Over the last 30 years, Particle Image Velocimetry (PIV) has become the most powerful tool to study velocity fields in fluid mechanics. This technique is non-intrusive requiring seeding the flow with small tracer particles. The hardware required for these sophisticated PIV methods is very expensive (CCD or CMOS high-speed cameras and lasers), and the present dissertation aims to develop novel and inexpensive alternatives.
The first part of this work investigates the use of multiple smartphones as a lower-cost Tomographic-PIV system for reconstructing 3D-3C velocity fields. We use colored shadows to imprint two or three different time-steps on the same image in a RGB-backlit configuration. We use commercially available Tomo-PIV software for the calibration, 3-D particle reconstruction, and particle-field correlations, to obtain three velocity components in a volume. The proposed system is tested with a vortex ring and the results are compared to stereoscopic-PIV for error estimations.
We expand this work to a high-speed time-resolved setup to obtain 3D-3C velocity fields in time. This improvement is possible using newer smartphones capable of recording high-speed video at HD resolution. The challenges of using such cameras are presented and tackled. The illumination system, testing flow and image processing is similar to the one presented in the first section. A benchmark of the smartphone system is carried out comparing it to a Tomo-PIV system capable of recording 4K video resolution.
A different approach is proposed to reconstruct a 3D-3C velocity field using a single color video camera. This technique uses chromatic structured light with color-gradients projected perpendicularly with respect to the color camera. Thus, we encode the depth position of the particles with a different wavelength of light. Different light sources are used to produce such color gradients.
Finally, a variation of the previous technique is tested using a single monochromatic camera and structured volumetric illumination with spatially varying intensity profiles. This technique enables us to encode the depth position of every particle in their intrinsic brightness. The proposed system can achieve a depth resolution of 200 levels, i.e., an order of magnitude higher than previously proposed systems.
|Date of Award||Oct 2018|
|Original language||English (US)|
- Physical Sciences and Engineering
|Supervisor||Sigurdur Thoroddsen (Supervisor)|
- Flow Measurements