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Record W1510127076 · doi:10.1115/gt2007-27632

CFD (Realizable K-ε) and Cold-Flow Testing for the Design of Air-Air Ejectors With Triangular Tabbed Driving Nozzles

2007· article· en· W1510127076 on OpenAlexafffund
S. F. McBean, 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
KeywordsComputational fluid dynamicsNozzleInjectorTurbulenceMechanical engineeringMixing (physics)EngineeringAirflowMechanicsMarine engineeringSimulationAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

This paper describes an investigation in which a commercial CFD software package has been used to evaluate the performance of air-air ejectors with triangular tabbed driving nozzles. The Realizable k-ε turbulence model was employed and CFD predictions were evaluated against experimental results. Ejector performance was measured on the basis of pumping, mixing, and back-pressure. It was discovered that CFD models were generally capable of predicting trends in pumping and mixing, but were unable to account for appropriate magnitude. In addition, CFD did not accurately represent the strength of the streamwise vortices generated by the tabs. At increased tab angles, CFD predicted values of back-pressure very well. However, under the 90° tab configuration, back-pressure was severely under-predicted.

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: Methods · Consensus signal: none
Teacher disagreement score0.794
Threshold uncertainty score0.271

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 head0.019
GPT teacher head0.218
Teacher spread0.199 · 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
GenreMethods

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
Published2007
Admission routes2
Has abstractyes

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