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Record W2339683930 · doi:10.1504/ijsa.2016.076077

A review on potential use of hydrogen in aviation applications

2016· review· en· W2339683930 on OpenAlexaff
İbrahim Dinçer, Canan Acar

Bibliographic record

VenueInternational Journal of Sustainable Aviation · 2016
Typereview
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsHydrogen productionJet fuelAviationEnvironmental scienceNatural gasHydrogen fuelWaste managementGreenhouse gasHydrogenSteam reformingProduction (economics)Fuel cellsEngineeringChemistryAerospace engineeringEconomics

Abstract

fetched live from OpenAlex

In this paper, utilisation of hydrogen as an alternative aviation fuel is reviewed, along with some past and present-day activities, covering three critical topics of energy consumption, environmental impact and emission related cost. It also evaluates the energy consumptions, environmental impacts, emission-related costs of jet fuel A, natural gas and hydrogen from selected production methods in short and long distance aircrafts. Furthermore, costs and efficiencies of hydrogen production from steam methane reforming and wind, PV, and hydro-based electrolysis are compared for a more detailed comparative assessment. Aviation fuel evaluation results show that hydrogen from hydro- and wind-based electrolysis is a promising clean, efficient and less costly candidate among the selected fuels in this study. The assessment study shows that compared to selected hydrogen production options, hydro-based electrolysis is the most advantageous one.

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 score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.022
GPT teacher head0.309
Teacher spread0.287 · 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

Citations123
Published2016
Admission routes1
Has abstractyes

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