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January 28, 2021, 13:48 |
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#21 | |
Senior Member
Kira
Join Date: Nov 2020
Location: Canada
Posts: 435
Rep Power: 8 |
Quote:
I now see why my supervisor spent like the majority of his academic life studying particles and their motions/distributions. Much like turbulence, there is still much to discover in this field. |
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January 28, 2021, 15:51 |
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#22 |
Senior Member
Arjun
Join Date: Mar 2009
Location: Nurenberg, Germany
Posts: 1,274
Rep Power: 34 |
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January 28, 2021, 15:57 |
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#23 | |
Senior Member
Arjun
Join Date: Mar 2009
Location: Nurenberg, Germany
Posts: 1,274
Rep Power: 34 |
Quote:
BTW I want to bring attention to one very interesting paper https://www.researchgate.net/publica...eakage_kernels Their approach is is quite interesting, but still marred with problems with moment inversion. Nonetheless it is quite easy to implement but capable. |
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January 28, 2021, 16:12 |
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#24 |
Senior Member
Kira
Join Date: Nov 2020
Location: Canada
Posts: 435
Rep Power: 8 |
Thank you for sharing that paper, Arjun.
From skimming through the article, the math does not look too complex, and the approach was indeed presented insightfully. The moment inversion has always been the problem child, I suppose. I am hoping that, like turbulence, the solution lies with the development of more powerful computers. Today I suppose there are just too many unknowns that we must just assume, but hopefully more powerful computers (and maybe even neural networks) could change more unknowns into certainties. I'll have to read that paper, and possibly some of the references they provide, more in-depth. Thanks again though, it was a good jump-off point for me. |
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January 28, 2021, 17:31 |
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#25 |
Senior Member
Joern Beilke
Join Date: Mar 2009
Location: Dresden
Posts: 501
Rep Power: 20 |
This reminds me of a conversation with Prof. Peric many years ago, when polyhedra were just invented. He told me that they (at cd-adapco) now develop and test all models with polyhedra, because errors in the modeling become visible in the first place. |
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March 9, 2021, 13:19 |
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#26 |
Senior Member
Arjun
Join Date: Mar 2009
Location: Nurenberg, Germany
Posts: 1,274
Rep Power: 34 |
I am attaching some results to demonstrate that making solver stable does not mean that solver shall become less accurate.
I chose two points (mesh1 and mesh2) to give an idea of what type of mesh we are dealing with. These are not the worst cells. I just picked two areas. The plot is for CD coefficient for DriveAir benchmark case that is availble online. The calculation was run with starccm by a japanese company and I am supposed to compare the wildkatze results on the mesh provided with same turbulence model (k Omega in this case). As we can see third order solver can predict the drag very good. PS: I think 11 million is too much mesh but this is what is provided for this so. PS2: The jump at around 1200 iterations is due to the fact that I realized that solver was running with first order upwind for convection term (everything else was third order). It was switched to third order upwind there. |
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