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Record W4232264958 · doi:10.1115/gt2008-51209

Improving the Performance of a Bent Ejector With Inlet Swirl

2008· article· en· W4232264958 on OpenAlexafffund
Asim Maqsood, A. M. Birk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor Technologies Research
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInjectorNozzleBent molecular geometryInletTube (container)MechanicsMaterials scienceWork (physics)Gas turbinesBack pressureFlow (mathematics)Overall pressure ratioMechanical engineeringPhysicsEngineeringGas compressorComposite material

Abstract

fetched live from OpenAlex

Ejectors are commonly employed in gas turbine exhaust systems for reasons such as space ventilation and IR suppression. Ejectors may incorporate bends in the geometry for various reasons. Studies have shown that the bend has a deteriorating effect on the performance of an ejector. This work was aimed to investigate the effect of exhaust gas swirl on improving the performance of a bent ejector. Four short oblong ejectors with different degrees of bend in the mixing tube and four swirl conditions were tested in this study. The primary nozzle, in all the cases, was composed of a circular to oblong transition. Testing was performed at ambient and hot primary flow with 0, 10, 20 and 30° swirl angles. It was observed that the swirl had a strong affect on the performance of a bent ejector. Improvement of up to 55, 96 and 180% was obtained in the pumping ratio, pressure rise and total efficiency respectively with a 20° swirl in the exhaust gas.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.013
GPT teacher head0.209
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2008
Admission routes2
Has abstractyes

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