Detect non-physical solutions in a statistic way instead of just P or T <= 0#2830
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pcarruscag wants to merge 6 commits into
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Detect non-physical solutions in a statistic way instead of just P or T <= 0#2830pcarruscag wants to merge 6 commits into
pcarruscag wants to merge 6 commits into
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| for (int i = -nSigma; i < nSigma; ++i) { | ||
| const int jump = (i == -1); | ||
| outlierTable.AddColumn(std::to_string(i) + " to " + std::to_string(i + 1 + jump), 12); | ||
| i += jump; |
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Hello, Could you please share which turbulence model it is? |
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It's an outlier when a value is more than twice the standard deviation away from the mean? |
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The default is 5 times the standard deviation, and can be adjusted via the parameters. |
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Proposed Changes
I went back to the HLCRM (everything but the Nacelle's version), and there are areas (sharp corners) behind the pylons where temperature becomes either very low or very high.
Eventually, these unstable points (about 100 out of ~4M) lead to divergence despite the automatic relaxation and CFL adaptation. Solution limiters are not enough, probably because within these regions the solution appears smooth relative to the large changes between points.
So the idea here is to find these points from the mean temperature and standard deviation and treat them as first order; we already had something like this for edges where the reconstructed states become non-physical.
Off by default. I don't have a small enough case that triggers this.
It may not work very well for flows with huge temperature ranges.
PR Checklist
pre-commit run --allto format old commits.