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Record W3200800360 · doi:10.1149/1945-7111/ac2656

Effects of Distribution Zone Design on Flow Uniformity and Pressure Drop in PEMFC

2021· article· en· W3200800360 on OpenAlexaff
Yu Yuan, Zhigang Zhan, Luyan He, Xiaoxiang Yang, Xiongbiao Wan, Pang‐Chieh Sui, Mu Pan

Bibliographic record

VenueJournal of The Electrochemical Society · 2021
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Victoria
FundersNational Natural Science Foundation of China
KeywordsMechanicsPressure dropVolumetric flow rateFlow (mathematics)Materials scienceProton exchange membrane fuel cellDistribution uniformityFlow coefficientGeometryChemistryPhysicsComposite materialMathematics

Abstract

fetched live from OpenAlex

A typical flow field plate of proton exchange membrane fuel cells has a distribution zone that connects the input ports and main channel region where major reactions take place. In this study, the effects of two distribution zone designs, i.e., the channel-ridge distribution zone (CRDZ) and dot matrix distribution zone (DMDZ), on the gas distribution uniformity and pressure drop are investigated numerically. For the flow fields with CRDZ, the theoretical mass flow rates and pressure distributions are found to agree with numerical results well. The flow rate distributions in the flow fields become more uniform when the turning angle increases to special angle. To design the flow fields with CRDZ, the channels can be arranged with simple geometry lines in the first step and then optimised with CFD considering the actual size to achieve a more uniform distribution and suitable pressure drop. For the flow fields with DMDZ, the uniformity of gas distribution depends on the shunt and dispersion effects of dot matrix, and the rectification and expansion effects of the distribution chamber. With an increase in porosity of DMDZ, the gas distribution gets better, and it is the most uniform when the distribution zone is an empty chamber.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.189

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 head0.003
GPT teacher head0.174
Teacher spread0.171 · 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 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

Citations63
Published2021
Admission routes1
Has abstractyes

Explore more

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