Tanks in series

[parkec3]

Tanks-In-Series Model

Implementation of the tanks in series model within Pybamm. Based on DOI: 10.1149/2.0342001JES.

Type of change

Please add a line in the relevant section of CHANGELOG.md to document the change (include PR #) - note reverse order of PR #s. If necessary, also add to the list of breaking changes.

• [x] New feature (non-breaking change which adds functionality)
• [ ] Optimization (back-end change that speeds up the code)
• [ ] Bug fix (non-breaking change which fixes an issue)

Key checklist:

• [x] No style issues: $ pre-commit run (or $ nox -s pre-commit) (see CONTRIBUTING.md for how to set this up to run automatically when committing locally, in just two lines of code)
• [ ] All tests pass: $ python run-tests.py --all (or $ nox -s tests)
• [ ] The documentation builds: $ python run-tests.py --doctest (or $ nox -s doctests)

You can run integration tests, unit tests, and doctests together at once, using $ python run-tests.py --quick (or $ nox -s quick).

Further checks:

• [ ] Code is commented, particularly in hard-to-understand areas
• [ ] Tests added that prove fix is effective or that feature works

[codecov[bot]]

Codecov Report

All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 99.46%. Comparing base (1e3f139) to head (e121f32). Report is 344 commits behind head on develop.

Additional details and impacted files

@@             Coverage Diff             @@
##           develop    #4310      +/-   ##
===========================================
- Coverage    99.50%   99.46%   -0.05%     
===========================================
  Files          289      290       +1     
  Lines        22146    22242      +96     
===========================================
+ Hits         22037    22123      +86     
- Misses         109      119      +10     

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[parkec3]

I have the model written up in a .py file, but I think there is an issue with domains/discretization. I'm getting this error when I run the following code:

Initially, I got an error of size (400,1) vs (1,1), and then I used pybamm.x_average(param.n.prim.c_init) on the initial conditions for the negative particle concentration, but the size still doesn't match up.

[valentinsulzer]

You need to average in both x- and r-directions, you can use param.n.prim.c_init_av which is already averaged

[parkec3]

It seems to be working now with Chen2020 parameter set.

[rtimms]

Looking good! Have you done a comparison to other models, e.g. using this example?

[kratman]

@parkec3 I am going to close this for now, we can re-open it in the future if you would like