SA rough

[DavidAnderegg]

Purpose

This extends the SA model for rough walls as described here.

How to use

To activate the rough variant, those ADflow options must be present:

"useRoughSA": True,
"useBlockette": False,

The surface roughness may be prescribed using cgns_utils overwriteBC with the following example bcfile:

4 kLow bcwall le BCDataSet_1 BCWall Dirichlet SandGrainRoughness 1e-3

Alternatively, the ADflow-method setSurfaceRoughness(self, ks, groupName=None) may be used.

Known bugs

The proposed changes work, but two bugs are still present:

• The effect of the specified surface-roughness is to little compared to SU2:

• The effect of the specified surface-roughness is to little compared to experiments:

• The predicted shape of the effect seems correct.

• The AD-gradients only agree to a relative tolerance of ~1e-8 compared to complex step.

All of this information may be found to more detail in the report i had to write.

Expected time until merged

This depends on the mdo-team. Although there are two bugs, I would argue this feature is already usable. I spent a lot of time trying to find the errors, but i was unsuccessful. My intention is to do something else and later come back and fix it.

Type of change

• Bugfix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (non-backwards-compatible fix or feature)
• Code style update (formatting, renaming)
• Refactoring (no functional changes, no API changes)
• Documentation update
• Maintenance update
• Other (please describe)

Testing

I used the tutorial wing and roughened its surface. Based on this, I introduced three tests:

• Add a new instance to test_adjoint.py with the rough wing.
• Add a new instance to test_functionals.py with the rough wing.
• Add a new instace to test_functionals.py where the rough SA variant is activated. But the restart and reference files are from the test rans_tut_wing. This makes sure SA rough with a roughness value of 0 is consistent to the regular SA model.

Checklist

• I have run flake8 and black to make sure the Python code adheres to PEP-8 and is consistently formatted
• I have formatted the Fortran code with fprettify or C/C++ code with clang-format as applicable
• I have run unit and regression tests which pass locally with my changes
• I have added new tests that prove my fix is effective or that my feature works
• I have added necessary documentation

[codecov]

Codecov Report

Attention: Patch coverage is 31.25000% with 11 lines in your changes are missing coverage. Please review.

Project coverage is 41.28%. Comparing base (de01140) to head (a784190).

❗ Current head a784190 differs from pull request most recent head 156b582. Consider uploading reports for the commit 156b582 to get more accurate results

Files	Patch %	Lines
adflow/pyADflow.py	31.25%	11 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #259      +/-   ##
==========================================
- Coverage   41.49%   41.28%   -0.21%     
==========================================
  Files          13       13              
  Lines        4066     4021      -45     
==========================================
- Hits         1687     1660      -27     
+ Misses       2379     2361      -18     

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