Digital Particle Image Velocimetry (DPIV) is a flow diagnostic technique that is able
to provide velocity measurements within a fluid whilst also offering flow
visualisation during analysis. Whole field velocity measurements are calculated by
using cross-correlation algorithms to process sequential images of flow tracer
particles recorded using a laser-camera system. This technique is capable of
calculating velocity fields in both two and three dimensions and is the most widely
used whole field measurement technique in flow diagnostics. With the advent of
time-resolved DPIV it is now possible to resolve the 3D spatio-temporal dynamics of
turbulent and transient flows as they develop over time. Minimising the systematic
and random errors associated with the cross-correlation of flow images is essential in
providing accurate quantitative results for DPIV.
This research has explored a variety of cross-correlation algorithms and techniques
developed to increase the accuracy of DPIV measurements. It is shown that these
methods are unable to suppress either the inherent errors associated with the random
distribution of particle images within each interrogation region or the background
noise of an image. This has been achieved through a combination of both theoretical
modelling and experimental verification for a uniform particle image displacement.
The study demonstrates that normalising the correlation field by the signal strength
that contributes to each point of the correlation field suppresses both the mean bias
and RMS error. A further enhancement to this routine has lead to the development
of a robust cross-correlation algorithm that is able to suppress the systematic errors
associated to the random distribution of particle images and background noise.
A Doctoral Thesis. Submitted in partial fulfillment of the requirements for the award of Doctor of Philosophy of Loughborough University.