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Record W2323901943 · doi:10.1149/1.3502357

3D Multiphase Modeling of PEMFC with Uneven Compression of GDL

2010· article· en· W2323901943 on OpenAlexfundno aff
Liang Qi, Kui Jiao, Aaron Pereira, Xianguo Li

Bibliographic record

VenueECS Transactions · 2010
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsClampingProton exchange membrane fuel cellAnodeMaterials scienceCathodeMembraneComposite materialFinite element methodCompression (physics)PorosityGaseous diffusionMembrane electrode assemblyDeformation (meteorology)Water transportPermeability (electromagnetism)Fuel cellsElectrodeChemical engineeringChemistryWater flowStructural engineeringGeotechnical engineeringMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Mass transport in the proton exchange membrane fuel cell is heavily impacted by deformation of the gas diffusion layer (GDL) due to the resultant changes of porosity and permeability. The effects of deformation, due to uneven compression, on water transport are investigated by using finite element analysis (FEA) and three-dimensional multi-component, multi-phase models. Numerical results indicate that clamping forces have a significant influence on water distribution in the membrane electrode assembly (MEA). Analysis of both membrane water content and liquid water content in the GDL and catalyst layer (CL) show that a higher clamping force helps retain water in the membrane on the anode side but leads to worse water flooding on the cathode side.

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: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.190
Teacher spread0.184 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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Same venueECS TransactionsSame topicFuel Cells and Related MaterialsFrench-language works237,207