Photonic Sensing and Characterisation of Multiphase Water Flows

Computational Sensing and Imaging

Sergio L. Carrasco-Ortiz, Eduardo Valero, Maria Morant and Roberto Llorente

Nanophotonics Technology Center, Universitat Politècnica de València, Camino de Vera s/n, Valencia, Spain

Keywords: Computational Sensing, Photonic Sensor, Imaging, CMOS, Multiphase Water Flow, Cavitation, Air Fraction,

Bubble Speed.

Abstract: This paper presents and demonstrates experimentally a photonic sensor based on laser excitation and CMOS

array detection of light interactions in a bubbling water column. Bubbling water columns are multiphase water

flows, which can be produced by cavitation effect in screws and pump-jets, or by water-air mixing in the dam

intakes and spillways in hydroelectric energy generation plants. The proposed optical sensor comprises a

CMOS array with a polymeric tuneable optical lens sampling the area illuminated by a diode laser operating

at 532 nm. By computational imaging, the sensor is able to measure the size, shape and speed of the bubbles

inside the water column and the corresponding bubble concentration. The developed laser-assisted sensor is

demonstrated experimentally in the laboratory with a water column at 0.05 MPa with good results employing

Optical Flow, SIFT and SURF computational methods.

1 INTRODUCTION

Bubble detection and multiphase flow monitoring are

important in terms of efficiency and safety in many

industrial and medical fields. A multiphase flow is

defined as a simultaneous flux of two phases or

components with an explicit interface between them

(Iyer, et al., 2013). A basic example of a two-phase

flow usually comprises a gas and a liquid, being the

most common bubble flows (Bian, et al., 2011).

Bubble flows play an important role in the study of

mechanism in gas/liquid two-phase flows where the

bubbles behaviour are a key element when

monitoring water columns in industrial applications

(Bian, et al., 2011). Some application examples of

two-phase flows are air bubbles found in

extracorporeal blood circuits of haemodialysis (Li, et

al., 2015) or in industrial bubble column reactors

(Iyer, et al., 2013). In the first example, in medical

applications, if a certain amount of air is pumped into

the patient, it may cause life-threatening coagulation

and haematomas (Li, et al., 2015). In the second

example, it is extremely important to know the

properties of the bubbles including its size, shape and

velocity for the design and function monitoring of

industrial reactors (Iyer, et al., 2013). In both cases,

image processing of bubbles has been proposed to

calculate the number of bubbles and cumulative

volume (Jonsson, 2014) (Muddle, et al., 1994).

A large number of bubbles in a flow can indicate

the presence of cavitation in hydraulic applications.

Cavitation is due to the pressure decrease generated

by an excessive blade angular velocity, which in turn

generates bubbles that explode in the blade surface

(Brennen, 2005). Cavitation spaces are usually

involved in highly turbulent flows as it happens in

vortex emissions produced in the torch effect

represented in Figure 1(a). Bubble explosion causes

erosion on the surface of dam spillways, reducing its

useful life (ETH Zurich, Laboratory of Hydraulics,

Hydrology and Glaciology, s.f.) as it is depicted in

Figure 1(b).

Figure 1: (a) Torch effect in a screw (b) Cavitation damage

on spillway profile of Nagarjunasagar Dam.

(a) (b)

Cavitation

damages

Carrasco-Ortiz, S., Valero, E., Morant, M. and Llorente, R.

Photonic Sensing and Characterisation of Multiphase Water Flows - Computational Sensing and Imaging.

DOI: 10.5220/0006553100550062

In Proceedings of the 6th International Conference on Photonics, Optics and Laser Technology (PHOTOPTICS 2018), pages 55-62

ISBN: 978-989-758-286-8

Furthermore, in devices such as pumps, hydraulic

turbines, pump-jets and screws in the naval area

(installed on ships and submarines), cavitation could

cause noise, vibration, permanent screw damage and

propulsion efficiency losses (Buckland, et al., 2013)

(Shan-Yong, et al., 2013). In this paper, we focus on

the detection (sensing) and characterization of

bubbles in a multiphase flow water column. A

potentially high bubble-density is considered in order

to address scenarios including cavitation in marine

screws, and water-air mix in hydroelectric energy

generation structures.

