1 imaging techniques for flow and motion measurement lecture 6 lichuan gui university of mississippi...
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Imaging Techniques for Flow and Motion Measurement
Lecture 6
Lichuan Gui
University of Mississippi
2011
PIV Recording EvaluationPIV Recording Evaluation
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Evaluation methods Evaluation methods Frequently used evaluation methods
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Evaluation methods Evaluation methods Particle trajectory identification
PIV recording- Single frame- Single long time exposure - LID mode
- Film or digital recording Evaluation
- Read film recordings with a microscope system - Identify particle trajectories in digital recording y
x
22 xyS
t
SV
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Young’s fringes evaluation system
laser
PC
2D traversesystem
CCD camera frosted glass
Evaluation methods Evaluation methods Young’s fringes method
PIV recording- Positive film - Single frame- Double/multiple exposed- HID & LS mode
Young’s fringes system
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Evaluation methods Evaluation methods Particle image tracking
PIV recording- Minimum 2 frames- Single exposure - LID mode
- Film or digital recording Evaluation
- Identify particle images & determine position of each particle image center - Pairing particles in two frames (many algorithms)- Velocity determined by position difference of paired particles & t
t1 t2
2,
2,
2121212 tt
tyy
yxx
x
12
12,,tt
xxtyxVx
12
12,,tt
yytyxVy
o x
y
o x
y
(x2, y2)
(x1, y1)
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Evaluation methods Evaluation methods Particle image tracking algorithm
Nearest point method- Two frame- Distance between particle images >> particle image displacement
2nd frame1st fame 1st fame & 2nd frame
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Evaluation methods Evaluation methods Particle image tracking algorithm
Two frame particle tracking algorithms - Distance between particle images >> particle image displacement - Neighborhood particle images used to help pairing particles- Different algorithms, e.g. “Spring model” by Okamoto
2nd frame1st fame 1st fame & 2nd frame
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Evaluation methods Evaluation methods Particle image tracking algorithm
Multi frame particle tracking algorithms - Time history of particle images used to help pairing particles- Velocity variation small enough in several consecutive frames
1st fame 2nd fame 3rd fame 4th fame
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m
n
(m, n)
-SS
o
Auto-correlation
Cross-correlation
0.0
0.5
1.0
-15-10
-50
510
15
m
-10
0
10
n
(m,n)
Evaluation methods Evaluation methods Correlation-based interrogation
M
i
N
j
njmigjignm1 1
21 ,,,
t
myxV mmx
,
t
nyxV mmy
,
(m’,n’)
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Evaluation methods Evaluation methods Ensemble particle image pattern tracking
G k l G xM
i yN
j g i j
G k l G xM
i m yN
j n g i m j n
M m m
S m m
, , ,
, , ,
1 1
2 2
2 2
2 2
1st recording
2nd recording
Sample image pattern
Image pattern at (m,n)
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Evaluation methods Evaluation methods Ensemble particle image pattern tracking
1,2,1,0,12,1,0,,
1,2,1,0,12,1,0,,
NlMklkG
NlMklkG
SS
MM
MN dimensional vectors
SMnmD
,
Difference of the vectors
D(m, n) (%)
nm
-20-10
010
20
-20-10
010
20
0
20
40
60
80
100
0
20
40
60
80
100
-20-10
010
20-20-10
010
20
0
20
40
60
80
100
0
20
40
60
80
100
D(m, n) (%)
m n
Double exposure Single exposures
Image pattern difference as function of (m,n)
(m’,n’)
t
myxV mmx
,
t
nyxV mmy
,
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Evaluation with large window
Evaluation at identified particles
LID recordings with small interrogation window
Evaluation methods Evaluation methods Individual particle image pattern tracking
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1. Read EDPIV help manual pages:“PIV recording simulation settings”
2. Create a synthetic PIV recording pair of LID modeParticle number density: 1 / 32x32 pixelsRandom noise: intensity=0, mean value=804-roll-mill flow, Ax=500, Ay=500
In start window: menu choice “ File \ New image” and “Processing” button;
In “Image processing” window: menu “Tools \ Simulation settings \ Particle”menu “Tools \ Simulation settings \ Noise” menu “Tools \ Simulation settings \ Flow” menu “Tools \ One pair”
Press button “I” to switch imagesView overlapped image in “Evaluation window”
HomeworkHomework