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Record W3039955414 · doi:10.1002/er.5525

Development and evaluation of an integrated solid oxide fuel cell system for medium airplanes

2020· article· en· W3039955414 on OpenAlexaff
Reza Alizade Evrin, İbrahim Dinçer

Bibliographic record

VenueInternational Journal of Energy Research · 2020
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsExergySolid oxide fuel cellYield (engineering)HydrogenProcess engineeringOxideNuclear engineeringHydrogen fuelMaterials scienceFuel cellsOperating temperatureExergy efficiencyMechanical engineeringChemical engineeringThermodynamicsEnvironmental scienceEngineeringChemistryElectrical engineeringComposite materialMetallurgyPhysical chemistryPhysics

Abstract

fetched live from OpenAlex

This paper concerns a solid oxide fuel cell (SOFC) based integrated powering system for a medium-sized airplane and analyzes it thermodynamically for its performance assessment and evaluation. It further investigates the system in relation to aircraft operating conditions and provides the conceptual solutions for heating and cooling at various temperatures. The results of the energy and exergy analyses and performance assessments of the proposed integrated system are presented and discussed. The exergy and energy efficiencies of the main components are calculated and observed for the SOFCs with the maximum values of 84.54% and 80.31%, respectively. The present integrated system has overall energy and exergy efficiencies of 57.53% and 47.18%, respectively. Furthermore, it is found that an increase in the reforming temperature ratio can improve the hydrogen yield. However, when the system is operated at a temperature higher than 800°C, the hydrogen yield decreases since a reverse water gas shift reaction is more pronounced. Moreover, the SOFC shows the best performance at 800°C with a maximum power density of 1.23 W/cm2.

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)0.004
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 score0.019
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 science0.0010.000
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 head0.116
GPT teacher head0.413
Teacher spread0.297 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

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