Date of Award:
Master of Science (MS)
Mechanical and Aerospace Engineering
Barton L. Smith
In Particle Image Velocimetry (PIV), the number of particle images per interrogation region, or particle image density, impacts the strength of the correlation and, as a result, the number of valid vectors and the measurement uncertainty. Therefore, any a-priori estimate of the accuracy and uncertainty of PIV requires knowledge of the particle image density. An autocorrelation-based method for estimating the local, instantaneous, particle image density is presented. Synthetic images were used to develop an empirical relationship based on how the autocorrelation peak magnitude varies with particle image density, particle image diameter, illumination intensity, interrogation region size, and background noise.
This relationship was then tested using images from two experimental setups with different seeding densities and flow media. The experimental results were compared to image densities obtained through using a local maximum method as well as manual particle counts and are found to be robust. The effect of varying particle image intensities was also investigated and is found to affect the particle image density.
Warner, Scott O., "Autocorrelation-Based Estimate of Particle Image Density in Particle Image Velocimetry" (2012). All Graduate Theses and Dissertations. Paper 1386.
Copyright for this work is retained by the student.