1. Particle Image Velocimetry
Fundamental Theory and Operation

2. Outline Definition and Core Concepts of PIV
Software Processing- Cross-Correlation
Hardware Principles- Basic Operation

3. Particle Image Velocimetry
4/14/2017
Defn :Experimental means to determine the instantaneous flow field
A particle-laden flow is illuminated by a thin sheet of light (2D)
Two images are taken with a short time between each capture
The two images are compared to determine particle displacement
Particle displacement is translated into an instantaneous velocity measurements
PIV component technology is continuously evolving and it is a challenge for both the users as well as manufacturers to keep pace with the latest developments. The user is particularly concerned that the PIV system acquired today is not obsolete tomorrow. TSI has factored this need into our PowerView PIV system design and the result is a very versatile and flexible system that can incorporate future developments with minimum cost.
TSI’s philosophy has always centered around systems and processing approaches that can extract maximum information from the experiment. In PIV this would require that the raw image information is never lost and is available along with the corresponding vector file. The benefits of such a data management approach are numerous. Having the raw image would mean never having to repeat an experiment. The same image can be processed in several different ways to obtain detailed flow information not extracted during preliminary analysis. Comparing the vector field to the corresponding particle image field offers insight into the nature of the flow, on-line validation and enhances the confidence level in the velocity vector data. Availability of the raw image field offers the ability to get detailed information about the scatterers such as particle image size, concentration etc.
It is a misconception that higher speed of processing can be achieved only with dedicated hardware processors and discarding the raw image. Working in the 32-bit NT environment combined with multi-processor support is generating very high processing speeds for on-line data collection, analysis and display without the limitations associated with dedicated hardware of higher cost, loss of raw image and lack of flexibility.
The flexible approach offered by the TSI’s PowerView PIV system ensures that the system will grow as camera, interface and processing technologies evolve.
2-velocity component System
3-velocity component System
Copyright© 2007 TSI Incorporated

4. PIV Fundamentals
PIV measures are taken very quickly (milliseconds in total)
At time t1
Pulsed laser sheet illuminates a planar region of the flow
Particles are imaged on the camera (Frame A)
At time t1 + Dt
A second image (Frame B) is taken of a second light sheet
Statistical (Cross-Correlation) methods are used to determine the particle displacement over the time Dt, and thus the local velocity

5. FRAME A
Copyright© 2007 TSI Incorporated

6. FRAME B
Copyright© 2007 TSI Incorporated

7. Instantaneous Velocity Field
Copyright© 2007 TSI Incorporated

8. Measurement at one point over a period of time
PIV
LDV
Measurement at one point over a period of time
Provides time history of the flow and hence time averaged statistics at one point. Flow field mapped by traversing the measuring point
Velocity obtained by measuring time to travel a known distance
Measurement at many points at one instant of time
Provides instantaneous vector fields. Time averaged statistics obtained by averaging several image fields
Velocity obtained by measuring image displacement in a known time
LDV provides the time history of the flow (velocity components) at the measurement location. The measuring region or the measuring volume is the intersection region of the laser beams.
In PIV, measuring the displacement of particle images in the time between two laser flashes (multiple flashes can also be used) that illuminate a plane (in the flow field) provides the velocity information.
While the measurement volume size determines the spatial resolution in LDV, the maximum image displacement gives a measure of spatial resolution in PIV. Seeding (adding particles that follow the flow) requirements are comparable for both techniques.

