s of flow visualization video (900

images) were taken for each flow condition. Conse-

quently, a sufficiently large number of images are taken

to capture even infrequent events.

2.4 Image recognition software development

Image recognition software was developed in order to

automatically classify the flow regimes of the obtained

images. The original image is first converted into a

grayscale image as seen in Figs. 6a, 7a, 8a, 9a, and 10a

for different flow conditions and refrigerants. Next, the

pixels are thresholded (pixel values above a threshold

value are turned white and below that value are turned

black) to a percentage of the brightness of the white

background as seen in Figs. 6b, 7b, 8b, 9b, and 10b.

This percentage varied based upon tube type since the

light intensity changed as the distance of the back-

ground from the centerline of the tube varied with tube

size. It should be noted that thin vertical black lines are

drawn between the vertical stripes in Figs. 6b, 7b, 8b,

Table 1 Test section and stripe dimensions

Test sectionI.D. (mm)

Test sectionO.D. (mm)

Test sectionlength (m)

Stripewidth (mm)

Center to centerstripe distance (mm)

Distance of striped filmfom centerline of tube (mm)

8.00 12.70 0.25 2.1 5.1 485.43 9.53 1.20 1.1 3.5 363.90 6.35 1.20 0.8 2.1 201.74 3.00 0.25 0.3 1.0 3

Web Camera

Glass test section

Stroboscope

Striped DiffuseTranslucent Background

Fig. 4 Flow visualization schematic

566 Exp Fluids (2007) 42:563–573

123

9b, and 10b for illustration purposes and will be dis-

cussed later. If the threshold value is set correctly, the

vapor to liquid interface will appear black. The black

stripes which normally appear through the tube from

the background during liquid flow as seen in Fig. 6b are

bent/scattered horizontally when vapor is present in

the tube as seen in Figs. 7b, 8b, 9b, and 10b for vapor

only, intermittent, stratified, and annular flows,

respectively. Consequently, black regions are found in

the normally white space of the thresholded images.

This phenomenon is utilized in the image recognition

process to detect the presence of vapor at a given tube

Fig. 5 Flow visualizationpictures of R410A, 3.9 mmI.D. tube, 200 kg/m2-s, 0.99quality, and 25.0�C with aplane diffuse background(left) and with a stripeddiffuse background (right)

Fig. 6 a–c R134a liquid in5.43 mm I.D. Glass tubewithout thresholding, withthresholding, and with adirectional Sobel filterapplied, respectively

Fig. 7 a–d R410A vapor in5.43 mm I.D. glass tubewithout thresholding, withthresholding, with adirectional Sobel filterapplied, and a magnified view,respectively

Fig. 8 a–c R134a at 200 kg/m2-s, 2.2% quality, and25.0�C in 5.43 mm I.D. glasstube without thresholding,with thresholding, and with adirectional Sobel filterapplied, respectively

Exp Fluids (2007) 42:563–573 567

123

location. The vertical pixel lines that lie directly be-

tween the black stripes seen through the tube when all

liquid is present are always white with the maximum

pixel value of 255 as seen in Fig. 6b. Vertical black

lines were drawn in the thresholded images in Figs. 6b,

7b, 8b, 9b, and 10b to indicate the location of the pixel

lines that were scanned. If all of the pixel values in all

of the vertical pixel lines scanned are equal to 255, i.e.

unbroken as in Fig. 6b, then the flow is classified as the

liquid flow regime. If the flow has one or more broken

lines, indicating the presence of vapor, and one or

more of the lines are unbroken, indicating the presence

of liquid, the flow is classified as intermittent flow. A

three pipe diameter length of tube is used to determine

whether the flow is liquid only or intermittent.

If all of the scanned lines are broken a directional

Sobel filter algorithm is utilized as seen in Figs. 6c, 7c,

8c, 9c, and 10c in order to determine whether the flow

is stratified or annular (Figs. 6c, 8c are shown for

illustration purposes). The directional Sobel filter

computes the direction of each pixel brightness gradi-

ent vector and assigns a pixel intensity between 0 and

255 corresponding to 0� through 360� (counterclock-

wise) with 0 pointing directly down on the page. Two

horizontal pixel lines one diameter long are scanned on

the directional Sobel filtered image at the top of the

tube in the center of the solid gray region when vapor

is present (this region is absent when liquid is present).

