MétaCan
Menu
Back to cohort
Record W2007785635 · doi:10.1016/j.egypro.2012.09.081

Performance Evaluation of Metallic Foam Flow Fields

2012· article· en· W2007785635 on OpenAlexafffund
Samuel C. Yew, Mina Hoorfar

Bibliographic record

VenueEnergy Procedia · 2012
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversities Space Research Association
KeywordsMetal foamMaterials scienceCathodeAnodeMoistureDew pointLaminar flowProton exchange membrane fuel cellComposite materialBoundary layerPorosityChemical engineeringMechanicsFuel cellsChemistryElectrical engineeringElectrodeThermodynamicsEngineering

Abstract

fetched live from OpenAlex

Water management is one of the major areas of interest in the fuel cell research community. Previous studies have shown enhancement in water management and fuel cell performance using a parallel flow field design including a metallic foam structure in the channels of the cathode flow field [1] . In this paper, metallic foams are used in a new geometry and are applied to both cathode and anode flow fields to improve water removal, reduce production costs, and enhance the overall performance. The metallic foam increases turbulent mixing in the flow fields which prevents the formation of a laminar boundary layer at the surface of the gas diffusion layer (GDL). The foam also serves as a moisture wick removing liquid water away from the GDL while maintaining the level of moisture required avoiding dehydration of the membrane. In this study, the performance tests are conducted at a variety of dew point temperatures to determine the water management properties of the metallic foam in the new geometry. The performance of the metallic foam will be compared to that of a standard serpentine graphite flow field. The major criteria will include performance at nominal voltage and current and high current stability.

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 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 score0.371
Threshold uncertainty score0.328

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 head0.015
GPT teacher head0.208
Teacher spread0.193 · 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 teacher head, 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
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueEnergy ProcediaSame topicFuel Cells and Related MaterialsFrench-language works237,207