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Record W4251549137 · doi:10.1115/gt2010-22494

Emissions From a Gas Turbine Sector Rig Operated With Synthetic Aviation and Biodiesel Fuel

2010· article· en· W4251549137 on OpenAlexaff
Greg Pucher, W. Allan, Pierre Poitras

Bibliographic record

VenueVolume 2: Combustion, Fuels and Emissions, Parts A and B · 2010
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsCommunications Security EstablishmentRoyal Military College of Canada
Fundersnot available
KeywordsCombustionCombustion chamberNozzleSynthetic jetEnvironmental scienceMaterials scienceJet fuelExhaust gasJet (fluid)CombustorBiodieselWaste managementChemistryMechanical engineeringEngineeringAerospace engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Differences in exhaust emissions, smoke production, exhaust pattern factor, deposit build-up and fuel nozzle spray characteristics for various blends of conventional commercial jet fuel (Jet A-1) with synthetic and biodiesel formulations were investigated. Three synthetic fuel formulations and four Fatty Acid Methyl Esters (FAME) were evaluated as such. The synthetic fuels were tested in both neat (100%) and 50% by volume blends with Jet A-1, while the FAME fuels were blended in 2% and 20% proportions. The Combustion Chamber Sector Rig (CCSR), which houses a Rolls Royce T-56-A-15 combustion section, was utilized for emissions, deposits and exhaust pattern factor evaluation. A combustion chamber exhaust plane traversing thermocouple rake was employed to generate two dimensional temperature maps during operation. Following combustion testing, several combustion system components, including the combustion chamber, fuel nozzle and igniter plug were analyzed for relative levels of deposit build-up. A Phase Doppler Anemometry (PDA) system was employed to determine differences in droplet size distributions while an optical spray pattern analyzer was used to compare the spray pattern for the various fuel blends as they emerged from the T-56 nozzle.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.969

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 integrity0.0000.001
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 head0.010
GPT teacher head0.213
Teacher spread0.204 · 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

Citations5
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueVolume 2: Combustion, Fuels and Emissions, Parts A and BSame topicAdvanced Combustion Engine TechnologiesFrench-language works237,207