Date of Award

Spring 5-1-2011

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computing

School

Computing Sciences and Computer Engineering

Committee Chair

Andrew Strelzoff

Committee Chair Department

Computing

Committee Member 2

Ray Seyfarth

Committee Member 2 Department

Computing

Committee Member 3

Todd Holland

Committee Member 3 Department

Marine Science

Committee Member 4

Beddhu Murali

Committee Member 4 Department

Computing

Committee Member 5

Joe Calantoni

Committee Member 5 Department

Marine Science

Abstract

A modular particle image velocimetry program was developed and optimized to read and process video of river surface flows from different sensor types. The program was designed for long-term deployment with the ability to sample data continuously in realtime and save the results in a compact format. The time needed to compute a velocity measurement from video input was reduced by using concurrent processing techniques, multi-threading, and a graphics hardware-based correlation algorithm. When used to process field data on a low power Intel Atom based computer the PIV system was capable of computing up to 64 velocity measurements at a rate of 7.5 frames per second. A more powerful computer equipped with a discrete GPU was capable of computing 4800 velocity measurements at a rate of 7.5 frames per second when using the same PIV data and settings. Processing speed of the GPU correlation module was analyzed using a number of different benchmarks. Results show that the GPU-based correlation algorithm has the potential to improve the PIV processing speed of high-end workstations by as much as 2x and low-end portable computers by 10-20x. Methods were also introduced to improve the quality of PIV measurements on river currents. Processing video of river currents with the standard particle image velocimetry technique produced a large number of inaccurate vectors. Most of these inaccurate vectors were correctly identified and removed by using different confidence scoring and filtering techniques. Results from three different experiments were compared to the velocity measurements of other devices to verify the accuracy of the program. These measurements agree to within 16% difference. These results show that accurate PIV measurements of river surface velocity may be computed in real time even on low end and portable computer hardware.

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