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Record W4241180781 · doi:10.1149/ma2014-02/21/1032

OpenFCST: An Open-Source Mathematical Modelling Software for Polymer Electrolyte Fuel Cells

2014· article· en· W4241180781 on OpenAlexaff
Marc Secanell, Andreas Pütz, Phillip Wardlaw, Valentin Zingan, Madhur Bhaiya, M. S. Moore, Jie Zhou, Chad Balen, Kailyn Domican

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsAutomotive Fuel Cell Cooperation (Canada)University of Alberta
Fundersnot available
KeywordsElectrolyteComputer scienceMaterials scienceChemical engineeringProcess engineeringChemistryEngineeringPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Over the past two decades a myriad of polymer electrolyte fuel cell mathematical models have been proposed in the literature [1]. Fuel cell models with different mass, heat and charge transport, and electrochemical reaction mechanisms have been studied. Multi-scale catalyst layer representations, such as agglomerate models, have also been presented. Even though a large number of mathematical models have been proposed, direct comparisons of different catalyst layer models have seldom been performed. A detailed quantitative assessment of the effect of different physical processes, such as thermal-osmosis in water transport, has also not been performed. Such comparative studies have been difficult to undertake because most polymer electrolyte modeling work in the literature is typically based on different governing equations and/or a different set of fuel cell input parameters. OpenFCST is a finite element method based, open-source, mathematical modeling software for polymer electrolyte fuel cells. The aim of the software is to develop a platform for collaborative development of fuel cell models and for assessing the impact of different models in the literature. The software is well documented, contains several sample models, and it is available for download online at http://www.openfcst.mece.ualberta.ca/. The software currently contains a library of governing equations that includes Fick's law of diffusion, Ohm's law, the adsorbed water transport model proposed by Springer et al. [2], a thermal model including thermal osmosis and heat of sorption, and a database of electrochemical equations including models for multi-step reaction mechanisms for the oxygen reduction reaction and the hydrogen oxidation reaction. The software also contains a database of fuel cell materials including several catalyst layer representations such as a macro-homogeneous catalyst layer, an ionomer-filled agglomerate based catalyst layer, and a water-filled agglomerate based catalyst layer. Using openFCST, a multi-scale agglomerate model framework has been proposed to analyze any type of spherical agglomerate regardless of composition and electrochemical reactions [3]. Using this framework, different agglomerate models such as ionomer-filled [3] and water-filled [4] models can be analyzed under the same macro-scale conditions, e.g., same mass and heat transport models, in order to assess their impact on fuel cell performance and reaction distribution inside the catalyst layer. In this study, several agglomerate models are analyzed using different reaction kinetic models including the use of a cathode multi-step reaction kinetics model. Results show that, in ionomer-filled agglomerate models, the kinetic model used has the largest impact on fuel cell performance predictions, followed by the rate of oxygen dissolution. Proton transport has a negligible effect. A comparison of ionomer and water-filled agglomerate models is also presented. Using openFCST, an MEA model using the two types of agglomerate models is developed assuming a Tafel electrochemical model. Figure 1 shows the overall cell performance, the current produced inside a single agglomerate, and the macroscopic current distribution. The results show that, for a fuel cell with a conventional electrode, i.e. 10µm in thickness, even though the current produced by the agglomerates might be remarkably different (Fig. 2), the predicted cell performance, under most operating conditions, is not significantly affected by the agglomerate model. This is because of a macroscopic rearrangement of the current density as shown in Figure 3. In thin electrodes, the macroscopic rearrangement might not be possible leading to very different results. In summary, an overview of the first open-source, multi-dimensional, finite element based, polymer electrolyte fuel cell software in the literature is presented. The software can be used to analyze any type of membrane electrode assembly. In this presentation, it is used to analyze the effect of different kinetic models, boundary conditions, and agglomerate composition recently proposed in the literature. References [1] Weber, A.Z. and Newman, J. Modeling transport in polymer-electrolyte fuel cells, Chemical Reviews , 104(10):4679-4726, 2004. [2] Springer, T.E. et al. Polymer electrolyte fuel cell model, Journal of the Electrochemical Society , 138(8):2334-2342, 1991. [3] M. Moore et al., Understanding the effect of kinetic and mass transport processes in cathode agglomerates, Journal of the Electrochemical Society , 161(8), 2014. [4] Wang, Q. et al., Structure and performance of different types of agglomerates in cathode catalyst layers of PEM fuel cells, Journal of Electroanalytical Chemistry , 573(1):61-69, 2004.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.052
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0040.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0520.015

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.227
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreSoftware

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2014
Admission routes1
Has abstractyes

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