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Record W2898242989 · doi:10.1115/fedsm2018-83264

Effects of Offset Height on the Turbulent Characteristics of Rectangular Twin Jets

2018· article· en· W2898242989 on OpenAlexaff
Chidiebere F. Nwaiwu, Mark F. Tachie, Martin Agelin‐Chaab

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsOntario Tech UniversityUniversity of Manitoba
Fundersnot available
KeywordsNozzleTurbulenceOffset (computer science)Reynolds numberMechanicsAspect ratio (aeronautics)PhysicsGeometryPlane (geometry)Materials scienceOpticsMathematicsThermodynamics

Abstract

fetched live from OpenAlex

Turbulent characteristics of a twin jet were experimentally investigated for offset heights, h = 1d and 3d where d is the nozzle diameter. The experiments were conducted using a pair of rectangular nozzles of an aspect ratio of 3, that is oriented in the minor plane and with a nozzle separation ratio of 2.3. The Reynolds number, based on jet exit velocity and nozzle diameter was maintained at 4622. The results show that confinement effect decreased the merging point of the jets by 30% but there is no significant effect on the combined points. Reduced confinement showed an 89% reduction in the acceleration of the mean streamwise surface velocity. Two-point streamwise velocity correlations were used to investigate the large-scale coherent structures. The results revealed enhanced streamwise stretching of the structures as the offset height ratio decreased. In the combined region, the structures are more inclined towards the free surface as the offset height ratio decreased.

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)0.000
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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.005
GPT teacher head0.182
Teacher spread0.177 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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