9. Cross Correlation
All particles look alike, so it is hard to find the ‘same’ particle in both Frame A and B (PTV)
Instead, PIV uses a statistical approach to find the most likely displacement of a group of particles
Frame A is broken up into a grid of ‘interrogation regions’
The group of particles in the interrogation region creates a unique ‘fingerprint’ that we can look for in both frames (PIV)

10. Cross Correlation Fame B Search Area
(“Spot B”)
The relative location of the interrogation region in Frame A is known
A search area is defined in Frame B.
Frame A Interrogation
Region
(“Spot A”)

11. Original Particle Positions in Frame A
Cross Correlation
Original Particle Positions in Frame A

12. Location of particles in Frame B
Cross Correlation
Location of particles in Frame B

13. Cross Correlation
A ‘Spot Mask’ (above) created from Frame A, is scanned across the search area of Frame B to form a ‘correlation map’

14. Cross Correlation
The correlation map will have a peak relative to the location where the ‘fingerprint’ of the Spot Mask is identified in Frame B

15. Cross Correlation
The correlation values for different locations of the spot mask can be represented on a “correlation map”
In the ideal case, there is a distinct, single, round peak in the correlation map

16. Cross Correlation DY DX
The X and Y displacements of the particles are determined by the offset of the interrogation regions DX

17. Cross Correlation
Since we know the time separation (Dt) between the 2 laser pulses very accurately, we can measure the ‘group’ velocity as the displacement / Dt, and assign a single velocity vector

18. Cross Correlation A) B)
NOTE: Particles and Interrogation region are typically larger than represented here in this simplified example
Frame A
Spot Mask

19. Cross Correlation A) B) (1*1) (1*0) + (1*0) 1 Cross Correlation Map
Displacement (x,y) = (0,0) 1

20. Cross Correlation A) B) (1*0) + (1*0) Cross Correlation Map
Cross Correlation Map
Displacement (x,y) = (0,1) 1

21. Cross Correlation A) B) (1*1) (1*0) + (1*0) 2 2 Cross Correlation Map
Displacement (x,y) = (0,2) 1

22. Cross Correlation A) B) (1*0) + (1*0) 2 Cross Correlation Map2
Cross Correlation Map
Displacement (x,y) = (1,2) 1

23. Cross Correlation A) B) (1*1) + (1*1) 5 2 5 Cross Correlation Map
Displacement (x,y) = (2,2) 1

24. Cross Correlation A) B) Cross Correlation Map
Displacement (x,y) = (2,2)
Peak in the Correlation Map
is at (+2,+2), indicating that the
particles moved in this direction
from Frame A) to Frame B).

25. Processing Crosscorrelation Spot B Interrogation region Spot A Frame A
particle
displacement
Crosscorrelation
Frame B
Vector field

26. Cross Correlation
The process is repeated for each interrogation region in Frame A, resulting in a 2-Dimensional velocity field for the imaged region

27. Hardware Principles 2-component System 3-component System
PIV component technology is continuously evolving and it is a challenge for both the users as well as manufacturers to keep pace with the latest developments. The user is particularly concerned that the PIV system acquired today is not obsolete tomorrow. TSI has factored this need into our PowerView PIV system design and the result is a very versatile and flexible system that can incorporate future developments with minimum cost.
TSI’s philosophy has always centered around systems and processing approaches that can extract maximum information from the experiment. In PIV this would require that the raw image information is never lost and is available along with the corresponding vector file. The benefits of such a data management approach are numerous. Having the raw image would mean never having to repeat an experiment. The same image can be processed in several different ways to obtain detailed flow information not extracted during preliminary analysis. Comparing the vector field to the corresponding particle image field offers insight into the nature of the flow, on-line validation and enhances the confidence level in the velocity vector data. Availability of the raw image field offers the ability to get detailed information about the scatterers such as particle image size, concentration etc.
The flexible approach offered by the TSI’s PowerView PIV system ensures that the system will grow as camera, interface and processing technologies evolve.
2-component System
3-component System

