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Record W2735726989 · doi:10.1149/08008.0133ecst

On the Limitations of Volume-Averaged Descriptions of Gas Diffusion Layers in the Modeling of Polymer Electrolyte Fuel Cells

2017· article· en· W2735726989 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueECS Transactions · 2017
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Waterloo
FundersU.S. Department of Energy
KeywordsLattice Boltzmann methodsVolume (thermodynamics)Gaseous diffusionWork (physics)DiffusionElectrolyteMechanicsPolymerFinite volume methodStatistical physicsFuel cellsScale (ratio)Materials scienceThermodynamicsChemistryPhysicsChemical engineeringComposite materialEngineeringPhysical chemistry

Abstract

fetched live from OpenAlex

Understanding of the coupled transport processes that occur in thin gas diffusion layers (GDLs) is necessary to develop improved designs. The traditional technique used to model GDLs is the volume-averaged approximation. However, the applicability of this approach has been long questioned, and the error in the results is unclear. In this work, the limitations of GDL volume-averaged models are examined under single-phase conditions. The lattice Boltzmann method is combined with tomography images of carbon-paper GDLs to assess the existence of a representative elementary volume (REV) in terms of various effective properties. Then, the predictions of GDL volume-averaged and pore-scale formulations are compared by using a CFD model of a differential cell. The results show that a REV cannot be clearly defined. This leads to inhomogeneities in the pore-scale model that the volume-averaged model is not able to capture despite the overall flux through the GDL is similar in both cases.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.199
Teacher spread0.171 · 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