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John W. Vinti Particle Tracker Final Presentation
1. Particle Tracking in the
μPIVOT
Project Lead: John W. Vinti
Project Advisor: Derek Tretheway
Department of Mechanical & Materials Engineering, Portland State University,
P.O. Box 751, Portland, OR 97201, USA
June 10th 2015
2. Presentation Structure
1. Introduction to μPIVOT
Purpose
Principles
Sample Past Experiment
2. Project Goals
Bottlenecks of μPIVOT
Definition of Goals
System Limitations
Introduction to μPIVOT
Project Goals
Particle Tracker
Benefits3. Particle Tracker
Layout
Minimization of Operator Error
Key Design Advantages
Demonstrations
4. Benefits
Solution to Bottlenecks
Other Benefits
4. Purpose of μPIVOT
Used to study particle(s) suspended in both Newtonian and
Non-Newtonian Fluids
Theoretical particle-particle interactions in Non-Newtonian
Fluids is limited
Due to establishing proper constitutive equation
Used to Validate existing theoretical models and further
develop models for bulk flow
Computational models of particle suspension are needed
Introduction to μPIVOT
Project Goals
Particle Tracker
Benefits
5. Principles of μPIVOT
μPIVOT functions
1. Manipulates isolated particle/cell in microfluidic environment
2. Images the particle/cell
3. Characterizes the influential microfluidic environment
4. Quantifies applied stresses and induced strains
Equipment
1. Optical Tweezers (OT)
Single particle can be trapped in stationary position
Infrared Laser beam
Modeled as linear mechanical Spring 𝐹 𝑇𝑟𝑎𝑝 = 𝑘 Δ𝑥
2. Micron-resolution Particle Image Velocimetry (μPIV)
2-D velocity measurement technique
Seeds fluorescent Nano-Particles into Field
Particles illuminate with pulses from two frequency doubled Nd:YAG lasers
3. Moveable Stage
Locate Particles
Simulate Forces
Introduction to μPIVOT
Project Goals
Particle Tracker
Benefits
6. The μPIVOT
Introduction to μPIVOT
Project Goals
Particle Tracker
Benefits
Hardware
Software
GeviCam Coyote
NASA Spotlight
7. Sample Past Experiment
Manipulation of Suspended Single Cells by Microfluidics and
Optical Tweezers
Nathalie Néve, Sean S. Kohles, Shelly R. Winn, and Derek C.
Tretheway
Uses the μPIVOT to examine the viability and trap stiffness of
cartilage cells, identify the maximum fluid-induced stresses
possible in uniform and extensional flows, and to compare the
deformation characteristics of bone and muscle cells.
Introduction to μPIVOT
Project Goals
Particle Tracker
Benefits
8. Project Goals
“So… The μPIVOT System is without flaw?”
Introduction to μPIVOT
Project Goals
Particle Tracker
Benefits
9. Bottlenecks of μPIVOT - Calibration
OT behavior and trapping force are dependent upon particle type, laser
power, particle shape, particle size, and fluid media.
𝐹 𝑇𝑟𝑎𝑝 = 𝑘∆𝑥 (Trap Stiffness is needed)
Drag Force method, the non-linear Lateral Escape Force method, the
Equipartition method and the Power Spectrum method
The trap stiffness calculated by linearly fitting a range of known drag force
(𝐹 𝐷𝑟𝑎𝑔) versus displacement data (∆𝑥 ) (the difference between the particle
position when the particle is trapped without flow and trapped with flow) and
determining the slope.
Introduction to μPIVOT
Project Goals
Particle Tracker
Benefits
Configure
μPIVOT
Capture
Images
Export
Images
Import
Images to
Post Process
Software
Perform Post
Processing of
Each Frame
Collect
Data
Interpret
Data
Apply to
Experiment
LONG
10. Bottlenecks of μPIVOT – Cell Movement
Trapped particles/cells are ideally
static and are stabilized by inflow
outflow saddle point
Small uncontrollable perturbations
can cause the particle/cell to
become unstable and move around
within the saddle point
OT imparts continuous laser to keep
the particle/cell stabilized within
the flow
Consequence: Too much energy can
cause cell degradation and death
Introduction to μPIVOT
Project Goals
Particle Tracker
Benefits
*
11. Bottlenecks of μPIVOT – Post Processing
All data analysis of μPIVOT experiments are done post-process
Coyote and Spotlight
Limited useable data can be extracted instantaneously during experiments
Introduction to μPIVOT
Project Goals
Particle Tracker
Benefits
LONG
12. Definition of Goals
Provide a reliable system to perform the following
functions for the μPIVOT System:
Real Time Video Feed
Efficient Real Time Image Processing
Reliable Real Time Tracking Information
Reliable Real Time Deformation Analysis
Versatile Software for Numerous Applications
Variable Output Capability
Compatible with current system
Incorporate Same Functionalities of Post Processing
Software
Simple to Use (for undergraduates)
Introduction to μPIVOT
Project Goals
Particle Tracker
Benefits
13. System Limitations
Currently installed programs
SOFTWARE REAL
TIME
FEED
REAL TIME
IMAGE
PROCESSING
TRACKING
CAPABILITY
DEFORMATION
ANALYSIS
VERSATILITY VARIABLE
OUPUT
CAPABILITY
SIMPLICITY
(Subjective)
SPOTLIGHT X X X X
COYOTE X X X
PARTICLE
TRACKER
