Abstract
A novel algorithm to analyse PIV images in the presence of strong in-plane displacement gradients and reduce sub-grid filtering is proposed in this paper. Interrogation windows subjected to strong in-plane displacement gradients often produce correlation maps presenting multiple peaks. Standard multi-grid procedures discard such ambiguous correlation windows using a signal to noise (SNR) filter. The proposed algorithm improves the standard multi-grid algorithm allowing the detection of splintered peaks in a correlation map through an automatic threshold, producing multiple displacement vectors for each correlation area. Vector locations are chosen by translating images according to the peak displacements and by selecting the areas with the strongest match. The method is assessed on synthetic images of a boundary layer of varying intensity and a sinusoidal displacement field of changing wavelength. An experimental case of a flow exhibiting strong velocity gradients is also provided to show the improvements brought by this technique.
Highlights
Standard analysis of PIV images generally involves the division of sequential recordings into smaller interrogation areas and estimating the displacement of recorded particle images captured within those areas by means of cross-correlation
Correlation maps in PIV image analyses can present multiple peaks when strong gradients in particle image displacement are encountered within interrogation windows
Selected peaks are analysed in sequence to understand which areas of the interrogation window contribute to which identified correlation peak
Summary
Standard analysis of PIV images generally involves the division of sequential recordings into smaller interrogation areas and estimating the displacement of recorded particle images captured within those areas by means of cross-correlation. Particle images moving together in the same direction will produce a single strong peak in the cross-correlation map, allowing the retrieval of a representative displacement vector. For this analysis to be conducive, local in-plane displacement gradients must be negligible and an upper limit of 0.05 pixels/pixel is often suggested in literature (Keane and Adrian 1992). Velocity predictors are adopted to deform consecutive image snapshots with the aim of reducing the relative particle images displacement This step is typically combined with a reduction of the window size and a displacement correction using the velocity predictors.
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