Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.547405
Title: Digital particle image velocimetry (DPIV) : systematic error analysis
Author: Putman, Edward R. J.
Awarding Body: Loughborough University
Current Institution: Loughborough University
Date of Award: 2011
Availability of Full Text:
Access through EThOS:
Access through Institution:
Abstract:
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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.547405  DOI: Not available
Share: