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Record W2059506682 · doi:10.1080/14685248.2013.780659

A study in the developing region of square jet

2013· article· en· W2059506682 on OpenAlexafffund
Abbas Ghasemi, Vesselina Roussinova, Ram Balachandar

Bibliographic record

VenueJournal of Turbulence · 2013
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVortexJet (fluid)TurbulenceParticle image velocimetryPhysicsPlane (geometry)MechanicsInstabilityClassical mechanicsGeometryMathematics

Abstract

fetched live from OpenAlex

The developing region of a turbulent square jet is investigated using high-resolution particle image velocimetry (PIV). The mean velocity and turbulence stresses are presented in various horizontal planes, along the jet centerline covering the initial region of the jet as well as the transition to the self-similar region. To study the flow structure away from the central plane, velocity measurements in two additional horizontal planes, one located halfway from the jet central plane toward the edge and the other at the edge of the square jet, are also examined. Analysis of the instantaneous velocity fields reveal the presence of an arrow-like feature in the square jet due to the higher instability generated in the jet shear layer compared with a round jet. To elucidate the imprints of the vortex structures present in the jets, a swirling strength-based vortex identification methodology is applied on a large ensemble of instantaneous velocity fields. Statistical analysis of the number of vortex cores, and their size and rotational strength in the measurement plane is undertaken. Vortex population at the edge was found to be very different compared with that in the central plane.

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)0.000
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
Metaresearch0.0000.000
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 science0.0000.000
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 head0.019
GPT teacher head0.239
Teacher spread0.220 · 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

Citations23
Published2013
Admission routes2
Has abstractyes

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