PIVlab - Digital Particle Image Velocimetry Tool for MATLAB

Mehdi (Manager Mechanical & Aerospace Engineering) from MATHWORKS (the company behind Matlab) approached me a while ago and we discussed how they can support the development of PIVlab. PIVlab seems to be have some importance a relevance to MATHWORKS as it is a pretty popular 3rd party toolbox for Matlab. Together, we identified potential areas in PIVlab that could be improved and MATHWORKS then initiated an internal proposal for programming support. They selected the dutch company Vortech.nl for this project, and Maarten then started to optimize the most important part of PIVlab "PIV_fftmulti.m". He significantly improved the speed of calculations (factor 3), and lowered the memory consumption enormously. This especially helps when processing large images with fine grids, and also makes it possible to benefit from parallel processing of large images. This hasn't been possible before, because at some point RAM was full and the hard disk was used to store temporary dat...

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...