This location is determined manually because it is

found to be a function of tube diameter but is not

found to be a function of fluid properties. Conse-

quently, only three locations, one for each large tube

diameter (3.90–8.00 mm tubes), were manually deter-

mined for all of the present experiments. The location

of these scanned horizontal pixel lines are indicated by

white lines drawn on each image and can be seen in

Figs. 6c, 7c, 8c, 9c, and 10c for different flow condi-

tions. Figures 7d and 10d depict magnified views of the

scanned regions in Figs. 7c and 10c for vapor only flow

and annular flow, respectively. When only vapor is

present, as in Fig. 7, the scanned region depicted in

Fig. 7d is relatively homogeneous with the majority of

the pixel values at or near 128 which indicates a pixel

brightness gradient pointing in the upward direction.

Figure 11 supports this observation as it depicts the

scanned pixel brightness values from Fig. 7d that are

sorted in descending order. The pixel brightness dis-

tribution for a fully stratified flow where only vapor is

present at the top of the tube is similar to the distri-

bution in Fig. 11. However, as annular flow is ap-

proached a liquid film appears at the top of the tube

and it increases in waviness. This increase in waviness

Fig. 9 a–c R134a at 100 kg/m2-s, 44.1% quality, and25.0�C in 5.43 mm I.D. glasstube without thresholding,with thresholding, and with adirectional Sobel filterapplied, respectively

Figs. 10 a–d R410A at300 kg/m2-s, 79.3% quality,and 25.0�C in 5.43 mm I.D.glass tube withoutthresholding, withthresholding, with adirectional Sobel filterapplied, and a magnified view,respectively

568 Exp Fluids (2007) 42:563–573

123

causes the distribution of the scanned pixel brightness

to change as the waves scatter light in multiple direc-

tions as can be seen in Fig. 10d for a fully annular flow.

The once homogeneous gray region when vapor is

present with most of the pixel brightness values at 128

becomes inhomogeneous. The pixel values of the

scanned pixel lines of Fig. 10d are depicted in Fig. 12.

Figure 12 shows a greater number of pixels that have

deviated from the baseline of 128 and also has a greater

magnitude of deviation of pixel brightness values. This

phenomenon is utilized in order to detect annular flow.

The difference between each scanned pixel value

above 128 and the baseline of 128 are summed and

divided by the total number of pixels scanned in order

to obtain the average positive deviation from the

baseline. The average positive deviation from the

baseline was used instead of the mean absolute devi-

ation or the standard deviation because it was found to

have the least scatter when only vapor is present at the

top of the tube. A one-pipe diameter length of tube is

used to determine whether the image is stratified or

annular in order to provide a sufficiently large number

of pixels for reasonable resolution. The Sobel filter

algorithm is not used in the 1.74 mm diameter tube

because stratified flow was not observed in the micro-

channel. Therefore, if the flow is not classified as liquid/

intermittent in the 1.74 mm diameter tube it is classi-

fied as annular.