28. System Components
Imaging Subsystem (Laser, Beam delivery system, light optics)
Illuminate a plane in the flow (seeded) using a pulsed laser
Pulse energy, duration, and repetition rate
Typically Nd:YAG laser operating at 532 nm
Image Capture Subsystem (CCD Camera, Camera Interface, Synchronizer-Master control unit)
Master Timing devise triggers illumination and camera capturing
Camera captures particle images and records them
Analysis and Display Subsystem
Calculates and displays a two dimensional vector field from the particle image fields
Capable of processing higher-order statistics of flow field
A complete PIV system can be viewed as a combination of three sub-systems. The first and second subsystems (in the list above) are responsible for obtaining good particle image fields, and the third for processing and displaying corresponding velocity fields and their derivatives. While imaging involves on-line set up and optimization, analysis/display can be done on-line or off-line.
Laser has become the choice illumination device since it can produce high energy short duration pulses. The laser pulse is delivered to the measurement location using flexible beam guides with mirrors or through optical fibers. Optical elements can convert these pulsed laser beams into pulsing light sheets. CCD cameras are used to record the particle image field. A master control system referred to as a Synchronizer system is used as the timing device to generate the control signals and synchronize the camera with the laser pulses and the camera interface.
Camera interface is used to ensure very fast data transfer from the camera to the computer. Image acquisition/analysis/display software is the umbrella program that allows the user to set and optimize the hardware and the experiment, acquire images, process them and display velocity fields.

29. Nd:YAG Laser 15 mJ - 400 mJ per pulse 4 ns - 20 ns pulse duration
Acts like strobe light
freezes the particle images
Wide range of T
to measure flow velocities from mm/s to supersonic speeds
Hz Pulse Repetition Rate
532 nm Wavelength (Frequency Doubled)
Most visible wavelength
The dual Nd:YAG laser is the preferred light source for most PIV experiments. This integrated laser houses two separate laser heads whose pulses are combined with built-in beam combination optics to produce a coaxial pulse train. The very short pulse duration is fast enough to “freeze” even particle images in supersonic flow without image streaking. The high pulse energy is able to illuminate small particles for either air or water flows. Because two lasers are used any time between pulses is possible, with the full energy in each pulse.
Lower power (10 to 100 mJ ) Nd:YAG lasers are now available for water flow experiments or small air flow experiments. These small Nd:YAG lasers are able to deliver much higher pulse energy than even a 10 Watt argon laser.
Dual Nd:YAG s can come with variable repetition rate from Hz. A dual YAG laser with 15 Hz rep rate per laser works well with the special 30 Hz cross correlation camera, when two images are captured on separate frames. This would give 15 velocity fields per second.

30. Energy vs. Q-switch delayHi
Med
Low
100%
80%
60%
Pulse energy - % of maximum
40%
20%
Nd:YAG lasers use two trigger signals in generating a pulse of light. The first trigger fires a flash lamp. Photons from the flash lamp are absorbed by the Nd:YAG rod where this energy is stored. The second trigger opens the Q-Switch releasing the stored energy as a laser pulse. The light discharge from the flash lamp takes some time to reach maximum brightness and then decreases. The pulse energy depends on flash lamp brightness when the Q-Switch is opened. The figure above shows the relationship between pulse energy and Q-Switch delay.
By setting the Q-Switch delay time the pulse energy is selected. This allows a minimum energy pulse to be used for alignment and setup, and a high energy pulse to be used for the experiment. The Synchronizer and LaserPulse software set the laser pulse energy by setting the Q-Switch delay time.
The pulse energy can also be set (on some laser models) by setting the flash lamp voltage. Adjusting the pulse energy with Q-Switch delay is recommended because it keeps the laser at the operating temperature it was tuned for. This maintains the best beam quality. 0% 50
100
150
200
250
300
Q-Switch delay (microsec)

31. Light sheet Optics “Fan” laser light to create 2D measurement plane
Combination of cylindrical and spherical lenses
Cylindrical lens diverges (fans) light in one direction
Dictates measurement area
Spherical lens waists (thins) light sheet
Achieves “2D” sheet (very thin)
Cylindrical lens
Spherical lens
waist
Laser light sheet optics are used to control the dimensions of the illuminated area. A cylindrical lens controls the light sheet height. A spherical lens is used to control the light sheet thickness.
The top view shows the thickness of the light sheet. The cylindrical lens has no effect on the light sheet thickness. The spherical lens forms a waist in the light sheet at its focal point. The bottom view shows the light sheet height. The beam diverges in the vertical direction after passing through the negative focal length cylindrical lens. The positive focal length spherical lens reduces this height divergence only a little. Typically the cylindrical lens has a much shorter focal length than the spherical lens.
Typically the camera views the light sheet near the thickness waist, where the intensity is highest, but any place along the light sheet can be used. When selecting the light sheet optics and camera viewing position the light sheet intensity should not be allowed to vary too much over the field of view.
Fan

