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Record W1987874557 · doi:10.1002/er.1717

Numerical simulation of laminar flow development with heat and mass transfer in PEM fuel cell flow channels having oxygen and hydrogen suction at one channel wall

2010· article· en· W1987874557 on OpenAlexaff
Hassan Hassanzadeh, Xianguo Li, J.J. Baschuk, S. H. Mansouri

Bibliographic record

VenueInternational Journal of Energy Research · 2010
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNusselt numberLaminar flowProton exchange membrane fuel cellMass transferMechanicsHeat transferSherwood numberMaterials scienceThermodynamicsChemistryTurbulenceReynolds numberMembranePhysics

Abstract

fetched live from OpenAlex

Numerical simulation has been carried out of the fluid flow, heat and mass transfer for the developing laminar flow in polymer electrolyte membrane (PEM) fuel cell cathode and anode flow channels, respectively. Each flow channel is considered to be composed of two parallel walls, one porous (simulating electrode surface) and one non-porous, or impermeable, wall (simulating bipolar plate surface). Various flow situations have been analyzed, and the local and the averaged friction coefficient, Nusselt number for heat transfer and Sherwood number for mass transfer are determined for various flow conditions corresponding to different stoichiometries, operating current densities and operating pressures of the cell. The effect of suction or injection (blowing) wall boundary condition has also been investigated, corresponding to the oxygen consumption in the cathode and hydrogen consumption in the anode. Correlations for the averaged friction coefficient, Nusselt and Sherwood numbers are developed, which can be useful for PEM fuel cell modeling and design calculations. Copyright © 2010 John Wiley & Sons, Ltd.

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 score0.041
Threshold uncertainty score0.326

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 head0.019
GPT teacher head0.252
Teacher spread0.234 · 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

Citations9
Published2010
Admission routes1
Has abstractyes

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