Due to the compressibility of bubble flows, air is

capable of absorbing, until a certain point, the impact

of bubble shocks in cavitation processes (Peterka,

1983). However, if the bubble flow is not monitored

properly it can lead to the above mentioned damages

in the structure as represented in the example depicted

in Figure 1(b). The aeration of the flow changes its

dissipative behaviour –as provides less friction with

the contours and increased vorticity– (Chanson,

1994), which converts the concentration of air in a

variable of crucial interest. The level of bubble

concentration modifies the macroscopic properties of

the flow, increasing its draft and decreasing its

velocity and gives rise to different momentum

distributions relative to those of the exclusively liquid

flow. In addition, significant differences have been

found in the behaviour of the turbulence due to the

effect of the dispersed phase (air in this case) on the

carrier phase (water).

The most common solution to cavitation in

hydraulic structures is the design of hydro-dynamic

profiles that avoid big pressure gradients (Edel, 1977)

(Volkart & Rutschmann, 1984). However, as it is not

always possible and cavitation may occur at relatively

slow speeds, other sort of solutions are necessary. The

employment of coating materials have been also

proposed in the past to improve the resistant

properties (Cheng, et al., 1990). Accoustic detection

of the cavitation bubbles implosion has been also

evaluated in the literature (Duraiswami, et al., 1998),

where incipient cavitation is heard as intermittent

crackling while severe cavitation becomes a steady

hiss increasing in volume (Chudina, 2003) (Karassik

& McGuire, 1997). However, due to the chaotic rise

of the bubbles along the flow in both number and size,

it is not possible to predict the emitted noise to get a

full characterization (Chudina, 2003). Other

techniques for characterizing bubbles are based on

energy measurement –employing charge-coupled

devices (CCD) or hydrophones (Brujan, et al., 2002)–

or electric impedance measurement (O'hern, et al.,

1995), or using probes to measure if there is air or

water in contact with the probe (Chanson, 1997). All

these methods are invasive and they will modify the

hydrodynamic multiphase flow properties, such as

bubble shape and flow. To avoid this, we propose a

photonic sensor based on a camera and a diode laser

detection that does not interfere with the flow. The

diode laser is employed to excite the bubbling water

column in order to increase the contrast of a high-

density number of bubbles in the flow. This non-

invasive method enables monitoring the flow

behaviour taking high-speed images of the flow. With

the proposed post-processing algorithms, from these

images we can calculate the bubble speed and extract

the size (bubble concentration) and shape of the

bubbles in terms of equivalent area and diameter,

major half-axis, minor half-axis and perimeter.

2 LASER-CMOS SENSOR

ARCHITECTURE

2.1 Photonic Subsystems

Figure 2 represents the conceptual scheme of the

laboratory setup including the block diagram of the

main components comprising the proposed photonic

sensor for the detection and characterization of

bubbles in a multiphase flow. At the laboratory the

sensor is applied to a bubbly water column generated

-inside a transparent tank- with a cylindrical diffusor

with main characteristics of diameter 

diff

= 12.10

mm and length L

diff

= 26.50 mm. This cylindrical

diffusor is connected with a flexible plastic tube (

= 5.10 mm, 

out

= 8.10 mm) to a void pump

configured with 0.05 MPa.

The laser-assisted sensor is based on a camera that

comprises a complementary metal-oxide-

semiconductor (CMOS) sensor, a fixed lens and a

tunable lens controlled remotely with a driver. The

illumination of the bubbles is achieved with a laser

diode beam dispersed by a negative lens.

Figure 2: Conceptual scheme of the laser-assisted CMOS

sensor developed at the laboratory.

Wiring: Power-supply

USB 2.0 Ethernet (PoE)

Processing

algorithms

Negative

lens

Laser

Sensing area

CMOS

Computer

Power source

Driver

Void

pump

Diffusor

Water

tank

Fixed lens +

tunable lens

PHOTOPTICS 2018 - 6th International Conference on Photonics, Optics and Laser Technology

Figure 3: Tunable lens and fixed lens system’s calibration

curve for the equivalence of mm/px vs. dioptres.

