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Imaging Techniques for Flow and Motion Measurement
Lecture 17
Micro-scale Velocimetry
Lichuan Gui
University of Mississippi
2011
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Micro-scale Fluids
• Used to carry heat around a circuit
- on-chip IC cooling, micro heat pipes
• Used to create forces
- micro thrusters
• Used to transmit powers
- micro pumps and turbines
• Used to transport materials
- distribute cells, molecules to sensors
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Need for Microfluidic Diagnostics
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Even though Re«1, flows still complicated
Large surface roughness
Imprecise boundary conditions
Two-phase, non-Newtonian fluids
Coupled hydrodynamics and
electrodynamics
• Non-continuum effects
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Full-field Microfluidic Velocimetry
• X-ray microimaging
Lanzillotto, et al., Proc. ASME, 1996, AD52, 789-795.
• Molecular-Tagging Velocimetry (MTV)
Paul, et al., Anal. Chem., 1998, 70, 2459-2467.
• Micro-Particle Image Velocimetry (MPIV)
Santiago, et al., Exp. Fluids, 1998, 25(4), 316-319.
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X-ray Microimaging
• Positives
Can image inside normally
opaque devices
• Negatives
low resolution ~20-40mm
depth averaged (2-D)
requires slurry to scatter x-rays
X-rays
Phosphor screen
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Molecular-Tagging Velocimetry
• Positives
minimally intrusive
better with electricallydriven flows
• Negatives
low resolution ~20-40mm
depth averaged (2-D)
greatly affected by diffusion
UV laser
Blue laser
Blue laser
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Micro-Particle Image Velocimetry
• Positives
high resolution ~1 mm
small depth average ~2-10 mm
minimally intrusive
• Negatives
requires seeding flow
particles can become charged
Pulse laser
CCD
microscope
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Typical MPIV System
Micro Device
MCROFLUIDIC DEVICE
Flow out
Flow in
CCD CAMERA
Glass
cover
MICROSCOPE
Focal Plane
BEAM EXPANDER
Flood Illumination
Microscope
Beam
Expander
Nd:YAG LASER
Micro-Fluidics Lab
Purdue University
Epi-fluorescent
Prism / Filter Cube
Nd:YAG Laser
Micro-PIV image pair
l=532 nm
l = 610 nm
CCD Camera
(1280x1024 pixels)
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Typical MPIV System
– Micro-scale resolution
• Dimension of investigated flow structure in region
of 1 mm – 1 mm
• Nano-scale particles used
– Volume (flood) illumination
• Micro-scale light sheet not available
• 2D measurement in focus plane of microscope objective
– Fluorescent technique
• Fluorescent particles
e.g. excited by l=532nm and emitting l=610nm
• Low-pass or band-pass optical filters used to reduce noises
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Typical MPIV System
– Typical problems
• Low signal to noise ratio because of
– Low light intensity of nano-scale particles
– Low light intensity of back scattering imaging
– Illuminated particles out of focus plane
• Low particle image concentration
• Brownian motion of nano-scale particles
• Diffraction of nano-scale particles
• Large particle image displacement because of high
magnification and time interval limit
• etc
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Example: Microcantilever Driven Flow
250
200
150
100
2 mm
longest vector~2.25 mm/s
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50
100
150
(Provided by Micro Fluidics Lab at Purdue University)
200
250
300
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Microthruster: Magnification 40X
Particle size 700 nm
Number of pixels
Typical MPIV Image
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50
100
150
200
250
300
Gray Value
500 mm
- Background image filtered
- Particle image size
dp=5  8 pixels
- Image displacements
S= 15  40 pixels
- Image number density
3 in 32x32-pixel window
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MPIV Image Filter
Typical MPIV image features
- High single-pixel random noise level
because of low light intensity scattered/emitted by nano-scale particles
- High low-frequency noise level
because of particle images out of the focus plane
- Big particle images (dp>4 pixels, dp <4 pixels for standard PIV)
because of high imaging magnification
MPIV filter:
1 1
Gx, y   
9 1
1
 G x  i, y  j 
1
For SP noise
r
1

2r  12r  1 
r
r
 G x  i, y  j 
r
For LF noise
- Filter radius r big enough so that useful particle image information not be erased