Vapor or fully stratified flow (with no liquid at the

top of the tube), is found to have an average positive

deviation of 1.61, a maximum average positive devia-

tion of 2.07 and a minimum average positive deviation

of 0.916. The average positive deviation of the vapor

only condition is found to have no dependence on tube

size or refrigerant. The vapor only condition was only

encountered at qualities over 100%, therefore there

was no need to distinguish stratified and vapor only

flow in the present study. The flow is classified as

annular if the average positive deviation is greater than

9, and classified as stratified if the average positive

deviation is 9 or less. The threshold value is used

consistently for 8.00 through 3.90 mm tube sizes and all

refrigerants. This threshold value was selected through

comparison with heat transfer data obtained by Jassim

(2006). Moreover, Jassim (2006) found this value to

yield accurate heat transfer values for a wide range of

refrigerants, saturation temperatures, mass fluxes, and

qualities from multiple sources (806 data points in to-

tal). The heat transfer predictions match the conden-

sation models developed by Thome et al. (2003) and

Cavalini et al. (2003) which were each developed with

larger databases. The flow map data obtained with a

threshold value of 9 is also used by Jassim (2006) to

predict pressure drop (772 data points of different fluid

properties tube diameters and flow conditions) and

void fraction (428 data points of different fluid prop-

erties tube diameters and flow conditions) in horizontal

smooth tubes. Observations indicated that the pres-

ence of a liquid film at the top of the tube is not suf-

ficient for defining an ‘‘annular’’ flow. Instead, some

level of film activity, related to an agitation in a film

that results in the characteristics ascribed to annular

film heat transfer (turbulent flow), is used to determine

the threshold level. This algorithm can easily be mod-

ified to distinguish vapor only flow in multi-port tubes

by using a similar Sobel filter algorithm at the bottom

of the tube.

The automated flow visualization software loops

through each image in an AVI video file, classifies the

flow regime present, and keeps a running total of the

number of images in each flow regime. Furthermore, a

batch file system was utilized that sequentially reads

0

50

100

150

200

250

300

0

pixel number

eulav sse

nth

girb lexi

p

pixel brightness = 128

50 100 150 200 250 300 350 400

Fig. 11 Pixel brightness distribution of the pixel lines in Fig. 7dof R410A vapor in 5.43 mm I.D. glass tube with a directionalSobel filter applied

0

50

100

150

200

250

300

0

pixel number

eulav sse

nth

girb lexi

p

pixel brightness = 128

50 100 150 200 250 300 350 400

Fig. 12 Pixel brightness distribution of the pixel lines in Fig. 10dof R410A at 300 kg/m2-s, 79.3% quality, and 25.0�C in 5.43 mmI.D. glass tube with a directional Sobel filter applied

Exp Fluids (2007) 42:563–573 569

123

filenames from a text file and outputs time fraction

information to another text file.

The accuracy of the image recognition software in

the liquid and intermittent flow regime was determined

by manually classifying 4,800 images of different tube

size, mass fluxes, fluids, and qualities. The software is

found to have a maximum time fraction error of ±0.04

and an average error of ±0.01 for the liquid and

intermittent flow regimes. The error in the stratified

and annular flow regimes is estimated by changing the

average positive deviation threshold by 0.58 in either

direction, half of the difference between the maximum

and minimum average positive deviation observed

when vapor exists at the top of the tube, and then

computing the error associated with this change. Using

this method the average error in the annular and

stratified flow regime time fraction is found to be ±0.01

with a maximum of ±0.152 and a minimum of 0.

An alternate algorithm for automated image detec-

tion can also used with similar results. In this method

the image is thresholded at a lower value than for the

first method so that the black stripes from the back-

ground appear to be thin but solid when liquid is

present. In this case the presence of a liquid vapor

interface will bend/scatter the light so that white pixels

will appear, i.e. the black stripe is broken, where the

solid black stripe exists during liquid only flow. After

the image is thresholded, the pixel lines located inside

the black stripes which appear on the tube from the

background during liquid flow are scanned. If all of the

pixels in a scanned line are black, pixel intensity of 0, it

would indicate that the flow is liquid at that location. If

some of the pixels are white, i.e. the black stripe is

broken with a pixel intensity of 255, it would indicate

that a vapor–liquid interface exists at that location.

Consequently, if all of the pixels in the scanned vertical

pixel lines are black, the black stripe is unbroken with a

value of 0, the image would be classified as liquid flow.

If it is not classified as liquid flow (at least one of the

scanned pixel lines contains white pixels) and at least

one of the pixel lines contains only black pixels, indi-

cating liquid bridging, then the image is classified as

intermittent flow. If all of scanned lines contain white

pixels then the same algorithm is utilized as described in

the first method to classify the image as either stratified

or annular flow. The time fraction classification in the

present work uses the first algorithm mentioned.