The main functions of each opto-electronic

component depicted in Figure 2 are:

▪ Monochrome CMOS sensor, responsible for

the acquisition of data: A camera with CMOS

sensor was selected because it is the fastest and

safest way to capture optical information in

form of images. The sensor is configured

remotely in terms of resolution, exposure time,

gain, frames per second (fps) and recording

time. In these experiments, we used a sensor

with a maximum resolution of 1280×1024

pixels (px).

▪ Fixed lens with a focal length of f = 25 mm,

responsible for focusing the image together

with a tunable lens.

▪ Tunable lens, responsible for adjusting the lens

system: At the fixed lens outer face a tunable

lens is used. This lens is capable of varying its

dioptre number between −4 and 5 dpt to focus

in different depth planes.

▪ The lens system is controlled by algorithms that

set the tunable lens’ dpt. This is necessary for

the calculation of real lengths. The algorithms

calculate the equivalence between the image

size in terms of px and the real length in mm.

Figure 3 shows the calibration curve of the lens

system in terms of mm/px vs. dioptres. In this

work the refractive surface between the two

media (air and water) is plane, and by the

Snell’s law, it is obtained an increase factor m

= 1; meaning that there is no sizing increase due

to the refraction. Applying in Figure 3 a

polynomial adjustment of 8

degree, the

equivalence is given by the equation (1) being

their coefficients those written in Table 1:

  











(1)

Table 1: Equation (1) coefficients.

0.0405

-8.007e-3

2.618e-3

-1.098e-3

-7.327e-5

1.218e-4

2.537e-6

-8.161e-6

9.07e-7

The equivalence defined in equation (1) is

included in the processing algorithms to

calculate the equivalence between px and

millimetres for each measurement. Without

this calibration, we would be unable to

transform the pixels obtained in the post-

processing step into millimetres.

▪ The laser diode, responsible for illuminating

the bubbles and increase the contrast and

shadowing of the borders. A tunable output

power laser with up to 80 mW working at a

wavelength of λ = 532 nm is used. The

concrete wavelength was selected to match the

quantum efficiency of the CMOS sensor.

Figure 4 shows the efficiency of the CMOS

sensor used for different wavelengths. The

quantic efficiency of the CMOS sensor is over

68% for a λ = 532 nm. The geometrical laser

ray has a roundness Ο > 90 %, divergence

θ > 1.2 mrad and initial diameter 

= 1.5 mm.

The output power of the laser can be configured

depending on the voltage/intensity provided

ranging from 2.3 to 2.9 V and up to 350 mA. In

these experiments, we waited ten seconds after

switching on the laser in order to ensure the

output power.

Figure 4: CMOS quantum efficiency.

Diopters (m

-1

)

Equivalence

(mm/px)

-4 -3

-2 -1 0

3 4 5

0.05

0.1

0.15

0.2

0.25

Photonic Sensing and Characterisation of Multiphase Water Flows - Computational Sensing and Imaging

▪ Negative (divergent) lens, responsible for

enlarging the laser beam. In order to avoid the

appearance of sparkles in the image produced

by the CMOS sensor it is necessary to diverge

the laser beam. To achieve it, a negative

NBK-7 glass lens with plane-concave shape is

used with a focal distance f = –75 mm, diameter

of  = 50.8 mm.

2.2 Data Acquisition and Digital Signal

Processing

Figure 5 depicts the main steps followed for the data

acquisition and processing of the images to full

characterize the bubble flow.. As a first step, the

sensor is configured remotely via C commands

including the CMOS configuration, the output power

of the laser diode and the lens focus. The CMOS

sensor is configured in:

▪ Gain correction, employed to homogenise the

CMOS sensibility. The dead pixel correction

allows the software to fix the turned off pixels.

This correction consists in storing the average

value of the adjacent pixels in the black pixel.

