The benefit of ensemble correlation in PIVlab v2.30

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 seeded images, and averages the resulting correlation matrices. This results in a much better signal-to-noise ratio, and very high vector resolution for very low particles densities is possible.
Ensemble correlation is now fully integrated in PIVlab v2.30:

And here, you can see how the quality increases with increasing number of images:

7 image pairs
20 image pairs
90 image pairs
500 image pairs

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