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Record W2335379949 · doi:10.1115/gt2015-42230

Sea Level Performance of a CF-700 Engine Core With Alternative Fuels

2015· article· en· W2335379949 on OpenAlexafffund
Craig R. Davison, Pervez Canteenwalla, Jennifer L. Chalmers, Wajid A. Chishty

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsNational Research Council Canada
FundersTransport Canada
KeywordsKeroseneJet fuelEnvironmental scienceFuel oilThrust specific fuel consumptionFossil fuelWaste managementGas turbinesAutomotive engineeringFuel efficiencyNuclear engineeringEngineeringProcess engineeringMechanical engineeringChemistry

Abstract

fetched live from OpenAlex

The use of alternative fuels has the potential to enhance energy independence and reduce environmental impact of air travel. It is important to characterize gas turbine operation using such fuels under controlled conditions before implementing them in flight. The performance of a CF-700 engine core was examined in a sea level test facility. The following fuels were tested and will be reported on: 1. Jet A-1 – baseline fuel 2. 100% unblended Hydroprocessed Esters and Fatty Acids (HEFA) synthetic kerosene fuel with aromatics (SKA) 3. 100% unblended Fischer-Tropsch (FT) synthetic paraffinic kerosene (SPK) 4. Blended 50% HEFA-SPK and 50% Jet A-1 Fuel 2 above is an alternative fuel that can potentially be used without blending with conventional fuel. One purpose of the static engine testing was to determine if this fuel was suitable for use on subsequent test flights in a Dassault Falcon 20 aircraft. Engine performance testing was conducted at various power settings for each fuel. Relevant plots of performance are presented, compared and discussed. Transient tests were also performed including slams and chops. Observations of the effects of the different fuels on the engine fuel system are presented as some alternative fuels have the potential to cause seals to shrink and leaks to occur. The leaks observed are noted as are the steps taken to mitigate the problem.

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 score0.324
Threshold uncertainty score0.311

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 head0.067
GPT teacher head0.243
Teacher spread0.176 · 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

Citations6
Published2015
Admission routes2
Has abstractyes

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