Seedless Schlieren Imaging

[Tom Lukin]

Hi all,

I am new to fluid flow analysis and have been working with Schlieren imaging of 2D soap films. My images are seedless, and I am wondering if that makes using openPIV leagues more difficult, I am not a very skilled coder and if that is the case I will likely pivot to laser doppler velocimetry.

I have been using the openPIV tutorials and other notebook work-throughs, as well as the GUI, but haven't gotten anything that seems to follow the flow.

Any help is appreciated,

Cheers.

Tom.

(i have added the current images I am trying to analysis)

[Nepomnyashchikh, Ivan]

Hi, Tom,

looking at your images, I’m not sure how you are going to get velocity fields from them. You need, at least, some markers for PIV, but your images not just have no markers, but they also have rather bad distortions (due to the soap film) that would, otherwise, make PIV results prone to errors.

You need to seed your flow, if you want velocity fields. If you end up having dense seeding, use PIV (OpenPIV Python package), if you end up having sparse seeding, use PTV (OpenPTV Python package). There exist very different seeding particles, I bet you can find ones that cause negligible distortion to your soap film (or at least quantifiable and controlled distortion).

If you don’t need the fields and is fine with point velocity, then, yes, stuff like LDV will work for you (but I don’t quite remember if LDV requires seeding as well, though).

Ivan

 

From: openpi...@googlegroups.com <openpi...@googlegroups.com> On Behalf Of Tom Lukin
Sent: Sunday, February 2, 2025 3:35 PM
To: openpiv-users <openpi...@googlegroups.com>
Subject: [openpiv-users] Seedless Schlieren Imaging

 

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[Alex Liberzon]

Hi Tom

I'd also suggest first trying much faster recording - e.g. for BOS seedless velocimetry we have used high temporal resultion - then pixel- and sub-pixel displacements are possible to study using optical flow, super-resolution PIV, ensemble PIV and other methods. 

Alex

[Alex Liberzon]

here's the optical flow attempt:

https://deepnote.com/streamlit-apps/72d459a3-e204-4952-ac35-dc43213c8a82?utm_content=f9803084-3a0a-4cab-906b-223756a4947b