MétaCan
Menu
Back to cohort
Record W2068402528 · doi:10.1115/mnhmt2009-18268

Dynamic Condensation Modelling in PEMFC GDL

2009· article· en· W2068402528 on OpenAlexafffund
James Hinebaugh, Aimy Bazylak

Bibliographic record

VenueASME 2009 Second International Conference on Micro/Nanoscale Heat and Mass Transfer, Volume 2 · 2009
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsNucleationSaturation (graph theory)Proton exchange membrane fuel cellCondensationElectrolyteMaterials scienceMembranePorosityChemical engineeringThermodynamicsChemistryChemical physicsComposite materialPhysical chemistryEngineeringPhysicsElectrode

Abstract

fetched live from OpenAlex

A 2D dynamic pore network model is employed to study the liquid water saturation from condensation originating from a single nucleation site within the gas diffusion layer (GDL) of a polymer electrolyte membrane fuel cell (PEMFC). A complete derivation of the model along with the physical parameters is provided in this paper. Sensitivity analyses are performed to determine how the overall saturation pattern is affected by the following two parameters: condensation rate and nucleation site location. Our results indicate that when considering an initially dry GDL under typical PEMFC operating conditions, fluctuations in condensation rate have little impact on the overall saturation pattern compared to changes in condensation nucleation site location. We observe significant reductions in both overall saturation and local saturation near the catalyst layer as the condensation nucleation site is placed further away from the catalyst layer.

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 score0.022
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueASME 2009 Second International Conference on Micro/Nanoscale Heat and Mass Transfer, Volume 2Same topicFuel Cells and Related MaterialsFrench-language works237,207