32. PIV Image Capture Synchronizer controls timing with high precision
Controls laser power and “que” timing
Initiates camera capture
Camera captures 2 images “back to back” upon trigger
Frame A exposure Independent (user defined)
Frame B exposure Dependent (fixed)
Laser light intensity determines particle image brightness
NOT exposure time
Timing parameters chosen so that one laser pulse appears in frame A and another in frame B

33. Synchronization Camera Exposures Camera Image Readout Pulse separation
4/14/2017
Image 1 Exposure
Image 2
Exposure
Camera Exposures
Camera
Image Readout
Image 1 Readout
Image 2 Readout
Pulse separation
LaserPulses
Frame straddling technique allows very short time between laser pulses and thus provides the capability to measure higher and higher speed flows. Frame Straddling requires a close synchronization between the camera exposure and the pulsing of the laser.
Key camera specification is the time from the end of one exposure to the start of the next exposure, the minimum frame straddle time. When trying to make the highest flow velocity measurement the first pulse is delayed until the last moment of exposure 1, and the second pulse happens at the first moment of exposure 2.
Actual frame straddle time requires that Image1 be removed from the pixels before the second exposure starts. The time to move the image from the pixels depends on the image brightness. Actual frame straddle times can vary with experimental setup.
Camera Trigger: The Synchronizer Triggers the camera to start a double exposure sequence. The Frame Grabber is also triggered at this time to capture the next two images. An external trigger can initiate the camera trigger.
Camera Shutter Feedback: After a short time the Shutter Feedback signals the start of the first frame exposure.
Camera Exposure: The two frame exposure includes a short frame 1 exposure and a longer frame 2 exposure. The first exposure is just long enough to Q-Switch a Nd:YAG laser. During the second exposure image in frame 1 is readout.
Camera Digital Video Image output: The first image(frame 1) moves quickly from the light sensitive pixels to the readout registers. It then takes one frame time to transfer the image out of the camera and to the frame grabber. The second image cannot end until all of the first image has been read out of the camera.
Laser Pulses: The first laser pulse must end before the end of exposure 1. The second pulse must start after the beginning of exposure 2

34. Synchronization-Example Timing
4/14/2017
400 ns
2 ms
Camera Exposures
400 us
Camera
Image Readout
2 ms
500 us
LaserPulses
Frame straddling technique allows very short time between laser pulses and thus provides the capability to measure higher and higher speed flows. Frame Straddling requires a close synchronization between the camera exposure and the pulsing of the laser.
Key camera specification is the time from the end of one exposure to the start of the next exposure, the minimum frame straddle time. When trying to make the highest flow velocity measurement the first pulse is delayed until the last moment of exposure 1, and the second pulse happens at the first moment of exposure 2.
Actual frame straddle time requires that Image1 be removed from the pixels before the second exposure starts. The time to move the image from the pixels depends on the image brightness. Actual frame straddle times can vary with experimental setup.
Camera Trigger: The Synchronizer Triggers the camera to start a double exposure sequence. The Frame Grabber is also triggered at this time to capture the next two images. An external trigger can initiate the camera trigger.
Camera Shutter Feedback: After a short time the Shutter Feedback signals the start of the first frame exposure.
Camera Exposure: The two frame exposure includes a short frame 1 exposure and a longer frame 2 exposure. The first exposure is just long enough to Q-Switch a Nd:YAG laser. During the second exposure image in frame 1 is readout.
Camera Digital Video Image output: The first image(frame 1) moves quickly from the light sensitive pixels to the readout registers. It then takes one frame time to transfer the image out of the camera and to the frame grabber. The second image cannot end until all of the first image has been read out of the camera.
Laser Pulses: The first laser pulse must end before the end of exposure 1. The second pulse must start after the beginning of exposure 2

