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Record W2087883890 · doi:10.1002/er.1363

A review on macro-level modeling of planar solid oxide fuel cells

2007· review· en· W2087883890 on OpenAlexafffund
C. Özgür Çolpan, İbrahim Dinçer, Feridun Hamdullahpur

Bibliographic record

VenueInternational Journal of Energy Research · 2007
Typereview
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsUniversity of Ontario Institute of TechnologyCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsExergyOxideSolid oxide fuel cellHeat transferOhmic contactModeling and simulationMacroFuel cellsThermodynamicsConvectionMass transferMaterials scienceProcess engineeringMechanical engineeringChemistryComputer scienceChemical engineeringEngineeringAnodePhysicsSimulationElectrodePhysical chemistryMetallurgy

Abstract

fetched live from OpenAlex

In this review paper, a comprehensive literature survey on macro-level modeling of solid oxide fuel cells (SOFCs) is presented. First, the current status of the SOFC modeling is assessed. Second, modeling techniques are discussed in detail. These include the thermodynamics, electrochemistry and heat transfer aspects of the modeling. Thermodynamic relations for pure hydrogen as the fuel and then gas mixture as the fuel are given. Additionally, exergy destructed due to polarizations is shown. Then, modeling equations for ohmic, activation, and concentration polarizations are given. Handling the carbon deposition problem in the modeling is discussed. The inclusion of the convection and radiation heat transfer processes to the modeling is explained. Finally, the models in literature are compared in terms of the methodology used and suggestions for increasing the accuracy of the future models are given. Copyright © 2007 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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.004

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 head0.298
GPT teacher head0.506
Teacher spread0.208 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations112
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Energy ResearchSame topicAdvancements in Solid Oxide Fuel CellsFrench-language works237,207