Відео

In your setup, you have the cameras below looking "up" into the laser, would it work just as well if they were looking "down" the laser?

Truly exceptional teaching. If only more lecturers had your skills!

Always been skeptical of the derivation of pressure from the fluid velocity via PIV but tbh it's been above my knowledge. This method makes a lot of sense! I'll share with my colleague doing PIV!

So relaxing, I am falling asleep

Explanation=0👎

Amazing video!

Excellent Video and pretty easy and clear to understand the operations of blocks due to visualization!!

Don't remember, when I hit like on a youtube video the last time. Probably years ago. But this one deserves one.

nice! i used a fully digital implementation of this on a microcontroller. it means i can have a microcontroller be sending an approximation of a floating point value (from a pid controller) to drive a relay, but only switching at the cycle period at most :) in this case switching eventually leads to mechanical wear, so having something that switches less when further from 50% is very nice.

Nice. I wanna add those led infinite mirrors on mine to make the basisc portal man.

Sir, the .mex file is missing in Github, and the file has also been removed from Matlab works...

Link to the Github Open-Source code: github.com/3dfernando/pressure-osmosis If you use our code, I'd appreciate you cite us! Zigunov & Charonko (2024, ArXiv) "One-shot omnidirectional pressure integration through matrix inversion" arxiv.org/pdf/2402.09988 Zigunov & Charonko (2024, Measurement Science and Technology) "A fast, matrix-based method to perform omnidirectional pressure integration" iopscience.iop.org/article/10... DOI 10.1088/1361-6501/ad2da5

This is a talk I gave at LxLaser 2024 on a work I've done at Los Alamos National Lab. If you have interest or any questions about the work, feel free to comment or send me an e-mail! Link to the Github Open-Source code: github.com/3dfernando/pressure-osmosis Referenced papers: Zigunov & Charonko (2024, ArXiv) "One-shot omnidirectional pressure integration through matrix inversion" arxiv.org/pdf/2402.09988 Zigunov & Charonko (2024, Measurement Science and Technology) "A fast, matrix-based method to perform omnidirectional pressure integration" iopscience.iop.org/article/10.1088/1361-6501/ad2da5/meta DOI 10.1088/1361-6501/ad2da5

great Explaination Can you please tell me how to calculate the value of Sampling and feedback capacitor in 2nd order SD-ADC 2MHz samplig frequency and NFFT is 8192.

What does it mean by spatial patterns where the data is most temporally correlated? Like the spatial patterns across consecutive time? Or across like periodically when a time step repeats if something is periodic..?

Great work!

So all waves in the universe behave the same. Light and darkness, sound and stillness all the same just have different weight. That section where sound expand but I also contracts to something the steady motion it was in... Nothing does coming from something nothing is something.

Good explanation, but just one note. It will be easy for understanding if the final counter will be signed and bidirectional (up/down counter), so when the analog input is near zero and output of the comparator is "+ - + - + - ...." the output of accumulator is about zero too. I know that it is just a matter of signed and unsigned numbers, but anyway :)

silicon detectors in phones can see light out to about 1100nm. They put filters in to stop it on good cameras. Sometimes the filters can't block all the IR

Well you're explaining what is happening operationally (this specific form) but not the why or how it does it's actually function of "noise shaping". That being said the theory would be beyond most.

Great video Fernando. Is there a website that I can view relevant code to the fluid dynamic example you showed? I would love to explore that more. Thanks!

What an amazing explanation. Thank you very much!

Amazing as always

excellent visualization! Very helpful!💖

Great explanation and visualization!

Impressive!

Impressive! How is the sound related to the shown shadowgraphs?

Yes, this is the raw footage from the entire memory of our Phantom T2410 camera, because WHY NOT? Jet velocity is 100m/s (air jet at ambient temperature); and the video captures the shadowgraph of the flow (highlighting the Laplacian of density). The video resolution is reduced because we captured it at 290,000 fps. The total length of the video is 2.3 seconds, which stretch into 6.18 hours when re-played at 30 fps. Compression artifacts are courtesy of UA-cam. I can't do anything about that unfortunately 😐 The noise in the background is actually coming from the video. The intensity of each pixel is used as a small microphone to produce a sound wave that is slowed down to audible frequencies. Multiple pixels are used to make the length of the audio match the video; but it is all procedurally generated. The pictures of the shear layer turbulence are so mesmerizing that I had to share this with the world. Maybe you can use this as a background for study or sleep, who knows? 😂

Crazy

Whaaat

Damn that's insane

Great video and all but man why the 2 girls 1 cup music

Thanks for checking this short! Links to the referred papers: "A continuously scanning spatiotemporal averaging method for obtaining volumetric mean flow measurements with stereoscopic PIV" (Zigunov et al, ExIF, 2023) - link.springer.com/article/10.1007/s00348-023-03596-w "A fast, matrix-based method to perform omnidirectional pressure integration" (Zigunov and Charonko, MST, 2024) - iopscience.iop.org/article/10.1088/1361-6501/ad2da5 "One-shot omnidirectional pressure integration through matrix inversion" (Zigunov and Charonko, Under Review, 2024) arxiv.org/pdf/2402.09988.pdf

Thanks! Wouldve been nicer is you show how an analog signal was converted to digital bits by this ADC.

Thanks for checking our short! Here's our two papers on the method. First paper demonstrates the math integrals; second paper eliminates the iterative part of the method to solve for pressure in a single matrix inversion. A fast, matrix-based method to perform omnidirectional pressure integration arxiv.org/abs/2311.16935 One-shot omnidirectional pressure integration through matrix inversion arxiv.org/abs/2402.09988

Hello Fernando, Interesting technique described here. I was wondering whether HFSB's combined with counter illuminating lasers can solve the problem of intensity drop that you are describing. Ofc you would have to switch to particle tracking and multi-double-pulse techniques such as Shake The Box, to resolve high speed flows. Again, the time average would be captured though...

what is the difference between this method and SLS(scanning light sheet)?? Just curious, i am preparing an exam about this stuff

Thanks a lot....This is the best POD explaination I have seen.... Very helpful... Keep it up.

This was really cool! I was wondering if you could tell me how much of each part I need, because I want to replicate this project.

Details on the paper: link.springer.com/article/10.1007/s00348-023-03596-w

Hey nice video man! you always surprise me!

Good description of the basics. I would like to add that Nyquist refers to frequency and not phase (in recreating a representation of the input) if the sampler is asynchronous to the process, which is usually the case. You can prove this to yourself by making a small circuit with a sample and hold and slowly bring up the frequency of the input in relation to the sample frequency. At a sample rate of 1/2 the highest frequency (which is hard to get correct and will wonder in relation to a tightly regulated sample clock), asynchronously the samples are almost void of phase information. So, from a practical standpoint the first task is to preclude aliasing which means a low pass filter in relation to the sample period, with good process signal attenuation of around 40dB minimum. Then in designing the controller you would like the sample rate, to maintain phase, of at best 20x the highest process frequency. Many industrial off-the-shelf PID controllers provide this 20x sample rate, many call it oversampling as a marketing point. Now with this over sampling and elimination of aliasing we can design and tune a classic control law in the Z domain. If there are multiple feedback loops we must have synchronous sampling of all required process variables. From here we can use the classical control system stability criteria.

you're right, very cringe humor lmao

Man this animation was incredible, thankyou!

Great video. Do you need time resolved PIV measurements to conduct spectral POD? Thanks

THanks!

This video is gold.

Very good visualization of the interpretation of the V matrix. I just needed it, thank you

THIS IS AMAZING PLEASE KEEP DOING THIS