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Record W2332011646 · doi:10.1149/1.3505466

Two-Phase Flow Pressure Drop Hysteresis under Typical Operating Conditions for a Proton Exchange Membrane Fuel Cell

2010· article· en· W2332011646 on OpenAlexafffund
Ryan Anderson, David P. Wilkinson, Xiaotao Bi, Lifeng Zhang

Bibliographic record

VenueECS Transactions · 2010
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsHysteresisPressure dropProton exchange membrane fuel cellMaterials scienceStoichiometryDrop (telecommunication)Two-phase flowFlow (mathematics)Analytical Chemistry (journal)Phase (matter)ThermodynamicsMechanicsChemistryMembraneChromatographyElectrical engineering

Abstract

fetched live from OpenAlex

Two-phase flow pressure drop hysteresis was studied under conditions typical of an operating PEM fuel cell. Two-phase flow hysteresis occurs when the gas and liquid flow rates are increased and decreased along the same path but exhibit different pressure drops. Variables studied include temperature (30-90oC), air stoichiometry (1-4), and gas diffusion layer. The results were analyzed relative to a baseline of fully humidified air at 75oC and a stoichiometry of 2 with a SGL Carbon 25 BC GDL. The percentage change between ascending and descending pressure drop is used to quantify the relative magnitude of the hysteresis. It was found that a sufficient air stoichiometry ({greater than or equal to}4) can reduce the hysteresis, the GDL properties affect the water breakthrough mechanism and shift the onset of the hysteresis zone, and higher temperatures reduce the relative magnitude of the hysteresis effect. These results correlate well with photographs of the cathode channel two-phase flow.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.251
Teacher spread0.240 · 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 designBench or experimental
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

Citations6
Published2010
Admission routes2
Has abstractyes

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