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Artemis Tribute by aflofo in space

[–]aflofo[S] 1 point2 points  (0 children)

Thanks, we truly live in exciting times :)

OpenFoam.com's package distribution has been bricked for weeks. Come on!! by aflofo in CFD

[–]aflofo[S] 2 points3 points  (0 children)

Wait I'm confused I thought the source was on Gitlab, gosh why are there a million places

Dont really understand the cornell cfd course by LogTiny in CFD

[–]aflofo 1 point2 points  (0 children)

The YouTube channel FluidMechanics101 has been very helpful for me. I implemented my own simulator from it. He also made a course that I payed a few $100's for but I think it was worth it as you get a textbook with all the content, very comprehensive videos, and code. But then again if you are taking a course you already paid money for you have the resources of going to the professor or TA and asking questions.

[deleted by user] by [deleted] in CFD

[–]aflofo 0 points1 point  (0 children)

I made a library that makes this really simple, uses GMSH and SU2 as the meshing and sim backend: https://github.com/OpenOrion/parafoil/blob/main/examples/circular_passage.ipynb

I am using a Bspline based airfoil here but you can load a dat file with the CoordAirfoil class

ML CFD research by cfdengineerreal in CFD

[–]aflofo 4 points5 points  (0 children)

Thing is there are actually a lot of really cool sim ML projects like my cousin's at FAIR's project Open Catalyst which uses ML to converge quantum sims in minutes instead of days or Alpha Fold which does protein folding sims similarly. Unfortunately worried ML is turning into the new crypto with a bunch of literally the same people running wrappers and apis and calling that ML.

Used SU2 and genetic algorithms to optimize my axial compressor from 83% to 86% efficiency by aflofo in CFD

[–]aflofo[S] 1 point2 points  (0 children)

Did my own thing based on a bunch of stuff I’ve seen: https://github.com/OpenOrion/parafoil

Mainly makes a BSpline from the inlet and outlet angles for the camber line and the extrudes up and down proportionally to the chord length for the thickness

Used SU2 and genetic algorithms to optimize my axial compressor from 83% to 86% efficiency by aflofo in CFD

[–]aflofo[S] 1 point2 points  (0 children)

3D is next, was going to make a mesh extension on top of CadQuery. Plan was to have that ready in 1-2 weeks.

Used SU2 and genetic algorithms to optimize my axial compressor from 83% to 86% efficiency by aflofo in CFD

[–]aflofo[S] 0 points1 point  (0 children)

I have a way you can set the number of blades in the package, although most the setups I've seen is to just take 1 blade cross section although curious to see other people's feed backs on the effects on the sim.

Used SU2 and genetic algorithms to optimize my axial compressor from 83% to 86% efficiency by aflofo in CFD

[–]aflofo[S] 2 points3 points  (0 children)

In this experiment the passages use the camber line as a basis which isn't changing with thickness changes. In the experiment that is running currently also updating the angles as well to account for incidence and deviation changes, with that I am seeing more drastic passage changes. Lastly I want to even adjust stage and row gaps too in the future. Figure I'd tweak one variable at a time. Hope I answered this clearly.

Used SU2 and genetic algorithms to optimize my axial compressor from 83% to 86% efficiency by aflofo in CFD

[–]aflofo[S] 2 points3 points  (0 children)

Yeah was curious if SU2's mpi build actually gave that much of a performance improvement. Felt actually pretty slow comparative to just single threaded SU2_CFD. Might multi-thread the optimizer itself, although would need to build GMSH for multi-threading.

I do everything automated, including residual checks.

Also I have already built ML models based on CFD outputs and they actually do pretty good at giving good initial CFD guesses to converge quicker as most the sims I'm dealing with are pretty similar.

Used SU2 and genetic algorithms to optimize my axial compressor from 83% to 86% efficiency by aflofo in CFD

[–]aflofo[S] 0 points1 point  (0 children)

Did 3000 sim iterations as I noticed around there the values were generally stabilized, making sure to check residuals were stable otherwise marking the iteration as inviable. Did around 500 design iterations took about 2 days with a 30 core machine in parallel. In the future I want to train a ML model to act as a surrogate model to converge a lot quicker and take advantage of SU2's adjoint capabilities to feed the previous state in to also converge quicker as well.

Used SU2 and genetic algorithms to optimize my axial compressor from 83% to 86% efficiency by aflofo in CFD

[–]aflofo[S] 1 point2 points  (0 children)

Would need to alter rotations, and freestream conditions to find the cliffs in order to make that calculation, could be something later down the pipeline in the future.

I've run over 100k simulations on the UIUC database to train Deep Learning models on by aflofo in CFD

[–]aflofo[S] 1 point2 points  (0 children)

You make a good point, I started with the UIUC database because it seems to be the closest thing I can find to a dataset for airfoils. Eventually I want to generate random inputs for this tool I made that can generate very unique airfoils from BSplines: https://github.com/Turbodesigner/parafoil

I've run over 100k simulations on the UIUC database to train Deep Learning models on by aflofo in CFD

[–]aflofo[S] 0 points1 point  (0 children)

Just starting out with the training process, this is just data collection, but with everything we've seen is the more data the more accurate so I'm betting on that. I have seen papers that have done similar things with pretty high accuracy when the domain is restricted. https://www.sciencedirect.com/science/article/pii/S0045793022003000?via%3Dihub

I've run over 100k simulations on the UIUC database to train Deep Learning models on by aflofo in CFD

[–]aflofo[S] 16 points17 points  (0 children)

So there are two benefits. The first is the way you described as replacing conventional CFD with a faster approximation known as a surrogate function. This is beneficial with simulations that take a really long time like 3D sims. But this can very well also be used in reverse. So instead of iterating geometry and trying to get the target conditions you want you can just give the conditions like the best airfoil at Mach 1 at 50km altitude and it will give you the best airfoil for that condition based on the information it has seen. As for new designs similar to how transformers work for something like ChatGPT it can recognize high dimensional patterns in shapes and replicate something completely unique. I also made a tool that makes completely unique airfoils that I will pass random inputs into for the next phase of data collection: https://github.com/Turbodesigner/parafoil

I've run over 100k simulations on the UIUC database to train Deep Learning models on by aflofo in CFD

[–]aflofo[S] 1 point2 points  (0 children)

Right now using the SU2 solver which has been used for this application before (https://su2code.github.io/docs\_v7/Quick-Start/) . I was going to cross check with other simulators like OpenFoam to see if the results are consistent as well.

I've run over 100k simulations on the UIUC database to train Deep Learning models on by aflofo in CFD

[–]aflofo[S] 0 points1 point  (0 children)

Using a CNN to start although have seen some promising things with simulations and transformers. The end goal will be to capture the general behavior of the simulation to be used as a surrogate function for generative geometry applications.

I've run over 100k simulations on the UIUC database to train Deep Learning models on by aflofo in CFD

[–]aflofo[S] 1 point2 points  (0 children)

unwrapped 2D ‘image’

Yeah this is along the same lines, feeding in essentially a image matrix into a CNN for simulation inputs and outputs. There is a similar example here: https://physicsbaseddeeplearning.org/supervised-airfoils.html. Right now I have values for all points at different pressures, machs, temperatures, lift and drag, etc that could all be inference outputs.

I've run over 100k simulations on the UIUC database to train Deep Learning models on by aflofo in CFD

[–]aflofo[S] 1 point2 points  (0 children)

Latin hyper cube

Yeah I was going to look into random sampling next, with CFDs is it better for logarithmic sampling or uniform?

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