Advanced image analysis for particle image velocimetry. Part II: Ensemble-correlation methods
Willert, Christian (2009) Advanced image analysis for particle image velocimetry. Part II: Ensemble-correlation methods. In: VKI Lecture Series Monographs "Recent advances in particle image velocimetry", LS 2009-01. von Karman Institute for Fluid Mechanics. ISBN 978-2-930389-89-3.
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The ensemble correlation approach to particle image velocimetry (PIV) processing relies on averaging the correlation planes of many images before performing a peak search and subsequent estimation of the average displacement. Generally this processing method has found wide-spread use for the analysis of sparsely seeded micro-PIV image data and can achieve resolution at the single pixel level if the number of images is sufficiently large. The article investigates the potential of the ensemble correlation approach for macroscopic applications driven by two primary motivations: (1) to increase processing speed for the retrieval of mean and fluctuating components, and (2) to provide an estimator to aid in the processing of the individual frames at a later stage. The ensemble correlation algorithm, implemented as a coarse-to-fine grid refinement approach with adaptive sampling window offset, provides mean velocity data at least one order of magnitude faster than the conventional frame-by-frame PIV interrogation schemes and shows rapid convergence and minimal deviation from averaged data obtained by conventional PIV. Estimates of the fluctuation terms (RMS-values) are derived using a modified peak detection and fitting algorithm based on least squares fitting an elliptically shaped Gaussian distribution. The method is demonstrated on PIV recordings obtained from a subsonic free air jet and compared to results obtained by conventional (pair-by-pair) PIV processing. Mean displacements are matched within the expected uncertainty of PIV processing except in regions of increased turbulence which exhibit a systematic bias. At this point fluctuating components can only be roughly estimated but can be used the limit the peak search area while the processing of the individual frames at a later stage. A second example demonstrates the method on PIV recordings obtained from the flow of a transonic compressor. Aside from recovering the mean displacement an order of magnitude faster than conventional PIV processing the sampling resolution could be improved roughly 8 times (32 x 32 vs. 12 x 12 pixel) allowing much a higher spatial resolution of compression shocks.
|Document Type:||Book Section|
|Title:||Advanced image analysis for particle image velocimetry. Part II: Ensemble-correlation methods|
|In ISI Web of Science:||No|
|Publisher:||von Karman Institute for Fluid Mechanics|
|Series Name:||VKI Lecture Series Monographs "Recent advances in particle image velocimetry"|
|Keywords:||PIV, flow field diagnostics, displacement estimation, cross-correlation, ensemble correlation, correlation-based pattern matching, correlation averaging, adaptive processing, multi-resolution analysis, sub-pixel estimation, correlation peak|
|HGF - Research field:||Aeronautics, Space and Transport|
|HGF - Program:||Aeronautics|
|HGF - Program Themes:||L ER - Engine Research|
|DLR - Research area:||Aeronautics|
|DLR - Program:||L ER - Engine Research|
|DLR - Research theme (Project):||L - Virtual Engine and Validation Methods|
|Institutes and Institutions:||Institute of Propulsion Technology|
Institute of Propulsion Technology > Engine Measurement Systems
|Deposited By:||Dr.phil. Christian Willert|
|Deposited On:||19 Oct 2009 09:32|
|Last Modified:||19 Oct 2009 09:32|
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