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Digital particle image velocimetry (DPIV): systematic error analysis

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posted on 2011-11-22, 11:38 authored by Edward R.J. Putman
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.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Publisher

© by E. Putman

Publication date

2011

Notes

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

EThOS Persistent ID

uk.bl.ethos.547405

Language

  • en

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    Mechanical, Electrical and Manufacturing Engineering Theses

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