3 Results and discussion

Probabilistic two-phase flow regime map data is ob-

tained for the test matrix described above in a similar

manner as Nino (2002) has obtained data for multi-

port microchannels. Figures 13, 14, 15, and 16 depict

probabilistic two-phase flow regime map data for

R410A at 25.0� in the 3.90 mm test section with mass

fluxes varying from 100 to 400 kg/m2-s, respectively.

The solid points in the figures represent the time

fraction output from the image recognition code

developed. The liquid flow regime in the large tubes of

the present study is found to be less significant than the

liquid flow regime found by Nino (2002) in multi-port

microchannels. Consequently, the liquid flow regime is

considered to be part of the intermittent flow regime

and they are summed together. The stratified flow re-

gime is found in the 3.90 mm diameter and larger tubes

but is absent in the 1.74 mm diameter tube of the

present study and in the 1.54 mm multi-port micro-

channels of Nino (2002). This is consistent with the

findings of Diamanides and Westwater (1988) who

indicate that the transition between large tube and

microchannel behavior occurs in the 3 mm diameter

range. The vapor only flow regime, observed by Nino

(2002) in multi-port microchannels, is absent in all of

the single channel tubes of the present study. There-

fore, it is postulated that vapor only flow is a result of

multi-port effects. The annular flow regime time frac-

tion in the present study drops drastically as quality is

decreased below 0.4, however, there is a minimum at

approximately 0.1 quality and the time fraction is seen

to increase at lower qualities. This seems to lack a

physical basis since the annular flow time fraction

should disappear as quality approaches zero. The

intermittent and liquid flow regime should approach 1

as quality approaches 0 since all the flow should be-

come liquid. The low quality annular flow appears to

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1x

F

intermittent + liquid

stratified

annular

intermittent + liquid extrapolated

annular extrapolated

El Hajal et al. (2003) predictsstratified wavy flow for

entire quality range

Fig. 13 Probabilistic two-phase flow regime map data forR410A, 100 kg/m2-s, 25.0�C, adiabatic 3.90 mm I.D. tube

570 Exp Fluids (2007) 42:563–573

123

be intermittent flow with bubbles longer than the field

of view. The 30 frame per second frame rate is suffi-

cient to capture the movement of a bubble front or tail

in consecutive images of low quality and low mass flux

(200 kg/m2-s or less) flow exhibiting this behavior. The

apparently misclassified images are portions of single,

thin, smooth bubbles which stretch past the field of

view. Since the bubbles are smooth they cannot be

considered to have a turbulent film as we have defined

annular flow. This observation is similar to that pos-

tulated Jassim and Newell (2006) in multi-port micro-

channels, where they considered low quality vapor to

be intermittent flow with bubbles longer than the field

of view. Further investigation with a high-speed cam-

era would be required to verify this postulate at mass

fluxes higher than 200 kg/m2-s. However, low quality

annular flow is relatively insignificant with time frac-

tion values typically less than 0.2 and at qualities less

than 0.1. The time fraction data at qualities below the

minimum in the annular flow time fraction was sub-

tracted from the annular flow regime and added to the

intermittent flow regime. The resulting extrapolated

intermittent and liquid, and annular flow regimes are

plotted in Figs. 13, 14, 15, and 16 as ‘‘hollow’’ points.

Figures 13, 14, 15, and 16 also contain flow regime

transition predictions from the El Hajal et al. (2003)

flow regime map in order to compare the present

probabilistic two-phase flow map data with a tradi-

tional flow map found in the literature. The El Hajal

et al. (2003) predictions have some level of agreement

with the present flow map data. At a 100 kg/m2-s

(Fig. 13) the stratified time fraction is near 1 for the

majority of the quality range which agrees with the El

Hajal et al. (2003) prediction of stratified wavy flow. At

a 200 kg/m2-s (Fig. 14) El Hajal et al. (2003) predicts

stratified wavy flow up to a quality of 0.23 which cor-

responds to a time fraction of approximately 0.7. For

mass fluxes of 300 and 400 kg/m2-s (Figs. 15, 16,

respectively) the stratified time fraction is below 0.7.