35. Synchronizer System External trigger -or- Insight3G Software
Capture interface
Computer
The computer controlled Synchronizer is at the heart of all PIV systems. The primary function of a Synchronizer system is that of a precise timer and a master system controller. It synchronizes the timing of image capture with the pulsing of the laser. Acting as the master controller for system components, it automates control of the timing between laser pulses, camera, camera interfaces and any external device during system set-up and image acquisition. The Synchronizer enables the system to be completely computer controlled via a serial interface. Special crosscorrelation cameras are employed in PIV which enable pairs of images be taken with short time between pulses. Control of such cameras is built into the synchronizer. Signals for the laser flash lamps and Q-switches, the camera and the frame grabber are generated and automatically synchronized for effortless image acquisition. For PIV applications, pulse delay time and the time between pulses necessary to collect frame-straddling images, are controlled visa TSI’s INSIGHT software. The synchronizer has an auxiliary output for controlling various devices in an experimental rig. For periodic flows, where phase locked velocity measurements are desirable, the Laserpulse synchronizer can be externally triggered using a TTL signal from the experimental apparatus. For example, in an IC engine experiment, images are captured at a certain crank angle position to be ensemble averaged. In bio-fluid mechanics relating to heart valve flows, images are captured at certain events in the systolic/diastolic cycle. The synchronizer system provides all the needed control signals – and does not need adjustments – for the cameras, lasers and other devices supplied as part of the TSI PIV system.
Laser
control

36. PIV Rules of Thumb for great results
The key to good measurements is good raw data
The raw data of PIV measurements are particle images
In the Ideal Case
Seed particle images are 3 – 5 pixels in diameter
5 – 15 particles per interrogation region
Maximum particle displacement approximately 25% of size of interrogation region
It is not always possible to satisfy these in all measurements, but they are good experimental goals

37. Experiment Considerations
Important considerations:
What size field of view?
What desired spatial resolution?
Are the above 2 realistic together?
Appropriate seeding
Optical access
Camera 90 degrees with respect to laser sheet
View is not distorted
What does it look like when you view the measurement region from the perspective of the camera?
0.2 m
0.2 m
Field of View
Using 4MP (2k x 2k) Camera…
…apply 32pxl x 32pxl interrogation region
…apply 64pxl x 64pxl interrogation region

38. Experimental Setup General Setup Setup Tips
Laser illuminates plane of interest
Camera 90 degrees to light sheet
Good optical access (no distortions)
Camera is focused on ‘waist’ of the
light sheet
Setup Tips
Initial setup: steps should be performed in ‘free; continuous’ mode of the Insight 3G software
Use room light, no laser
Final setup: the best practice is to focus camera on seed particles in the flow
This assures that the focus is optimized to the laser sheet

39. Experimental Setup
When setting up the cameras to view laser light scattered
from particles:
Start on a large f-number (small aperture) and low laser energy
Increase alternately until seed particles are well-illuminated with minimal pixel saturation
WARNING: IF LARGE REGIONS OF SATURATED (PINK)
PIXELS APPEAR, STOP CAPTURE IMMEDIATELY
Laser light can damage camera pixels
Reduce laser energy and/or increase f-number

40. Calibration What are we ‘calibrating’?
We need to show the software how to convert from pixel units to the physical units relevant to our flow space
We are considering the simplest case in 2D PIV, where we view the light sheet at 90 degrees with good optical access
Off-axis viewing and optical distortions are sometimes unavoidable, and appropriate corrections can be made
In these cases, we need to give the software more information to be able to convert pixels to mm over the entire image region
Addressed in future webinars

