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Record W2032882482 · doi:10.1149/1.2214568

Flow Control in a Fuel Cell Flow Field for Improved Performance and Reliability

2006· article· en· W2032882482 on OpenAlexaff
Mauricio Blanco, David P. Wilkinson, Givon Yan, Haijiang Wang, Henbing Zhao

Bibliographic record

VenueECS Transactions · 2006
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsStack (abstract data type)Flow (mathematics)Current (fluid)Reliability (semiconductor)CathodeCurrent densityPressure dropFlow control (data)Materials scienceMechanicsProton exchange membrane fuel cellVoltageNuclear engineeringAutomotive engineeringPower (physics)Fuel cellsEngineeringComputer scienceElectrical engineeringThermodynamicsChemical engineeringPhysics

Abstract

fetched live from OpenAlex

In this paper, a new approach of changing the flow field active area with changing operating conditions is analyzed and discussed. In order to change the active area, two special cathode plates with interdigitated and serpentine flow fields were designed and tested. Each flow field is divided into six separate sections, which are connected to valves (open or closed manually). Reducing the active area in the low power region increases the effective current density (hence lower voltage) and increases the effective reactant flow and associated pressure drop. However, the absolute current and flow do not change from the normal situation for the full active area. Thus, improved fuel cell stack performance stability is achieved without increasing the parasitic load significantly. In addition, other direct benefits of this new approach for future fuel cell designs could include improved cell-to-cell reactant distribution, reduced low current density failure modes, and overall system advantages.

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 score0.436
Threshold uncertainty score0.333

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 head0.002
GPT teacher head0.161
Teacher spread0.159 · 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

Citations1
Published2006
Admission routes1
Has abstractyes

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