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Investigation of Potential Fuels for Hybrid Molten Carbonate Fuel Cell-Based Aircraft Propulsion Systems

2021· article· en· W3170956094 on OpenAlexafffund
Shaimaa Seyam, İbrahim Dinçer, Martin Agelin‐Chaab

Bibliographic record

VenueEnergy & Fuels · 2021
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTurbofanMolten carbonate fuel cellCombustorEnvironmental scienceSteam reformingPropulsionDimethyl etherWaste managementDiesel fuelProcess engineeringHydrogenMethanolCombustionEngineeringAutomotive engineeringChemistryHydrogen productionAerospace engineering

Abstract

fetched live from OpenAlex

Alternative fuels and innovative powering systems are recognized as essential for clean and sustainable aviation practices, performance improvements and emission reductions. This paper proposes a new hybrid turbofan and molten carbonate fuel cell (MCFC) system consisting of a steam reformer, a water gas shift reactor, and a catalytic burner. Five potential fuels are employed, including ethanol, methane, hydrogen, dimethyl ether, and methanol with different mass fractions to constitute five fuel blends. Thermodynamic analyses are conducted to study the proposed hybrid MCFC-turbofan performance, and the results are compared with a kerosene-based turbofan. It is found that a traditional turbofan performance can produce 42 MW with 59% energetic efficiency and 71% exergetic efficiency, whereas the hybrid MCFC turbofan can produce 40 MW by mixing all the five alternative fuels, but with higher performance of 65% and 80% energetic and exergetic efficiencies, respectively. Also, the CO2 emissions are substantially reduced by 75%. The fuel blends provide better performance and much lower CO2 emissions.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.223
Teacher spread0.210 · 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.

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

Citations14
Published2021
Admission routes2
Has abstractyes

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