Multi-Exposure Darkfield Digital Inline Holography for Ultrafast Microparticle Tracking
Grazzini Placucci, Rafael
2022-10
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Multi-Exposure Darkfield Digital Inline Holography for Ultrafast Microparticle Tracking
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2022-10
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Abstract
Current imaging solutions are unable to characterize complex and 3D ultra-highspeedmicroscopic flows with sufficient sampling rate, spatial resolution or depth-offield.
Options that overcome the challenge in temporal resolution typically sacrifice
depth-of-field and spatial resolution for increased framerate, and employ an array of
sensors to extend measurements to three dimensions, further aggravating system cost
and complexity. Systems that can overcome these challenges without compromising
image resolution or affordability are therefore indispensable to advance fluid dynamics,
aerosol, and biological research, among others. In fact, they are a essential in challenging
flow scenarios such as laminar-turbulent transition in the hypersonic boundary
layer, where megahertz frequencies, micron-scale resolution, and a depth-of-field extended
to the centimeter range is essential to capture high frequency and 3D small-scale
instabilities with sufficient fidelity.
In this thesis, a unique holographic imaging technique named multi-exposure darkfield
digital inline holography was developed to deliver 3D microparticle tracking at
megahertz frequencies over an extended depth-of-field with high resolution using a
single-camera setup. The proposed system integrates a low-cost nanosecond pulsed
laser and a high-resolution digital camera operating at a prolonged exposure time to
capture multiple particle exposures per image frame. A high-pass spatial frequency
filter is introduced prior to the camera to prevent the saturation of the sensor and allow
the acquisition of full-frame images at megahertz frequencies. The optical system
is accompanied by a deep learning framework that incorporates a physics-based synthetic
hologram generation algorithm and a conditional generative adversarial network
to create a vast dataset of labeled darkfield holograms that are subsequently used to
train a regression convolutional neural network for particle depth estimation. Finally,
the innovation is demonstrated by imaging 300-350 μm tracers in a 12 × 10 × 30 mm3
measurement volume located above a magnetic rod rotating at 3,000 RPM. Particle
trajectories were acquired with a frequency of 750 Hz and spatial resolutions of 2.27
μm and 20 μm in the planar and axial directions, respectively, and used to calculate
statistics on particle velocities for a total of 124 trajectories.
Description
University of Minnesota M.S.M.E. thesis. October 2022. Major: Mechanical Engineering. Advisor: Jiarong Hong. 1 computer file (PDF); vi, 59 pages.
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Grazzini Placucci, Rafael. (2022). Multi-Exposure Darkfield Digital Inline Holography for Ultrafast Microparticle Tracking. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/260110.
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