Furthermore, it is useful to post-process the

images as improves the detection area, but at

the same time, may cause errors in the image’s

adaptive histogram equalisation.

▪ Exposure time, also known as shutter speed,

adjusts the amount of incident light in order to

avoid blurred images.

▪ Recording speed: Readout speed tells how

many frames-per-second the camera can

capture. In these experiments, after capturing

the images at a speed of 100 fps (i.e. each frame

is captured at 1/100 seconds or 10 ms), the

speed is immediately set to 1110 fps (i.e.

1/1110 or 0.9 ms) reducing the region of

interest (RoI). This greatly improves the result

of bubble speed estimation.

Once the image sequence is recorded, adjusting

the focus system, the equivalence value is obtained

with the mm/px conversion depicted previously in

Figure 3. In the image-processing step, the

recognition of any object is carried out by the

discrimination of what is seen regarding the ideal

concept of the object to be identified. To achieve that,

it is necessary to transform the images acquired in

greyscale –pixels whose values go from 0 to 1, being

0 a black pixel and being 1 a white pixel– into images

in which there are just black and white pixels. This

process is known as binarization as only 0 or 1 values

remain in the image.

Figure 5: Data acquisition and processing diagram.

To minimize the interclass variance between the

white and black pixels, it is necessary to have some

ambient lighting. An accurate conversion from a grey

image to a binarized image is essential for the

application of region detection algorithms. The laser

diode illumination provided by our optical sensor

increases the contrast of the bubbles in the flow and

enhances the binarization process.

In order to carry out the identification of

circular/elliptical forms some extra filtering and

processing should be implemented to the images, as

depicted in Figure 6.

PHOTOPTICS 2018 - 6th International Conference on Photonics, Optics and Laser Technology

Figure 6: Evolution of an image captured from the water

column after digital signal processing.

The filters and processes applied to the images are

integrated in C/Fortran language. Figure 6 shows the

different steps applied to the images captured from

the water column including:

▪ Wiener filter (Bankman, 2008): provides a

statistical computation from a degraded image.

This filter is designed considering that the

image and its noise are random processes.

▪ Image erosion: This function erodes the binary

image applying a structuring element object in

order to erase the remaining noise after the

Wiener filter.

▪ Image dilation: Due to the previous image

erosion, in some cases it is necessary to recover

lost white pixels as it can be observed in Figure

6. In order to do so, a dilatation of the image is

implemented employing the same structuring

element object applied previously in the

erosion step.

▪ Bubble filling: In order to be able to employ

properly the region detection algorithms, we

explore the binary image as a matrix composed

by ones and zeros. The filling algorithms locate

those 0 pixels in the matrix surrounded by ones

and replaces the 0 within by 1, filling the

bubble contour.

▪ Anomalous item dismiss: In the sensing area,

two or more bubble may appear too close each

other, providing mistakes in the statistical

analysis of the bubble shape. To avoid this, data

with 1.5 times higher the morphological bubble

parameters typical deviation, was debugged

from the analysis.

After the statistical analysis of the detected

bubbles, the bubbles’ speed is calculated. In this

topic, we performed an exhaustive state-of-the-art

study of the methods available for the calculation of

the velocity profile of moving objects. In particular,

in these experiments we compare three evaluation

methods:

• Optical Flow, based on the distribution of

apparent velocities of brightness movement

patterns. Pyramidal optical flow arises from the

relative movement of objects and the observer,

so it does not take into account the movement

of the observer (Horn & Schunck, 1981).

• Scale-Invariant Feature Transform (SIFT)

(Lowe, 2004), which computing steps are

scale-space extrema detection, key-point

localization, orientation assignment and key-

point descriptor. This method identifies key-

points in a set of images in order to compare

later every new image with an older one to find

matching features based on the Euclidean

distance of their feature vectors.

• Speed-Up Robust Features (SURF) (Bay, et al.,

2008): This method searches for patterns in the

recognized points in discrete images classified

in three types, i.e. points of interest, descriptors

and markers.

Once the bubbles’ speed is calculated, the

processing algorithms represent the results in form of

histograms and saves the binarized video for further

evaluation.

