PIVlab - Digital Particle Image Velocimetry Tool for MATLAB

PIVlab v2.30 features 'multipass window deformation ensemble correlation'. This type of correlation has been introduced by Santiago et al.  in 1998. It is especially helpful in micron-resolution particle image velocimetry (micro-PIV, µPIV), as it can deal with very low seeding densities (see below). Ensemble correlation PIV should be used if you want to analyze a steady flow that has a low seeding density. You need to record a large number of images to have the full benefit of ensemble correlation in PIVlab. The yellow lines show the interrogation areas (IAs). The white dots are particles, and you can see that there are hardly any particles in the IAs. The general rule of thumb for PIV is that there should be 5 - 15 particles per IA. This is clearly not the case here. Standard PIV algorithms will fail. In order to get a better result, you would have to increase the size of the IAs dramatically. With ensemble correlation, PIVlab analyzes a series of sparsely seed...

The new PIVlab release supports an advanced correlation technique (I want to thank Kyle Lynch for the discussion about this). Using a multipass window deformation technique, significantly reduces noise in the data. The maximal vector resolution can also be increased and peak-locking is reduced a lot. PIV images that have a low quality can now be processed with higher precision (see the video at the bottom). Additionally, it is possible to draw some streamlines and streamline rakes, this is useful for adding some qualitative information to the analyses. The image export procedure has also been improved, it is possible to export the displayed result at any desired resolution (only limited by the RAM on your computer). Standard direct cross-correlation (DCC) analysis using a 14*14px interrogation area with 50% overlap: Multipass, window-deformation using a 6*6px interrogation area with 50% overlap in the final pass: Streamlines: Processing low quality images: The new...