Pointwise boundary surface values

[Arpit-Babbar]

This PR add pointwise values on the surface like the coefficient of pressure and friction.

The computation has been verified using the reference data from Roy Charles Swanson, Stefan Langer, Structured and Unstructured Grid Methods (2016)

The verification requires plotting along with reference data, for which the script is available at https://gist.github.com/Arpit-Babbar/924823c08f4be962856a99c107b18fb5

TODO

• Add tests for pointwise surface values (How to do it?)
• The many TODOs within the code

[github-actions]

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Created with ❤️ by the Trixi.jl community.

[Arpit-Babbar]

I currently have two issues

1. The output of pointwise values is saved into a .txt file. Does that need to be changed?

2. The pointwise surface values are currently being computed as analysis_integral quantities in the AnalysisCallback. Where else can they belong? I think that it should be given its own place within the AnalysisCallback, one that does not print any values to the screen (because there are no values to be printed).

[codecov]

Codecov Report

Attention: Patch coverage is 91.41104% with 14 lines in your changes missing coverage. Please review.

Project coverage is 93.88%. Comparing base (480aea9) to head (4745ec8).

Files with missing lines	Patch %	Lines
...rc/callbacks_step/analysis_surface_pointwise_2d.jl	92.24%	9 Missing ⚠️
src/callbacks_step/analysis.jl	82.14%	5 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1920      +/-   ##
==========================================
- Coverage   96.34%   93.88%   -2.46%     
==========================================
  Files         470      471       +1     
  Lines       37497    37626     +129     
==========================================
- Hits        36125    35323     -802     
- Misses       1372     2303     +931     

Flag	Coverage Δ
unittests	93.88% <91.41%> (-2.46%)	⬇️

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[andrewwinters5000]

I did not do a full review, just left a question about the need to add another package dependency. As far as your other concerns here are my preliminary thoughts.

1. A .txt file is fine as ACSII data can be post-processed by nearly any program. However, if we are willing to use DelimitedFiles would something like .csv be better? Just thinking out loud.
2. Since no integral is computed, then, as far as I understand it, the analysis_integral is basically being overloaded for easier dispatch purposes. Then the pointwise values are computed on the fly with similar drag and lift functions and the write-to-file occurs within the loop over all nodes instead of afterwards. My initial reaction is that this could be confusing. It would be better to be more explicit and create a new generic function called something like analysis_pointwise that could still reuse the caches and such from the AnalysisCallback struct.

[DanielDoehring]

I suggest we adapt/copy this code

Trixi.jl/src/callbacks_step/analysis.jl

Lines 583 to 607 in 6554973

	# Iterate over tuples of analysis integrals in a type-stable way using "lispy tuple programming".
	function analyze_integrals(analysis_integrals::NTuple{N, Any}, io, du, u, t,
	semi) where {N}
	# Extract the first analysis integral and process it; keep the remaining to be processed later
	quantity = first(analysis_integrals)
	remaining_quantities = Base.tail(analysis_integrals)
	res = analyze(quantity, du, u, t, semi)
	if mpi_isroot()
	@printf(" %-12s:", pretty_form_utf(quantity))
	@printf(" % 10.8e", res)
	@printf(io, " % 10.8e", res)
	end
	mpi_println()
	# Recursively call this method with the unprocessed integrals
	analyze_integrals(remaining_quantities, io, du, u, t, semi)
	return nothing
	end
	# terminate the type-stable iteration over tuples
	function analyze_integrals(analysis_integrals::Tuple{}, io, du, u, t, semi)
	nothing
	end

to non-integrated quantities, i.e., some which are not returning a scalar value possibly like this:

# Iterate over tuples of analysis integrals in a type-stable way using "lispy tuple programming".
function analyze_pointwise(analysis_quantities::NTuple{N, Any}, io, du, u, t,
                           semi) where {N}

    # Extract the first analysis integral and process it; keep the remaining to be processed later
    quantity = first(analysis_quantities)
    remaining_quantities = Base.tail(analysis_quantities)

    analyze(quantity, du, u, t, semi)

    # Recursively call this method with the unprocessed integrals
    analyze_integrals(remaining_quantities, io, du, u, t, semi)
    return nothing
end

and then call this here:

Trixi.jl/src/callbacks_step/analysis.jl

Lines 487 to 488 in 6554973

	# additional integrals
	analyze_integrals(analysis_integrals, io, du, u, t, semi)

[DanielDoehring]

Regarding the type of file, I think we should aim for something that is easily post-processed, as now one does not get a single value but instead many, which are most likely also accompanied by spatial positions.

[sloede]

Regarding the type of file, I think we should aim for something that is easily post-processed, as now one does not get a single value but instead many, which are most likely also accompanied by spatial positions.

I recommend to take a look at how I/O is implemented in the TimeSeriesCallback, which allows to record data at each time step at a given set of "recording points". We use an internal caching infrastructure and write all data to HDF5 files. There's also the output of the AnalysisCallback, which writes integral quantities to an ASCII file using CSV formatting.

Overall, the decision on which file format to use IMHO depends heavily on data set size: If either the number of recording points or the number of recordings is at most 10-20, an ASCII file might still work. However, if you want to record at, say, 100 points, and do so for 1000 times over the course of a simulation, I'd probably opt for an HDF5 file.

[DanielDoehring]

We should put this on hold until #1959 is tackled / we decide how we address #1955

[DanielDoehring]

@Arpit-Babbar : I did some changes to be AMR-ready, see #1959 . Now the test fail, however. Can you take a look if we still match the reference data or if I messed something up?

[Arpit-Babbar]

Looking good to me! As I was involved in the coding, it would be nice to get an additional review, preferably by someone who coded up some h5 export as well, such as @sloede and/or @andrewwinters5000

These are the plots from Arpits gist:

I regenerated the data and saw that both the plots look exactly the same as this. Did we remove the reordering (following Michael's suggestion?)? That will need the CI to be updated.

[DanielDoehring]

I regenerated the data and saw that both the plots look exactly the same as this. Did we remove the reordering (following Michael's suggestion?)? That will need the CI to be updated.

That is good!

Yes, the sorting is not longer done.
Take a look at the test files/CI test, I now sort there, but the values do not agree. This is why I asked you to check this again :)

[Arpit-Babbar]

Yes, the sorting is not longer done. Take a look at the test files/CI test, I now sort there, but the values do not agree. This is why I asked you to check this again :)

I see it now. The sorting

    @test sort(cp_vals[1:10]) ≈ [1.5152278762705806
           1.5182860925755697
           1.7417814818119175
           1.871513931283643
           1.874247697759559
           2.0354491762572806
           2.0400855926847936
           2.0956920203526193
           2.098639791879171
           2.1591959225932777]

does not look equivalent to sorting along the increasing x direction. For that, you will have to sort along read(Trixi.h5open("out/CP_x_000007.h5"))["point_coordinates"][:,1]

[DanielDoehring]

I see it now. The sorting
does not look equivalent to sorting along the increasing x direction.

Yes, but this is also not intended. As you can see, the reference data is also sorting in ascending order, so I seek to do the same with the data, as I am not sure whether the order of the indices is deterministic.

We match some values, while some we miss, see : https://github.com/trixi-framework/Trixi.jl/actions/runs/9810227214/job/27089931612?pr=1920#step:7:8617

[DanielDoehring]

I guess we could also move this into a package called TrixiAero or something similar that implements this specialized analysis routines, thereby not polluting the analyze file even more.