3 EXPERIMENTAL BUBBLE

CHARACTERIZATION

RESULTS

The validation of the developed processing

algorithms using the proposed sensor is performed at

the laboratory with bubbly water columns within a

transparent tank as depicted previously in Figure 2.

In these experiments, the void pump was set to 0.05

MPa to generate a turbulent bubbly flow with similar

bubble shapes and sizes.

3.1 Air Bubble Concentration

Calculation

The air bubble concentration, i.e. the air in the form

of bubbles in the sensed area, is carried out

integrating all the white regions detected after the

binarization of the image. Figure 7(a) shows the raw

image acquired by the sensor and Figure 7(b) shows

the binarized image result for bubble concentration

calculation. In the processed representation, the

background was coloured in green to enhance the

contrast of the results.

As it can be observed in Figure 7(b), for the

concentration calculation we do not dismiss any

anomalous form, as the clusters of bubbles are also

taken into account for the total concentration in the

area. In these experiments, the bubble concentration

is evaluated in every frame acquired (i.e. 500 frames).

Original

Image

Wiener

Filter

Binary

Image

Erosion Dilation

Bubble

Filling

Photonic Sensing and Characterisation of Multiphase Water Flows - Computational Sensing and Imaging

Figure 7: (a) Original frame vs. (b) binarized frame sample

for the calculation of bubble concentration.

Figure 8 shows the experimental mean of bubble

concentration measured in an area of 53.67 cm

(81.92 mm × 65.52 mm), during 5 s for 500 images.

Figure 8: Experimental results of bubble concentration

analysis. Horizontal line indicated the mean of bubble

concentration in the detected area of 21.5%.

In this example, the measured bubble

concentration mean is 21.5%, ranging from 19.51%

to 23.49% in a sensed period of 5 seconds. Figure 8

represent in the red marks the bubble concentration

for each frame and in blue lines the slope between

sequential frames.

3.2 Bubbles’ Shape

As it was described in detail in Section 2.2, applying

region detection algorithms to the binarized images,

we were able to quantify shape parameters in the

white regions –filled bubbles–. From the filtered data,

region detection algorithms are able to calculate the

bubbles’ shape parameters, e.g. equivalent area and

both half-axes. The typical deviation (σ) for each

parameter is calculated to debug data what surpass 1.5

times its σ. This debugging is intended to remove the

bubbles that, being too close each other, appeared as

one bigger bubble after binarization. Figure 9

compares the acquired data with the processed image

after binarization, filtering and debugging. The

processing algorithms are applied to the same original

raw data as in Figure 7 so, comparing both figures,

we can observe the debugging of bubble clusters and

anomalous items for the analysis of bubbles’ shapes.

Figure 9: (a) Original frame vs. (b) processed frame sample

for the statistical analysis of bubbles’ shapes.

(a)

(b)

Figure 10: Histogram of the experimental results for

(a) minor and (b) major half-axis estimation (raw vs.

debugged data) expressed as parts per unit.

Figure 10, shows the experimental results for

minor and major half-axis. The mode of the minor

and major half-axis is 1.67 mm and 2.04,

respectively, as observed in Figure 10(a) and (b). It is

clearly observed that the debugging of anomalous

items eliminates clusters of half-axis bigger than

2.63 mm (minor half-axis) and larger than 4.44 mm

(major half-axis).

PHOTOPTICS 2018 - 6th International Conference on Photonics, Optics and Laser Technology

Figure 11: Histogram of the experimental results for

equivalent area estimation (raw vs. debugged data)

expressed as parts per unit.

Figure 11 shows the statistical results for

equivalent area of the bubbles present in the water

column. The mode of the equivalent area is 0.66 mm

being the maximum equivalent area found by the

algorithms after the debugging of anomalous items

7.55 mm

By means of statistical analysis of data associated

with bubbles such as mode, mean (), minimum

(Min), maximum (Max) and σ; the measurement of

the fraction of air and bubble shape in critical

multiphase flows in electricity generation facilities is

carried out. Table 2 summarizes the statistical

analysis results obtained with debugged data. From

the analysis of the experimental bubbly water

column, the minor and major half-axis has a mean of

1.64 and 2.57 mm, respectively. The mean equivalent

area of the bubbles is of 3.19 mm

with a deviation of

σ = 1.78. This deviation appears due to the non-

uniform porous diameter of the diffusor that generates

bubbles with different sizes as expected in a real-case

hydraulic scenario.

