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19 changes: 17 additions & 2 deletions joss.06531/10.21105.joss.06531.crossref.xml
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<depositor_name>JOSS Admin</depositor_name>
<email_address>[email protected]</email_address>
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R., Robinson, M., &amp; Chapman, S. J. (2021). Python battery
mathematical modelling (PyBaMM). Journal of Open Research Software,
9(1). https://doi.org/10.5334/jors.309</unstructured_citation>
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<citation key="kirby2018efficient">
<article_title>Solver composition across the PDE/linear
algebra barrier</article_title>
<author>Kirby</author>
<journal_title>SIAM Journal on Scientific
Computing</journal_title>
<issue>1</issue>
<volume>40</volume>
<doi>10.1137/17M1133208</doi>
<cYear>2018</cYear>
<unstructured_citation>Kirby, R. C., &amp; Mitchell, L.
(2018). Solver composition across the PDE/linear algebra barrier. SIAM
Journal on Scientific Computing, 40(1), C76–C98.
https://doi.org/10.1137/17M1133208</unstructured_citation>
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<citation key="Mitusch2019">
<article_title>Dolfin-adjoint 2018.1: Automated adjoints for
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29 changes: 23 additions & 6 deletions joss.06531/paper.jats/10.21105.joss.06531.jats
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element method is required to use EchemFEM. Firedrake is preferred
over FEniCS and FEniCSx for several reasons: it offers a custom
preconditioning interface
(<xref alt="Mitusch et al., 2019" rid="ref-Mitusch2019" ref-type="bibr">Mitusch
et al., 2019</xref>), Firedrake continues to be actively developed
unlike FEniCS, and unlike FEniCSx, it already includes automatic
adjoint capabilities.</p>
(<xref alt="Kirby &amp; Mitchell, 2018" rid="ref-kirby2018efficient" ref-type="bibr">Kirby
&amp; Mitchell, 2018</xref>), Firedrake continues to be actively
developed unlike FEniCS, and unlike FEniCSx, it already includes
automatic adjoint capabilities.</p>
<p>The repository includes several examples of electrochemical devices
such as flow reactors, flow batteries, and CO2 electrolyzers.</p>
</sec>
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available in Firedrake. A plethora of solver options are available
through simple PETSc keywords and custom operators for preconditioning
can be defined using Firedrake
(<xref alt="Mitusch et al., 2019" rid="ref-Mitusch2019" ref-type="bibr">Mitusch
et al., 2019</xref>). In Roy et al.
(<xref alt="Kirby &amp; Mitchell, 2018" rid="ref-kirby2018efficient" ref-type="bibr">Kirby
&amp; Mitchell, 2018</xref>). In Roy et al.
(<xref alt="2023" rid="ref-roy2023scalable" ref-type="bibr">2023</xref>),
scalable block preconditioners were developed for the electroneutral
Nernst-Planck equations with DG and implemented in EchemFEM.</p>
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<article-title>OpenFOAM</article-title>
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<pub-id pub-id-type="doi">10.5334/jors.309</pub-id>
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<ref id="ref-kirby2018efficient">
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<article-title>Solver composition across the PDE/linear algebra barrier</article-title>
<source>SIAM Journal on Scientific Computing</source>
<year iso-8601-date="2018">2018</year>
<volume>40</volume>
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