MétaCan
Menu
Back to cohort
Record W2068328681 · doi:10.1115/gt2006-90017

The Effect of an Entraining Diffuser on the Performance of Circular-to-Slot Exhaust Ducts With a 90 Degree Bend

2006· article· en· W2068328681 on OpenAlexafffund
Sebastiaan Bottenheim, A. M. Birk, D. Poirier

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInletDuct (anatomy)Transverse planeMechanicsDiffuser (optics)Materials scienceNozzleVortexSecondary flowInternal flowFlow (mathematics)OpticsStructural engineeringPhysicsTurbulenceEngineeringMechanical engineeringThermodynamics

Abstract

fetched live from OpenAlex

An experimental study has been undertaken on a 3-stage entraining diffuser with a distorted inlet flow. Two different circular-to-slot transition ducts were used as driving nozzles. Both transition ducts included a 90 degree bend. Varying degrees of inlet swirl were also considered. A 7-hole pressure probe was used to traverse the diffuser outlets. With a longitudinal duct the measured flows showed severe impingement of the primary flow on the walls of the entraining diffuser. Similar outlet flow distributions were observed for all cases of inlet swirl considered. In contrast, only minimal primary flow impingement was observed with a transverse duct. The use of the transverse duct also resulted in significant secondary flows including multiple large-scale vortices. These secondary flows intensified when swirl was added. Four performance parameters were calculated: the entrainment ratio, back pressure coefficient, pressure recovery coefficient and the entraining diffuser efficiency. The results showed that peak performance was attained at 20° of inlet swirl for both configurations. The addition of an entraining diffuser was found to result in minimal improvements of performance for the longitudinal duct configuration and significant improvements for the transverse orientation.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score0.245

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 head0.005
GPT teacher head0.177
Teacher spread0.171 · 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 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

Citations2
Published2006
Admission routes2
Has abstractyes

Explore more

Same topicTurbomachinery Performance and OptimizationFrench-language works237,207