Thesis-2010-Wormald.pdf (10.45 MB)
Download file

Numerical techniques in digital microscopic holographic particle image velocimetry

Download (10.45 MB)
posted on 15.11.2010, 08:59 authored by S. Andrew Wormald
Digital microscopic holographic particle image velocimetry (DµHPIV) is a technique which records scattered coherent light and uses it to measure displacement of particles in a fluid flow. The work in this thesis begins with the construction of a digital holographic microscope and explores the different possible methods of recording and holographic reconstruction, finding an off-axis forward-scatter geometry to be most suitable for the task. A comparison follows of methods to measure displacement in a sparsely seeded environment by performing a simple experiment. It finds that complex amplitude correlation performs significantly better than both intensity correlation and nearest neighbour analysis; the two other possible methods of displacement tracking. Later, an experiment is performed to investigate the behaviour of a microfluidic blood separator. The separator is intended to remove blood plasma from whole blood without other contaminants such as red blood cells and without the need for expensive laboratory equipment. In this chapter a new technique, higher order correlation, is introduced which can be used to strengthen the peaks in correlations of three or more particle images in a flow, and a potential flow CFD model of the separator is built from scratch to predict whether the separator will work, and against which the results can be compared. Finally, there is an experiment carried out which for the first time allows aberration free imaging within objects with irregular, highly curved surfaces; in this case optical fibres and inkjet droplets, by numerically reconstructing the droplet surface.



  • Mechanical, Electrical and Manufacturing Engineering


© S. Andrew Wormald

Publication date



A Doctoral Thesis. Submitted in partial fulfillment of the requirements for the award of Doctor of Philosophy of Loughborough University.

EThOS Persistent ID



Usage metrics