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Record W4409457917 · doi:10.2514/1.b39246

Ejector Recirculation and Entrainment

2025· article· en· W4409457917 on OpenAlexaff
Joel H. Kramer, Derek Lastiwka, Jeff Edwards, Artem Korobenko, Craig T. Johansen, Chris Morton

Bibliographic record

VenueJournal of Propulsion and Power · 2025
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsMcMaster UniversityUniversity of Calgary
Fundersnot available
KeywordsEntrainment (biomusicology)InjectorMechanicsEnvironmental scienceAerospace engineeringMaterials scienceMeteorologyEngineeringMechanical engineeringPhysicsAcoustics

Abstract

fetched live from OpenAlex

Confined-jet flow structures were investigated in the context of an ejector ramjet application. An ejector apparatus was constructed with variable mixing chamber diameters and downstream flow restrictions in the form of an orifice plate. The performance of the ejector was characterized by its entrainment and compression ratios for 30 unique geometric combinations. A mixing chamber diameter that was incrementally larger than the orifice plate diameter maximized the entrainment ratio performance of the ejector. Measurements of the wall static pressure, collected by wall pressure taps, were used to determine the relationship between the ejector performance and the confined-jet flow structures within. Experimental wall static-pressure measurements were supplemented by Reynolds-averaged Navier–Stokes [Formula: see text] shear-stress transport computational fluid dynamics simulations of 18 ejector geometric combinations. The initial static-pressure gradient, axial length of the confined-jet flow, location of the recirculation core, and recirculation eddy separation and reattachment points were investigated for a correlation to the maximum entrainment performance of the ejector. The recirculation eddy size and fully developed location of the confined-jet flow showed little evidence of correlation to the ejector performance.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.123

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 head0.003
GPT teacher head0.206
Teacher spread0.202 · 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 designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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