X X X X X X X
Combine the features of both programs into one
Introduction to μPIVOT
Project Goals
Particle Tracker
Benefits
14. Particle Tracker
“As you can see this project is a big deal”
Introduction to μPIVOT
Project Goals
Particle Tracker
Benefits
15. Layout
(a) Real time video feed
(b) Real time filtered video feed
(c) Start video feed pushbutton
(d) Stop video feed pushbutton
(e) Threshold filter slider
(f) Area filter slider
(g) Frames to track popup
(h) Track real time feed pushbutton*
(j) Tracking status indicator**
(k) Tracking location indicators**
(m) Length indicators**
(n) Breadth indicators**
(a) (b)
(c) (d)
(e)
(f)
(g) (h)
(j)
(k)
(m)
(n)
Introduction to μPIVOT
Project Goals
Particle Tracker
Benefits
16. Minimization of Operator Error
*(h) Track real time feed pushbutton
Dependent upon input of (c) Start video feed pushbutton
**(j) Tracking status indicator
Dependent upon input of (h) Track real time feed pushbutton
Disappears after the specified number of frames are tracked
**(k) Tracking, (m) Length, and (n) Breadth location indicators
Dependent upon input of (h) Track real time feed pushbutton
Disappears after the specified number of frames are tracked
Serves as verification that track is accurate via superimposing
Introduction to μPIVOT
Project Goals
Particle Tracker
Benefits
17. Key Design Advantages
MATLAB Based System
The University has a license for MATLAB and the Image Acquisition
Toolbox
Likely to retain MATLAB for the future
Control friendly environment
Graphical User Interface
Provides a simple to use environment for operators
Allows for operator control over processing
Limited Toolbox Use
No need for the University to purchase any other toolboxes for
MATLAB
Previous versions of the Particle Tracker used Toolboxes not
available to University
Introduction to μPIVOT
Project Goals
Particle Tracker
Benefits
19. Benefits Introduction to μPIVOT
Project Goals
Particle Tracker
Benefits
“He’s right! When you look at it that way, it’s not
so bad!”
20. *
Solution to Bottlenecks
Calibration
Cell Movement – Pulse (Opportunity)
Introduction to μPIVOT
Project Goals
Particle Tracker
Benefits
Configure
μPIVOT
Capture
Images
Export
Images
Import
Images to
Post Process
Software
Perform Post
Processing of
Each Frame
Collect
Data
Interpret
Data
Apply to
Experiment
Configure
μPIVOT
Run
Software
Collect/Analyze
Data
Apply to
Experiment
Real Time – Processing
Centroid
Area
Length
Breadth
Orientation
21. Other Benefits
Within the μPIVOT System
Non-Newtonian Fluid Studies
Capable of performing multiple particle analysis
Outside μPIVOT System
Incorporation into other systems that require tracking
Limited MATLAB Toolbox Usage
Non-Engineering based Systems
Educational Tool
Fully annotated MATLAB Code
Fully customizable
For Other Experiments
Introduction to μPIVOT
Project Goals
Particle Tracker
Benefits
Questions During
About Me:
BS ME from University at Buffalo 2012
Lean Six Sigma Black Belt 2014
Worked at ITT Enidine Design Team, DoD Contract for missle isolation systems
Worked at Welch Allyn Manufacturing Med Devices,
In killer rock and roll band
Hopefully Be awake at the end and I’ll be granted a masters
Started Spring 2014
Project Started in Summer 2014
4 prior revisions 1 final
Acknowledgements
OT – Wavelength 1064nm
OT – Beam is passed through a lens and is focus on diffraction limited spot
OT – Delta X is particle displacement from trap center, k is trap stiffness
mPIV – It images the emitted light with filtering techniques
mPIV- Lasers Synced with Charged Coupled Device Camera
mPIV – Can be 3-D if scan layer planes and applying continuity equation
Red Lines are the position of OT Beam
Describe and Picture, picture first
Velcoties, how to calibrate process,
Non Newtonian preface – oscilating flow time delay (phase Shift)
Gives an idea of how powerful this system can be and what it can acomplish
First Identify the Bottlenecks of the system
Want drag force equal to Trap Force for equilibrium
Drag Force Easiest Method
For highly nonspherical and/or biological objects, the drag force method alone may not be sufficient, therefore additional trap calibration methods may be necessary.
Takes a full day
A is the radius of the spherical particle, l is the half height of the channel, μ is the fluid viscosity, and v is the fluid velocity experienced by the sphere.
Cross Flow Scenario
Now that we understand these bottlenecks, we can generate design requirements for a new software application to help circumvent these issues
Discuss Specifics for each system More
Two things go to one
Reinforce Barbones MATLAB can run with toolbox,
Calibration – Shortened Process. Saves Time and Money
Cross Flow – Longer cell life less usage of OT
Real time – No more post processing
Limit Energy input
Why not PIV for Tracking – Slow Framerate, not really real time, and image correlation, used for groups of particles not single particle
