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Record W4251622892 · doi:10.1149/ma2014-02/21/1044

Invited: Pore Network Modeling of the Full Membrane Electrode Assembly of a Polymer Electrolyte Membrane Fuel Cell

2014· article· en· W4251622892 on OpenAlexaff
Mahmoudreza Aghighi, Jeff T. Gostick

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsMcGill University
Fundersnot available
KeywordsProton exchange membrane fuel cellMaterials scienceElectrolyteMembrane electrode assemblyMechanicsPercolation theoryThermal diffusivityMultiphase flowElectrodePower densityCapillary actionPorosityWater transportProcess engineeringThermodynamicsChemical engineeringPower (physics)Composite materialChemistryWater flowFuel cellsEngineeringConductivityGeotechnical engineeringPhysics

Abstract

fetched live from OpenAlex

(PEMFCs) are a major component of a sustainable energy economy. Their high energy and power density make them uniquely capable of replacing the internal combustion engine. The porous electrode in the PEMFC must be designed to withstand the presence of liquid water, which is a by-product of the electrochemical reaction. Ensuring high rates of gaseous reactant transport to the catalyst layer is essential to producing high power density, efficient and cost effective cells. Consequently, the study of liquid water behavior in the porous electrodes is of extreme interest. A very large number of numerical models have been published, attempting to use multiphase flow models in PEMFC and computational fluid dynamics packages based on continuum mechanics to optimize the electrode. There are several limitations to the continuum approach that will be discussed. The most obvious is that the transport properties must be measured experimentally, and then input into the computation as constitutive relationships. This is problematic for properties are difficult to measure, such as effective diffusivity in partially water saturated media [1, 2], gas-liquid surface area, etc. An alternative modeling approach that is receiving increased interest is pore network modeling (PNM). In PNMs, the media is mapped as a set of interconnected pores and throats, transport is modeled as a resistor network and capillary behavior is modeled using percolation theory concepts. In this paradigm there is no difficulty modeling the impact of multiphase flow, and importantly, PNMs do not require experimentally measured transport parameters are constitutive relationships. A pore network model produced using the OpenPNM package has been developed to simulate the impact of mass transfer through the GDL network on the electrochemical kinetics and fuel cell operation. OpenPNM is an open source framework implemented in Python. This framework is capable of building a porous structure, applying pore-scale physics, and simulating numerous algorithms on pore network models. In this work, constant voltage boundary conditions have been applied to the catalyst layer to predict overvoltage in proton exchange fuel cell systems, as shown by the polarization curve in Figure 1. As can be seen, the mass transfer losses place an upper limit on the maximum current that can be generated in a cell. Only mass transfer losses in the GDL are considered, but there is no theoretical reason why the catalyst layer can cannot be included in future work. This approach can be extended to include ionic losses in the membrane phase as well. The eventual goal is to provide a fully viable alternative to the continuum model approach. References 1. J. T. Gostick, M. A. Ioannidis, M. W. Fowler, M. D. Pritzker, J. Power Sources 194, 433 (2009). 2. J. T. Gostick, M. A. Ioannidis, M. W. Fowler, M. D. Pritzker, in Modern Aspects of Electrochemistry, C. Y. Wang, U. Pasaogullari, Eds. (Springer, Berlin, 2010), vol. 49. Acknowledgements This work was funded by AFCC and the NSERC CRD program.

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.004

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 head0.006
GPT teacher head0.179
Teacher spread0.174 · 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
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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