This document summarizes a study on measuring void fraction in two-phase flows using image processing techniques. The authors developed an image sequence algorithm to measure void fraction from high-speed camera images of two-phase flows in mini/micro channels. The algorithm calculates chordal void fraction, number of bubbles, bubble frequency, and average bubble length. When applied to experimental images of air-water flow, the algorithm provided void fraction measurements that closely matched values from established correlations. The algorithm offers an inexpensive and non-intrusive method for characterizing important parameters of two-phase flows.

1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME
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TWO PHASE FLOW VOID FRACTION MEASUREMENT USING
IMAGE PROCESSING TECHNIQUE
Manish P. Pujara1
, Lav Kumar2
, Ashish Mogra3
1,2,3
(Department of Mechanical Engineering, Sardar Vallabhbhai National Institute of
Technology, Surat-395007, India)
ABSTRACT
Two phase flow in mini channel are vital in design and development of thermal
system such as air- condition system, electronics equipment cooling system. Mini channel
offer superior heat transfer due to their large surface area to volume ratios and the heat
transfer rate is a function of the flow pattern, bubble velocity, gas void fraction. Present paper
discusses review of void fraction measurement techniques and developed void fraction image
sequence algorithm using image processing techniques for void fraction measurement in
mini/micro-channel
Keywords: image processing, mini-channel, two phase flow, void fraction, void fraction
image sequence algorithm
1. INTRODUCTION
Void fraction is vital property which can be used to differentiate two phase flow from
single phase flow. It is key property for determine other important two phase flow property
such as
• Two phase density
• Two phase viscosity
• Average velocity of two phase
• Pressure drop
• Heat transfer coefficient
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ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
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Void fraction defines many ways such as local void fraction is refer to that at a point,
Chordal void fraction is defined as fractional length of path through the channel occupied by
gas phase, cross –sectional void fraction is defined as fraction cross-section area of channel
occupied by gas phase and volumetric void fraction in which fraction volume of channel
occupied by gas phase. Application and limitation of different void fraction method shown in
Table 1
Fu et al [1] measured void fraction by image processing techniques by considering 1000
image recorded by high speed camera. Chung et al [3] measured void fraction by image
processing in 530 µm, 250 µm, 100 µm, and 50 µm diameter channels. The images were
assigned a void fraction of zero when liquid alone and one for gas alone. Triplett et al [4]
measured void fraction by analyzing photograph of different flow pattern such as bubbly
flow, slug flow but void fraction measurement for churn flow is difficult. Zhang et al [5]
measured void fraction by using neutron radiography and image processing techniques.
Saisorn et al [2] measured void fraction by image processing techniques in 0.53, 0.22,0,15
mm diameter channel and by quick closing valve in 4.5 mm diameter channel. Kariyasaki et
al [8] measured void fraction by constant electric current method in 1, 2.4, and 4.9mm.
Table 1 Void Fraction Measurement Techniques
Measurement techniques Application Limitation
Intrusive method
Quick closing valve Volumetric void fraction
measurement
Finite require for closing of the valve,
considerable time require for bringing
the system back to the steady state
between successive experiment
Conduction probe Local time averaged and
chordal void fraction
measurement
Limited to measure bubbly and slug
flow regime
Non-intrusive method
By pressure drop Volumetric void fraction
measurement
Friction pressure drop and
acceleration pressure drop neglected ,
manometer line filled by single phase
Radiation absorption and
scattering method
Chordal void fraction
measurement
Expensive ,difficult to handle high
energy radiation ,presence of metal
wall induce error
Laser beam method Chordal void fraction
measurement ,flow pattern
identification
Expensive
Photographic method Chordal ,cross-section void
fraction measurement
Error due to operation perform on
image
Impedance method Volumetric void fraction
,low cost ,suitable for
transition measurement

3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME
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2. VOID FRACTION IMAGE SEQUENCE ALGORITHM
Developed Image sequence algorithm is able to measures two phase flow void
fraction, number of bubble, bubble frequency, average slug length.
Pre-processing
1. Capture two phase video by high resolution digital camera in Fig.1
2. Captured video of two phase flow is converted to binary video in Fig.1
3. Find out number of frame required for selected bubble travel from entry to exit of
observed test section for decide image interval /frame interval
Figure. 1. Captured RGB image of two phase flow is converted to binary image
4. Binary video is saved as image sequence for input of void fraction image sequence
algorithm
Input parameters
1. Image sequence /image file destination
2. Total number of frame
3. Frame interval for analysis for analysis (avoid repetition of bubble in calculation mainly
for number of bubble in particular time and reduce calculation time )
Void fraction image sequence algorithm
1. Read image
Image matrix is M (I, J) (in Fig.2)
Where I is for number of row (I=1, 2, 3...................n), J is for number of column (J=1, 2,
3....................m).

4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME
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Figure. 2. Image matrix M (I, J)
2. Calculate row axis (X which middle horizontal axis ) as show in Fig.2
2
n
X =
3. Now image pixel value is read at row axis M(X,J)
Where if M(X, J) = 255 than bubble length counting start and stop at M(X, J) =0 (white
portion for gas phase in image has a pixel value 255 and black portion has a pixel value 0)
again same process is repeated when M(X, J) = 255. This process repeat in all image which
give total number of bubble (K=1, 2.............Z), bubble length (LBK) .
TNP (Total number of pixel) =number of image used for analysis * number of pixel
calculated on horizontal axis per image
Average bubble length =
Z
LB
Z
k
K∑=1
Where Z is Total number of bubble
Chordal void fraction =
TNP
LB
Z
k
K∑=1
Output of void fraction image sequence algorithm
1. Chordal void fraction
2. Bubble frequency
3. Number of bubble for particular time duration
4. Average bubble length
3. CASE STUDY
Output obtained by void fraction image sequence algorithm and void fraction
calculated by Armand correlation show in Table 2 and Fig.3

5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
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a b
c d
Figure .3. a) pixel time series ,b) void fraction ,c) bubble length (pixel) ,d) average bubble
length (mm)
Table 2 Input and Output of Void fraction image sequence algorithm
Input parameters Output parameters
Total number of image 600 Measured Void Fraction
(α)(Fig.3-b)
0.6233
gas volume flow rate (LPM) 0.16 Bubble length (mm) (Fig.3-d) 5.4745
Liquid volume flow rate (LPM) 0.05 Number of bubble( 167
Frame interval 28 Bubble frequency (number of
bubble/sec)
8.3002
Analysis time (second) 20 Void fraction by Armand
correlation ((α=0.83333*β) [6]
0.6348
β= Qg /(Qg + Ql)
=0.7619
4. CONCLUSION
Void fraction calculated by image processing techniques is easy, cheap and safe than
other techniques such as quick closing valve, Radiation absorption and scattering method,
laser beam. Developed void fraction image sequence algorithm using image processing
techniques calculate void fraction nearly accurate. Void fraction image sequence algorithm
also calculates bubble length, number of bubble, bubble frequency within less time.

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6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME
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