Table 2: Statistical analysis with debugged data.

Parameter

Mode



Min

Max

Minor half-

axis (mm)

1.67

1.64

0.78

2.63

0.47

Major half-

axis (mm)

2.04

2.57

1.10

4.44

0.85

Eq. area

(mm

)

0.66

3.19

0.64

7.55

1.78

3.3 Bubbles’ Speed Calculation

Figure 12 represents the 3D histograms of the bubble

speed distribution in bubbly water column calculated

with different estimation methods. The mean and the

mode of speed results represented in Figure 12 are

summarised in Table 3. It can be observed that all the

speed estimation algorithms (Optical Flow, SIFT and

SURF) have a mode of 0.4 m/s. The SIFT estimation

has a mean slightly higher than Optical Flow and

SURF methods but only in a 5%.

Figure 12: Bubbles’ speed estimation calculated with

different algorithms expressed as parts per unit.

Table 3: Bubbles’ speed statistical results for different

estimation methods.

Method

Mean

Mode

Optical Flow

0.40 m/s

SIFT

0.42 m/s

0.40 m/s

SURF

0.40 m/s

4 CONCLUSIONS

We propose and demonstrate experimentally a

photonic sensor comprising a laser diode and a

CMOS array with tunable optic lens designed to

calculate the size and speed of the bubbles present in

a bubbly water column. With this information, it is

possible to increase efficiency and security in power

generation plants while ensuring a longer duration of

the usefulness not only of the structure but also of the

hydraulic components, such as turbines and screws.

In this work, we presented proposed laser-assisted

CMOS sensor and the associated algorithms to

process the data. The optical sensor comprises a

CMOS sensor that captures high-speed frames of the

bubbles illuminated with a laser diode. A laser diode

emitting in λ = 532 nm is selected to optimize the

CMOS sensing performance.

Experimental data gathered indicates that

bubble’s shape depends on the diffusor porous

dimensions. In these validation examples the minor

and major half-axis of the bubbles are estimated to be

1.64 and 2.57 mm, respectively. The mean equivalent

area of the bubbles is of 3.19 mm

with a deviation of

Photonic Sensing and Characterisation of Multiphase Water Flows - Computational Sensing and Imaging

σ = 1.78. Such typical deviation appears due to the

diffusor porous diameter is not uniform; generating

bubbles with different sizes emulating a real-case

hydraulic scenario. The experimental results point out

that a correct binarization is necessary to calculate the

air concentration in the sensed area. For this reason,

the laser-assisted sensor increases the contrast and

shape contouring of the bubbles to enhance the

processing efficiency.

ACKNOWLEDGEMENTS

This research work was supported in part by Spain

National Plan MINECO/FEDER UE RTC-2014-

2232-3 HIDRASENSE and TEC2015-70858-C2-1-R

XCORE projects. BIOFRACTIVE project with IIS

La Fe is also acknowledged. M. Morant work was

partly supported by UPV postdoc PAID-10-16

program.

REFERENCES

Bankman, I., 2008. Handbook of Medical Image

Processing and Analysis. s.l.:Academic Press.

Bay, H., Ess, A., Tuylaars, T. & Van Gool, L., 2008. Speed-

Up Robust features (SURF). Computer Vision and

Image Understanding, Junio, 110(3), pp. 346-359.

Bian, Y., Dong, F. & Wang, H., 2011. Reconstruction of

Rising Bubble with Digital Image Processing Method.

Instrumentation and Measurement Technology

Conference (I2MTC).

Brennen, C. E., 2005. Fundamentals of Multiphase Flows.

Pasadena(California): Cambridge University Press.

Brujan, E. A., Keen, G. S., Vogel, A. & Blake, J. R., 2002.