41. Calibration Procedure
Assumption: Cameras focused
on laser light sheet
Calibration Steps
Capture and save image of “Ruler” in plane of laser light sheet
Create a calibration file with the saved image
Use the 2D-Calibration software tool to measure across a known distance in the calibration image

42. Seed Density Considerations for Seeding Density:
The true test of appropriate seed particle density is examining the Particle Images
Considerations for Seeding Density:
Field of View is known
Desired Spatial Resolution
known
Interrogation Region Size
Do I have enough particles in
each interrogation region?
5-15 particles per region

43. Optimizing Dt
In addition to imaging the flow field, we must also determine
the timing between Frame A and Frame B.
This parameter is called Δt
We must select a Δt so that the displacement follows our “rule of thumb” of 25% of our intended interrogation region
Ensure consistency with experimental objectives
Field of view
Spatial resolution
Appropriate limits on particle displacement
Maximum particle displacement should be approximately 25% of the interrogation region

44. Optimizing Dt
The first step in optimizing Dt is developing “an eye for it”
Can you see the displacement?
Although qualitative, this is a critical step in optimizing a PIV measurement
If displacements appears random, reduce Dt
If there is little / no displacement, increase Dt

45. Optimizing Dt
There are several ways to ‘measure’ the displacement for a more quantitative assessment
Zoom into individual particles
Perform ‘Point Processing’ and assess displacement of individual spots
Remember, this process is iterative. Expect to alternate between experimental optimization and processing adjustments

46. Optimizing Dt Timing Setup
The Timing Setup Window in Insight 3G Dt

47. Basic PIV Processing Start with “general purpose” settings
Basic processing lets you know what the flow is doing and how well your images process
A good foundation for further optimization
Iteratively optimize
This may include making experimental changes and acquiring new images

48. PIV Rules of Thumb Review
Seed particle images are 3 – 5 pixels in diameter
5 – 15 particles per interrogation region
Maximum particle displacement approximately 25% of size of interrogation region
3-5 pixel particles allows for a Gaussian ‘fit’ to determine location to subpixel accuracy, but still allows for a high seeding density
Multiple particles per interrogation region strengthen the correlation
25% displacements keep most particles within the interrogation region, allowing the search to match with most of the spots

49. Basic PIV Processing
Insight3G’s default settings are a good starting point
Classic PIV produces 1 vector field from 1 image
64x64 pixel size for both spot A and spot B works well for moderately-seeded flows with similar x and y velocity ranges
The Nyquist grid setting gives us 50% overlap between neighboring spots
Since we are using NyquistGrid, the starting spots are the final spots
The FFT correlator is a good general-purpose choice.
This enforces our 25% rule of thumb
The Gaussian peak engine is normally the best choice for obtaining accurate velocities.

50. Evaluating Processing
The easiest way to evaluate your processing settings is to process a vector field.
Adjust the scale of the vectors for a clear view
Do the generated vectors appear physically reasonable?
Are there many “red” vectors that failed validation?

51. Evaluating Processing
Vector Statistics provide more useful information
Maximum U and V pixel displacements
Number and percentage good vectors

52. Evaluating Processing
This image is a good candidate for increasing the spatial resolution by decreasing interrogation region size
99.6% valid vectors
maximum particle displacements of >5 pixels
High seeding density suggests 5-15 particle rule of thumb possible for smaller interrogation region
What to consider:
How much smaller?
Spot A? Spot B?

53. Spot Dimensions
The Spot A size is used to determine spatial resolution
Velocity of all particles in Spot A is averaged into a single vector
The Spot B size and maximum dx, dy determine search area size
Larger sizes slow down processing, only help with large displacements
Spot A
Spot B

54. Adjusting spot dimensions
Dimensions of Spot A determines:
How many particles in the interrogation region (5-15)
Smallest resolvable flow features (spatial resolution)
Maximum allowed displacement (dx, dy):
Maximum measurable particle displacements (dynamic range) in X and Y
25% spot width, height
Adjusting these features can accommodate processing smaller interrogation regions.

