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Record W2016785717 · doi:10.1115/gt2010-23499

Effect of Entraining Diffuser on the Performance of Bent Exhaust Ejectors

2010· article· en· W2016785717 on OpenAlexafffund
Asim Maqsood, A. M. Birk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDiffuser (optics)InjectorNozzleMechanicsStatic pressureMaterials scienceMixing (physics)ThermocoupleTube (container)Back pressureBent molecular geometryMechanical engineeringEngineeringOpticsPhysicsComposite material

Abstract

fetched live from OpenAlex

This paper presents experimental data from subsonic air-air bent ejectors with ring type entraining diffusers. Three oblong ejectors with different degrees of bend in the mixing tubes were used in the study. The experiments were performed on a wind tunnel capable of providing air up to 2 kg/s. The performance was studied at different primary flow temperature and swirl conditions. Pressure, velocity and temperature was measured upstream of the primary nozzle and at the exit of the diffuser. Detailed traverses were done with 7-hole probes and thermocouples. As would be expected, the performance of the ejector decreases with the degree of bend. The diffusers were added to improve the pressure recovery in the ejector and for self cooling of the diffuser. Improved pressure recovery typically leads to improved pumping in ejectors. With the diffuser added, the overall length of the ejectors was increased from 2.5 to 3.8 times the mixing tube equivalent diameter. Different trends of pumping ratio with the swirl conditions were obtained with hot and cold primary flows.

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: Bench or experimental · Consensus signal: Bench or experimental
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 science0.0000.000
Research integrity0.0000.000
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 head0.004
GPT teacher head0.197
Teacher spread0.193 · 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 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

Citations3
Published2010
Admission routes2
Has abstractyes

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