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Record W2568679852 · doi:10.2514/6.2017-0150

Investigation of Engine Performance at Altitude Using Selected Alternative Fuels for the National Jet Fuels Combustion Program

2017· article· en· W2568679852 on OpenAlexafffundabout
Pervez Canteenwalla, Wajid A. Chishty

Bibliographic record

Venue55th AIAA Aerospace Sciences Meeting · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsNational Research Council Canada
FundersAir Force Research LaboratoryMinistère de la Défense Nationale
KeywordsCombustionJet fuelJet engineAlternative fuelsJet (fluid)Altitude (triangle)Aerospace engineeringEnvironmental scienceAutomotive engineeringAeronauticsNuclear engineeringEngineeringChemistry

Abstract

fetched live from OpenAlex

The National Research Council Canada (NRC) is an allied partner in the National Jet Fuels Combustion Program (NJFCP), which is a US multi-agency program led by the Federal Aviation Administration (FAA). NJFCP aims to accelerate the ASTM International fuel approval process for alternative jet fuels (AJF) by conducting combustion-related testing and modelling to gain a better understanding of the effect of AJF on engine combustion. In support of NJFCP, NRC has undertaken studies on a turbojet engine to investigate the effect of various fuels on engine ignition at simulated altitude conditions. One baseline conventional petroleum derived jet fuel with “average” physical properties and three test fuels meant to represent AJF with unique characteristics were tested. Significant differences were observed in the ignition performance of the fuels. Despite the test fuel having very different chemical compositions, the fuel physical properties appear to have the predominant influence on the ignition performance.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

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 head0.312
Teacher spread0.248 · 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 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

Citations6
Published2017
Admission routes3
Has abstractyes

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