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Record W4296691780 · doi:10.32920/ryerson.14661852.v2

An Investigation Into The Comparability Of Common Jet Engine Materials With Next Generation Biofuels

2022· preprint· en· W4296691780 on OpenAlexafffund
Paul Yoon

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsToronto Metropolitan University
FundersPratt and Whitney Canada
KeywordsJet fuelAviation biofuelBiofuelEnvironmental scienceFossil fuelJet engineEnergy independenceCombustionGasolineWaste managementCarbon footprintProcess engineeringBioenergyGreenhouse gasEngineeringRenewable energyMechanical engineeringChemistry

Abstract

fetched live from OpenAlex

Growing concerns regarding the environmental impact of burning fossil fuels and energy independence has spurned the transportation industry into developing a more eco-friendly and sustainable way to travel. The most promising frontier in this endeavor is the development of alternative fuels that will significantly reduce the carbon footprint of our current engines with little or no modifications required. The effect that one of these frontier fuels may have, camelina based biofuel, on various fuel system materials was evaluated in this report in accordance with ASTM D4054 in the form of a 50:50 mixture. The materials went through an immersion process at varying temperatures and tested under the numerous standards outlined by the American Society for Testing and Materials (ASTM). The results were compared to a benchmark Jet A1 fuel since the effects of this fuel on current fuel system materials is extensively known. The purpose of this research is to determine the viability of using a 50:50 biofuel mixture in current aircraft fuel systems by evaluating the physical properties after being fuel treated. The results showed that the biofuel mixture affected the non-metallic materials in varying degrees, while not affecting the metallic materials.

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)0.000
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 score0.216
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.064
GPT teacher head0.269
Teacher spread0.205 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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