Grazzini Placucci, Rafael2024-01-192024-01-192022-10https://hdl.handle.net/11299/260110University of Minnesota M.S.M.E. thesis. October 2022. Major: Mechanical Engineering. Advisor: Jiarong Hong. 1 computer file (PDF); vi, 59 pages.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.enDarkfieldDeep learningHolographyMulti-exposureParticle trackingThesis or Dissertation