MétaCan
Menu
Back to cohort

Novel Experimental Approach to Studying the Thermal Stability and Coking Propensity of Jet Fuel

2017· article· en· W2601263191 on OpenAlexafffund
Frank T. C. Yuen, Jason Liang, Neell G. Young, Saeid Oskooei, Sri Sreekanth, Ömer L. Gülder

Bibliographic record

VenueEnergy & Fuels · 2017
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaBioFuelNet CanadaPratt & Whitney
KeywordsNozzlePressure dropCoolantNuclear engineeringMaterials scienceThermalDrop (telecommunication)InjectorJet fuelStack (abstract data type)RepeatabilityMechanicsVolumetric flow rateFuel injectionMechanical engineeringChemistryThermodynamicsAerospace engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

An experimental apparatus was designed and built to conduct studies on the thermal stability and carbon deposition leading to coking in the fuel injection nozzles of small gas turbine engines. The apparatus is a simplified but controlled representation of an aircraft fuel system consisting of a preheating section and a test section. The preheating section simulates the heating of the fuel when it is used as a coolant on board an aircraft, and the test section simulates the geometry, temperature, pressure, and flow rates of the fuel injection nozzles. Proof-of-principle experiments were performed to verify the functionality of the apparatus and the repeatability of measurements. The pressure drop across the test section was used during experiments to monitor deposit buildup, and the effective reduction in the test-section diameter due to deposit blockage was calculated. The deposition rate was validated further using a carbon-burnoff apparatus. The experimental results showed that the pressure drop increased significantly with increasing testing time, as expected, and that measuring the pressure drop is an effective method of monitoring and quantifying deposit buildup.

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 score0.046
Threshold uncertainty score0.362

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)0.0000.000

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 head0.054
GPT teacher head0.250
Teacher spread0.196 · 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

Citations16
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueEnergy & FuelsSame topicHeat transfer and supercritical fluidsFrench-language works237,207