Since quite a while, I benefit from discussions with the main developer of OpenPIV (Prof. Alex Liberzon from Tel Aviv University) and other people that contribute to OpenPIV. It is almost a bit like a cooperation, as we share thoughts on different topics, and Alex is really into practical and theoretical PIV application. I think we have a very friendly rivalry in developing useful PIV software. The results are available here: https://github.com/alexlib/openpiv_pivlab_von_Karman_data You will notice that they are very noisy. But we intentionally decided to make the analysis very challenging for our software. The final interrogation area is e.g. only 6*6 pixels, and no smoothing is allowed. I don't really see a difference between OpenPIV and PIVlab, which is probably a good sign. It might be interesting to include commercial software too in this comparison in the future. By the way: Happy New Year to all PIVlab users (and of course all OpenPIV users too ;-D). The year 2020 has been
Recently, I asked Prof. de Payrebrune from the University Kaiserslautern / Computational Physics in Engineering if they could lend me their Chronos 1.4 low-cost high-speed camera for some tests. She agreed to support the development of PIVlab which is very cool and kind of her!! Now I did some tests with the camera (captures data at 1.3 megapixels at 1000 Hz) and my new LD-PS pulsed laser diode. The LD-PS has a built-in synchronizer, so it triggers the Chronos and can also do frame-straddling. The Chronos has absolutely brilliant trigger characteristics, as you have complete control over the exposure timing. Now I am not limited to an interframe-time of 1/1000s anymore, but can go down to 10 µs. When capturing data at 1000 fps, the duty cycle of the illumination becomes quite high. So I added two little fans to the LD-PS housing. Now a duty cycle of up to 50% can be reached without the laser or the driver becoming too hot. The awesome Chronos high-speed camera from krontech.ca Latest
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