Limited particle image velocimetry (PIV) methods were used in field due to three main difficulties: 1. On-site computing device is needed; 2. An observing station is required for cameras; 3. The locating and camera calibration are complex. To overcome these problems, we used four parallel laser pointers as ground reference points and a smartphone as a computing core for calculating and demonstrating the flow field of the river. The research verified and showed the feasibility of the device. In conclusion, we developed a portable, affordable and easy-operation flow field measuring device.

1. Abstract of the 9th APRU Research Symposium
1 Introduction
1.1 The potential of on-site PIV
To measure the strength of flooding is important for the
hazard warning and prevention. In the past, the flow data
is usually obtained by the rating-curve method which may
not be precise during the high flow. For getting more
precise flow discharge data, flow velocity is an essential
piece of information (Lin, 2011). However, traditional
velocity measuring is difficult and dangerous for workers
during a flooding event. Hence, the non-intrusive
velocimetries are getting more and more popular.
1.2 Literature review
In the literatures, continuous-wave radar, pulsed
radar-doppler and particle image velocimetry (PIV)
(Adrian, 2005) are common non-intrusive velocity
measuring tools (Lee, 2003). In this research, we focus on
the PIV method because it is capable of measuring the
planar velocity data. Furthermore, PIV has a great
potential for field applications due to the fast
development of camera technology and computational
capability.
Theoretically, PIV uses the cross-correlation analysis
between two consecutive images on the same location
with a time delay to get the flow velocity field. Fujita et al.
(1998) developed a large-scale PIV(LSPIV), which
measures the surface flow velocity on the river for the
200 meter square area, and the error is less than 3.5%.
Kim et al. (2008) used LSPIV with a well-equipped
vehicle in the field to extend the mobility.
1.3 Challenges of on-site PIV
However, there are still some difficulties and
complexities in the traditional PIV methods. First, the
camera and computer are needed for the flow recording
and image processing in the field. The building-up of a
monitoring station may be costly. Second, for the image
ortho-rectification, surveyors need to conduct a field
survey to find out the ground reference points for
determining the coefficients of transforming equations of
the camera. But, a field survey may be difficult and
time-consuming.
1.4 The present research work
In this study, incorporated with the laser positioning
technique and mobile phone technology, we developed a
portable PIV device which can overcome the
aforementioned difficulties.
2 Method
2.1 Laser positioning method
A laser projection device is attached to the mobile
phone, which can project four laser points on the flow
surface as the reference scale. Based on the rotation angle
of mobile phone camera, the coordinates of the projected
ABSTRACT: Limited particle image velocimetry (PIV) methods were used in field due to three main
difficulties: 1. On-site computing device is needed; 2. An observing station is required for cameras;
3. The locating and camera calibration are complex. To overcome these problems, we used four
parallel laser pointers as ground reference points and a smartphone as a computing core for
calculating and demonstrating the flow field of the river. The research verified and showed the
feasibility of the device. In conclusion, we developed a portable, affordable and easy-operation flow
field measuring device.
Development of A Portable PIV for On-site Flow Field
Yao-Yu, Yang
Department of Civil Engineering, National Taiwan University, Taiwan
Franco, Lin
National Center for High-performance Computing, National Applied Research Laboratories, Taiwan
Min-Cheng, Wen
Department of Civil Engineering, National Taiwan University, Taiwan
Wen-Yi, Chang
National Center for High-performance Computing, National Applied Research Laboratories, Taiwan
Shih-Chung, Kang
Department of Civil Engineering, National Taiwan University, Taiwan

2. Abstract of the 9th APRU Research Symposium
points can be calculated as follows:
   
   
   
   













cos/,0,
tantancos/,cos/,
tantan,cos/,
0,0,
Hyx
WHWyx
WWyx
yx
D
C
B
A
(1)
where W and H are the width and height of the laser
projection device, respectively;  and  are the yaw angle
and pitch angle of the camera in this situation.
In the image preprocessing, for the laser point
recognition and tracking, a red-dot recognition algorithm
is used to obtain the laser point locations in the image
coordinate system.
2.2 Image ortho-rectification
For assuming that 4 laser points fall on the same plane,
Equations (2) and (3) (Rafael et. al., 2008) can be used to
produce the ortho-images in the real coordinate system, in
which the coefficients are determined by the known
coordinates of four laser points in the two systems.
𝑥′
= 𝑐1 𝑥 + 𝑐2 𝑦 + 𝑐3 𝑥𝑦 + 𝑐4 (2)
𝑦′
= 𝑐5 𝑥 + 𝑐6 𝑦 + 𝑐7 𝑥𝑦 + 𝑐8 (3)
where x and y are in the real coordinate system; x' and y'
are in the image coordinate system.
2.3 PIV algorithm
The traditional PIV algorithm (Adrian, 2005) is used to
calculate the surface velocity on the flow, which analyzes
the cross-correlation from two consecutive images with a
time difference. A fixed-size window (Interrogation Area,
IA) is selected in the first image, and the corresponding
window in the second image is then chosen for
calculating the cross-correlation coefficient by the
following relation:
 

M
x
N
y
fg nymxgyxfnm
1 1
),(),(),( (4)
Figure 1. The portable PIV device
in which f(x,y) is the pixel value at time t, and g(x,y) is the
pixel value at time t+Δt. M and N are the size of IA
windows, and m and n are the movement index. In the
present study, the image size is 1280×960, and IA size is
chosen as 32×32. After obtaining the maximum value
fg(m' ,n') at time t, the movement of IA, m' and n', means
the displacement of flow particles.
2.4 mobile phone implementation
Because common algorithms were developed in C++
environment, we implemented C++ based algorithm on
Android platform via Java native interface. The algorithm
was developed using OpenCV C++ library.
3 Result and Conclusion
In verification, we showed the sequential simulated
vortex and boundary layer flow on the monitor, and
calculated the direction of the flow by mobile application
(Fig. 2). The flow field can be accurately demonstrated on
the screen.
This device measures the surface flow velocity. By
applying flow rate model, we can measure the flow rate
of the river more reliably.
Figure 2. The flow field calculated by mobile
4 References
Adrian, R. J. (2005), Twenty years of particle image
velocimetry, Experiments in Fluids 39: 159-169.
Fujita, I., et al. (1998), Large-scale particle image velocimetry
for flow analysis in hydraulic engineering applications.
Journal of Hydraulic Research 36(3): 397-414.
Kim, Y., et al. (2008), Stream discharge using mobile
large-scale particle image velocimetry: A proof of concept.
Water Resources Research 44(9): W09502.
Lee, Ming.-Ching. (2003), Development of Non-contact
Methods for Water Surface Velocity and River Discharge
Measurements, Department of Hydraulic and Ocean
Engineering, National Cheng Kung University. Doctor of
Philosophy.
Rafael C. Gonzalez, R. E. W. (2008), An Adapted Version:
Digital Image Processing, Pearson Education Taiwan Ltd.
Lin, Ying-Chih, Chu, Mu-Shou., Chan Hsun-Chuan, Kao,
Shen-Ching, Leu, Jan-Mou (2011), Estimation of High
Discharge Using Measured Surface Velocity, Journal of
Chinese Soil and Water Conservation 42(1): 23-36.