For various particle dynamics applications, micron-resolution particle tracking velocimetry (mPTV) offers several advantages over mPIV. For example, PTV measurements allow the study of dynamics of Brownian particles in the presence of velocity gradients, the quantification of particle-particle interactions in a flow, and the (direct) study of electrophoretic (drift) particle motion. In the latter case, PTV can be used to quantify particle-to-particle differences in electrophoretic drift mobilities and to quantify channels with varying electroosmotic wall mobility (Devasenathipathy et al. 2002).
PTV is the general term for image-based
tracking of individual seed particles in a flow. The technique uses custom
pattern recognition, particle image analysis, and particle image tracking
algorithms. In our lab, we typically use the so-called super-resolution PIV
algorithm in which we first obtain PIV measurements of the flow, then use this
preliminary (PIV) data to predict probably displacements of individual
particles, and finally search images for the displacement of individual
particles. We leverage a Kalman filtering algorithm for the prediction of the
particle trajectory and use c2 testing for validation of particle identification (Takehara et al. 2000). After
the PIV step, the velocity of each particle is interpolated from the measured
velocity field, and this velocity is used as a predictor for the particle
information in the next image via Kalman filtering. The c2 statistical validation
uses information of particle location, previous particle velocity, particle
image size, and particle image intensity to validate particle image pairs. An
iterative procedure is implemented until the number of particle pairs matched is
maximized. The goal is a robust individual particle tracking algorithm with a
minimal number of user inputs. The technique is not as robust as PIV but offers
unique information in some cases.
Optical setup
The particle tracking system uses epifluorescent
microscopy, pulsed-laser illumination, and CCD imaging to capture particle
images. The imaging system is identical to the system we use for micron-resolution particle image velocimetry (mPIV).
mPTV is achieved using specialized
image interrogation algorithms to provide micro-scale displacement measurements
for individual particles.
Sample particle velocity field quantification
Below is an example application of mPTV.
As described above, we used the super-resolution algorithm to apply mPIV
or mPTV respectively to a pressure
driven flow through a microchannel intersection.
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Figure 3 Particle tracking velocimetry measurements for 500 nm diameter particles in a pressure driven flow through a cross-channel interaction. (a) Velocity field obtained by applying a combined PIV and PTV analysis to particle images, (b) individual velocity vectors in an exploded view of sub-region shown in part a. The maximum particle velocity is approximately 500 um/s. |
Figure 3 shows the result for the data images described above using the combined PIV/PTV analysis. The velocity field obtained with the PIV/PTV analysis is unstructured, given the random position of validated particle displacements inside the flow field. The number of vectors obtained by PIV is 480 in the test section while the number of vectors identified by the super-resolution-Kalman filtering – c2 method is 5200. Note that the vectors from the PTV analysis are individual particle displacements, randomly positioned in the flow field, and include the Brownian motion component.
References
1.) Devasenathipathy, S., Santiago, J.G., Meinhart, C.D., and Wereley, S.T., and Takehara, K., "Particle Tracking Techniques for Microfabricated Fluidic Systems," Experiments in Fluids; April 2003; vol.34, no.4, p.504-14
2.) Keane RD; Adrian RJ; Zhang Y. (1995) Super-Resolution Particle Imaging Velocimetry. Measurement Science Technology 6: pp. 754-768.
3.) Takehara K; Adrian RJ; Etoh GT; Christensen KT (2000) A Kalman tracker for super-resolution PIV. Exp Fluids 29: pp. S34-S41.