This is in agreement with the previous observation in

that the El Hajal et al. (2003) flow map does not pre-

dict any stratified flow for the 300 and 400 kg/m2-s

mass fluxes. The intermittent to annular transition for

the 200 kg/m2-s mass flux (Fig. 14) predicted by El

Hajal et al. (2003) appears to occur when the annular

flow time fraction reaches 1. However, the intermittent

to annular transitions predicted by El Hajal et al.

(2003) for the 300 and 400 kg/m2-s (Figs. 15, 16,

respectively) do not seem to agree. The annular flow

time fraction reaches a value of 1 at lower qualities as

mass flux increases. Discrepancies between the El

Hajal et al. (2003) predictions and the present proba-

bilistic data may be attributed to the fact the El Hajal

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1x

F


stratified

annular



Intermittent Annular

El Hajal et al. (2003)Flow Boundary Prediction

X=0.49

Stratified Wavy

Intermittent

X=0.23


0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1x

F


stratified

annular





X=0.49


0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1

x

F


stratified

annular





X=0.49


Exp Fluids (2007) 42:563–573 571

123

et al. (2003) map is a generalized flow map based on a

wide range of fluid properties, tube sizes, and flow

conditions.

The uncertainty in quality is determined from the

uncertainty of the pressure, temperature, mass flow

rate, and preheater energy input measurements. The

uncertainty in quality is highest in Fig. 13 where it is

less than ±0.033 at x = 0.99, ±0.018 at x = 0.5, and

±0.003 at x = 0.02 and the uncertainty in quality is the

lowest in Fig. 16 where it is less than ±0.018 at x = 0.99,

±0.01 at x = 0.5, and ±0.002 at x = 0.02. The uncer-

tainty in mass flux is maximum of ±4% with the

majority of the uncertainty below ±2%.

The probabilistic flow regime map data obtained by

the methods detailed in the present study is used to

create a generalized probabilistic flow regime map for

smooth horizontal tubes by Jassim (2006). Jassim’s

(2006) generalized probabilistic flow map is developed

by curve fitting the present data. The curve fit constants

are then generalized as functions of tube diameter,

mass flux, and fluid properties in order to increase the

utility of the probabilistic flow regime map.

4 Concluding remarks

In summary, probabilistic two-phase flow maps are

found in the literature to be useful in the modeling of

two-phase flow in multi-port microchannels. A two-

phase flow loop was constructed and a new web camera

based image recognition technique was developed in

order to obtain the flow visualization images necessary

to obtain probabilistic two phase flow map data for

smooth, horizontal, adiabatic, single channel tubes.

The flow visualization technique utilizes a new optical

method consisting of an illuminated striped diffuse

background to enhance the images and aid in the im-

age recognition process. Nearly, one million flow

visualization images were obtained for R134a at 25.0,

35.0, and 49.7�C, R410A at 25.0�C, mass fluxes from

100 to 600 kg/m2-s, qualities from 0 to1 in 8.00, 5.43,

3.90, and 1.74 mm I.D. smooth, horizontal, adiabatic

tubes in order to provide the flow visualization data

necessary to create a generalized probabilistic flow

regime map. Image recognition software is developed

to classify the flow regime present in each image and

formulate the time fraction of each flow regime for

each flow condition. The time fraction error associated

with the image recognition software is found to be a

maximum of ±0.04 and an average of ±0.01 for the

intermittent and liquid flow regimes and an average of

±0.01 with a maximum of ±0.152 for the stratified and

annular flow regimes. Jassim (2006) developed curve

fits of the present probabilistic two-phase flow map

data in order to generalize the time fraction informa-

tion using physically meaningful parameters. The time

fraction information represented as continuous func-

tions can be utilized to model single tube pressure

drop, void fraction, and heat transfer with a common

two-phase flow map basis in a similar manner as Jassim

and Newell (2006) demonstrated for multi-port mi-

crochannels.

Acknowledgments The authors would like to thank the AirConditioning and Research Center (ACRC) at the University ofIllinois for their financial support. The authors would also like tothank Matthew Alonso, Francisco Garcia, Sarah Brewer, FrankLam for aiding in the data acquisition, and Wen Wu for hisassistance with Visual Basic.

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