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Record W7083825650 · doi:10.26629/jtr.2023.09

Inspection the Fuel Spray Pattern of The Injector Nozzle in Turboprop Engine by Using Fuel Testing Device

2023· article· en· W7083825650 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of Technology Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene expression and cancer classification
Canadian institutionsnot available
Fundersnot available
KeywordsInjectorTurbopropFuel injectionNozzleCombustionFuel tankTurbojetCombustion chamber

Abstract

fetched live from OpenAlex

This study deals experimental study to inspect the fuel spray pattern in fuel injector nozzle in turboprop engine type PT6A-60 SERIES produced by Pratt & Whitney Canada by using fuel testing device. due to incomplete combustion which is mainly occurred due to incorrect shape of fuel spray, it results in carbon formation inside the combustion chamber, which negatively effects on the fuel injector and compressor turbine (CT) vane. The cleaning procedure to fuel injector is done by two methods which are handling clean and ultrasonic clean device. The testing is done by using fuel injector testing machine which is use fuel to check spray pattern. This machine was made due to the need for it by the maintenance team, and the actual machine used by the manufacturer was not available. This test was carried out at different conditions which is the first at the start of operation at 20 psi, the second at low speed, 30 psi pressure, and the third at maximum speed at high pressure 60 psi. The case 01 and 02 have been taken at the time of maintenance check of the fuel injector in 15/12/2022, while the case 03 and 04 were taken also randomly from the same engine but in different time at 09/03/2023. From obtained results, most of the cases were acceptable and does not need to re-cleaning after testing, except case 04 was not acceptable and need to clean by ultrasonic device.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.010
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.074
GPT teacher head0.368
Teacher spread0.294 · 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
Published2023
Admission routes1
Has abstractyes

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