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Substantiation of the ratio of biofuel and kerosin in the mixture for its application as aviation fuel

2020· article· en· W3038172359 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCivil Aviation High TECHNOLOGIES · 2020
Typearticle
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsInternational Civil Aviation Organization
Fundersnot available
KeywordsKeroseneAviationAviation fuelJet fuelQuality (philosophy)Civil aviationRenewable fuelsEngineeringFossil fuelPetroleumProduction (economics)Petroleum productEnvironmental scienceWaste managementEnvironmental economicsAerospace engineeringEconomicsChemistry

Abstract

fetched live from OpenAlex

Today, technologies for the production of alternative fuels and for the development of engines on different operating principles are actively developing, due to both the tightening of the environmental requirements of ICAO (International Civil Aviation Organization) for harmful emissions into the atmosphere and the depletion of non-renewable resources, and the interests of the oil importing countries. Strict requirements are imposed on the quality of aviation fuels related to ensuring the reliability of aviation technology and flight safety. Requirement toughening for quality indicators will inevitably lead to higher fuel prices, so today we can observe some concessions in domestic and foreign regulatory documents to certain quality indicators of aviation fuels, for example, to indicators of low-temperature properties. It follows that the use of petroleum fuels will sooner or later become inappropriate. Technologies to produce synthetic and biological fuels from various types of raw materials make it possible to obtain fuel with close quality indicators to traditional kerosene, but it has not yet been completely replaced. Therefore, today we are considering the use of alternative fuels in a mixture with petroleum kerosene in various proportions. The question remains open: in what proportion is it possible to use mixtures of alternative fuel with kerosene on the aircraft without any negative consequences for their operation. Based on the known dependencies, a mathematical model is proposed for calculating some operational indicators of fuel, engine and aircraft depending on the proportion of mixing alternative fuel and kerosene. In accordance with the calculations, the most rational ratio of petroleum kerosene and SPK fuel is substantiated both from the point of view of the necessary operational properties and from the point of view of economic feasibility.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.237
Teacher spread0.219 · 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