The final stage of the collapse of a cavitation bubble

close to a rigid boundary. Physics of Fluids, 14(1), pp.

85-92.

Buckland, H. C., Masters, I., Orme, J. A. C. & Baker, T.,

2013. Cavitation inception and simulation in blade

element momentum theory for modelling tidal stream

turbine. Part A: Journal of Power and Energy, Junio,

227(4), pp. 479-485.

Chanson, H., 1994. Drag reduction in open channel.

Journal of Hydraulic Research, 32(1), pp. 87-101.

Chanson, H., 1997. Air bubble entrainment in open

channels. Flow structure and bubble size distributions.

Intl Jl of Multiphase Flow, 23(1), pp. 193-203.

Cheng, C., Webster, C. & Wong, J., 1990. Cavitation-

resistant coatings for concrete hydraulic structures. ACI

Materials Journal, 87(6), pp. 594-601.

Chudina, M., 2003. Noise as an Indicator of Cavitation in a

Centrifugal Pump. Acoustical Physics, 49(4), pp. 463-

474.

Duraiswami, R., Prabhukumar, S. & Chahine, G. L., 1998.

Bubble counting using an inverse acoustic scattering

method. The Journal of the Acoustical Society of

America, 104(5), pp. 2699-2717.

Edel, Y. U., 1977. Guide to protecting spillway structures

from cavitation, Power Technology and

Engineeringformerly Hydrotechnical Construction).

Springer, 11(9), pp. 948-949.

ETH Zurich, Laboratory of Hydraulics, Hydrology and

Glaciology, n.d. Air detrainment of high-speed waters

flows. [Online] Available at: http:\\www.vwa.ethz.ch/

people/hy/archive/hy_151_air_detrainment [Accessed

2017 06 12].

Horn, B. K. P. & Schunck, B. G., 1981. Determining optical

flow. Artificial Intelligence, 17(1-3), pp. 185-203.

Iyer, K., Shridharani, S., Arunkumar, S. & Venkatesan, M.,

2013. Application of Image processing for a Bubble

Column Reactor. Computational Intelligence and

Computing Research (ICCIC).

Jonsson, P., 2014. Evaluation of air micro bubbles in

dialysis sustems in vitro. American Journal of Kidney

Diseases, 63(5).

Karassik, I. & McGuire, T., 1997. Centrifugal Pumps. 2nd

ed. ed. New York: Pergamon.

Li, N., Jia, L. & Zhang, P., 2015. Detection and volume

estimation of bubbles in blood circuit of hemodialysis

by morphological image processing. Cybernetics and

Intelligent Systems (CIS) and IEEE Conference on

Robotics, Automation and Mechatronics (RAM).

Lowe, D. G., 2004. Distinctive Image Feature from Scale-

Invariant Keypoints. International Journal of

Computer Vision, Noviembre, 60(2), pp. 91-110.

Muddle, R. F., Schulte, H. B. M. & Vand den Akker, H. E.

A., 1994. Analysis of a bubbling 2-D gas-fluidized bed

using image processing. Powder Technology, 81(2), pp.

149-159.

O'hern, T. et al., 1995. Development of an electrical

impedance tomography system for an air-water vertical

water-column. Forum on Measurement techniques in

Multiphase Flowa ASME, pp. 1-9.

Peterka, A. J., 1983. The effect of entrained air on cavitation

pitting. Proceedings of Minnesorta International

Hydraulic Convention, pp. 507-518.

Shan-Yong, L., Jiang-An, W. & Si-Guang et al., Z., 2013.

Laser detection method of shi`p wake bubbles based on

multiple scattering intensity and polarization

characteristics.. Acta Phys. Sin, 62(6)(060704).

Volkart, P. & Rutschmann, P., 1984. Rapid flow in spillway

chutes with and without deflectors. A model-prototype

comparison. Proceedings of the Symposium on Scale

Effects in Modeling Hydrualic Research, pp. 3-6.

PHOTOPTICS 2018 - 6th International Conference on Photonics, Optics and Laser Technology