55. Adjusting Spot Dimensions
Reprocessed with 32x32
Now we are starting to get more invalid vectors.
Why? Let’s look closer:
Uneven seeding leaves some spots empty

56. Adjusting Spot Dimensions
Point Processing lets us evaluate the outcome of processing at a given spot.
Here we see how low seeding density affects the correlation map
Multiple peaks
Lumpy Background

57. Point Processing Point Processing shows us other information as well
Peak Ratio
Processing Algorithms
Input and as-processed spots
Location of the correlation peak within the map
Near the center is best
Dx, Dy

58. Adjusting Spot Dimensions
For some flows, square spots may not be optimal
For example, a boundary layer
Large displacements, low gradients in X (need large spot size)
Small displacements, sharp gradients in Y (need small spot size)
Can use rectangular spots to optimize both dimensions
Rectangular spot A averages velocities over a rectangular region
Rectangular spot B combined with larger allowed displacements looks for a correlation over a larger area

59. Adjusting spot dimensions
This flow is sufficiently seeded for 32x32 (or even 24x24) interrogation, but we still get many invalid vectors.
Dx is too large for 32x32 spot A and spot B size
Try different spotA and spotB sizes
Here we could also offset the search region in spot B
Requires knowledge of the flow field
Only works in flows with a dominant flow direction

60. Adjusting spot dimensions
64x24 Spot A
64x24 Spot B
0.25 Maximum dx
32x32 Spot A
64x32 Spot B
0.49 Maximum dx

61. Single-Pass drawbacks
With a fixed spot size and single pass, smaller interrogation regions give higher spatial resolution but reduced dynamic range
There are workarounds
We can relax the 25% rule of thumb and use a larger spot B if the image quality is high
We can use rectangular interrogation regions if the flow geometry permits
We can offset spot B
There is a better option – Multi-pass processing
Can optimize for both spatial resolution and dynamic range
Improves SNR for better quality vector fields
Operates like a dynamic spot offset

62. Multi-Pass PIV Processing
Using the RecursiveNyquist-Grid Multi-pass PIV processing lets us increase our spatial resolution

63. Multi-pass PIV Processing
Process PIV image multiple times in sequence
Successive passes use the initial results as a starting guess
The search area in Spot B is “offset” relative to Spot A – centered on the displacement “guess”
Spot A and Spot B will contain only the “fingerprint”
Higher SNR, more and better vectors
25% rule of thumb only applies to first pass
Spot B
Spot A

64. Vector Post Processing
Vector Validation
Basic PIV processing checks only SNR to validate vectors
Need a way to “weed out” invalid vectors
In final data
In between passes
Vector Conditioning
Interpolation to replace discarded vectors
Smoothing
Especially important for intermediate passes in multi-pass techniques
Avoids using a bad vector as a guess for additional processing

65. Vector Validation Global Validation
Check velocity against specified range or number of standard deviations from the mean
Good when you know what range of velocities to expect

66. Vector Validation Local Validation
More flexible, compares a vector with its neighbors
Discriminates based on difference between the vector and the median or mean displacement (in pixels), or dimensionless “Universal” median
Decrease neighborhood size for flows with high gradients
Option to replace discarded vectors with local median or secondary correlation peak

67. Vector Conditioning Apply After discarding invalid vectors
Replace discarded vectors with the local median or mean velocity
Optionally apply a low-pass filter to the vector field for smoothing
Keep in mind that smoothing changes all vectors – modifying your data

68. Deformation Grid Processing
Means to sharpen peak of correlation map
Accounts for high rotation or velocity gradients within interrogation regions
Anticipates displacement of particles, and deforms Spot B to account for such gradients
Surrounding velocities used to determine deformation
Spot A
Sharpen peak of correlation function
Deformed Spot B
“Traditional” Spot B
(Overlaid Spot Masks)

69. Deformation Grid Processing
Stronger correlation peak and better accuracy with high gradients
Higher computational cost

70. Thank you!
Questions?