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MPIV Image Filter
- Reduce influence of LF noises on the evaluation function
Evaluation samples
Evaluation functions
No filter
Micro-PIV image filter
(m,n)
n
(m,n)
n
m
(a)
m
(b)
- Overall effect of MPIV in a micro-channel flow measurement
Mean velocity profile
Standard deviation
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Average Correlation Function
Correlation functions of replicated measurements at one point in the steady flow:
- position of the main correlation peak not change
- height and position of correlation peaks resulting from noises vary randomly
+
1
N
1 (m, n)
+ •••••
2 (m, n)
=
 ensemble(m, n)
+
 N (m, n)
Average evaluation function method (Meinhart, Wereley and Santiago, 2000)
- average instantaneous evaluation functions to increase the signal-to-noise rato
- only for steady laminar flows
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Long-distance Forward-Scattering MPIV
Problem/solution for applying PIV in micro-scale air jet flow
1. Seeding
2. Working distance
- more difficult than in liquid flow
- smoke particles (Raffel et al.: dp<mm)
- long for micro-scale air jet flow
- long-distance microscope
(QUESTAR QM 100: WD>100 mm)
3. Illumination
- insufficient for sub-micron particles
- forward-scattering configuration
(Raffel et al.: 103)
4. High velocity
- limited by high imaging magnification
- advanced imaging system
(PCO200: ∆t=200 ns)
5. Low image number density
& unsteady flow
- average correlation impossible
- individual image pattern tracking
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Long-distance Forward-Scattering MPIV
Experimental setup
Long-distance Forward-Scattering MPIV
PCO2000 camera
Questar QM 100
14-bit dynamic range
Working distance up to 350 mm
4-GB image memory
14.7 fps @ 20482048 pix
New Wave Solo II-30
532 nm
Beam diameter: 2.5 mm
Repetition Rate: 30 Hz
Test & data acquisition
Reduced image size 1024256 pix for 60 fps (30 image pairs per second)
3 partitions in 4-GB memory for 3 axial positions in each test case
Working distance 120 mm for measurement area 960240 mm2 (0.94 mm/pixel )
1676 recording pairs in each group
Time interval 200 ns
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Long-distance Forward-Scattering MPIV
• Sample PIV recordings pairs (red: 1st image, green: 2nd image)
• Vector maps obtained by individual particle image pattern tracking
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Long-distance Forward-Scattering MPIV
• Overlapped sample PIV recordings pairs (50 pairs)
• Overlapped vector maps (50 vector maps)
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Long-distance Forward-Scattering MPIV
• Remove erroneous vectors by using a median filter
• Calculate local mean, fluctuation & correlation on a regular grid
0
10
20
30
40
50
60
70
80
90
V-fluctuation [m/s]:
100
1600
1600
1550
1550
y [mm]
y [mm]
Mean Velocity [m/s]:
1500
1450
1400
1400
-300
-200
-100
0
100
200
300
400
2
-400
-300
-200
-100
x [mm]
0
1
2
3
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5
6
7
8
9
1600
1550
1550
1500
1450
1400
1400
-200
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12
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16
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0
100
200
300
400
-100
0
x [mm]
100
200
300
400
-50
-40
-30
-20
-10
0
10
20
30
40
50
1500
1450
-300
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uv [m2/s2]:
1600
-400
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x [mm]
y [mm]
y [mm]
U-fluctuation [m/s]:
4
1500
1450
-400
0
-400
-300
-200
-100
0
100
200
300
x [mm]
(Test at y/D = 1.5, Re  3200, 1676 vector maps, 802412 raw vectors, 559259 valid vectors)
400
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Long-distance Forward-Scattering MPIV
• High-speed air jet test results
600
Velocity fluctuation [m/s]:
500
0
400
2
4
6
8
10
12
14
16
18
300
x [mm]
200
100
0
-100
-200
-300
-400
110 m/s
-500
250
500
750
1000
1250
1500
1750
2000
2250
2500
y [mm]
Mean velocity and velocity fluctuation at 3 positions along the jet axis
(D=500 μm, Re  3200)
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Homework
– Reading
• Meinhart CD, Wereley ST, Gray MHB (2000) Volume illumination for
two-dimensional particle image velocimetry. Meas. Sci. Technol. 11, pp.
809-814
• Wereley ST, Gui L, Meinhart CD (2002) Advanced algorithms for
microscale velocimetry, AIAA Journal, Vol. 40, #6
– Practice with EDPIV
• Work with sample: IMAGE GROUP - MICROCHANNEL FLOW
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