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Record W2198754502 · doi:10.1115/imece2014-38522

The Effect of Longitudinal Core Flow on Trailing Vortex Stability

2014· article· en· W2198754502 on OpenAlexaff
Amir Allaf-Akbari, A. Gordon L. Holloway, Joseph W. Hall

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsVortexStarting vortexAirfoilVortex generatorVorticityPhysicsMechanicsHorseshoe vortexJet (fluid)Vortex ringBurgers vortexTrailing edgeVortex sheddingTurbulenceAerospace engineeringReynolds numberEngineering

Abstract

fetched live from OpenAlex

The current experimental study investigates the effect of longitudinal core flow on the formation and structure of a trailing vortex. The vortex is generated using four airfoils connected to a central hub through which a jet flow is added to the vortex core. Time averaged vorticity, circumferential velocity, and turbulent kinetic energy are studied. The statistics of vortex wandering are identified and corrections applied to the vorticity distribution. The vortex generator used in this study was built on the basis of the design described by Beninati et al. [1]. It uses four NACA0012 airfoils connected to a central hub. The wings orientation can be adjusted such that each contributes to a strong trailing vortex on the center of the test section. The vortex generator also had the capability to deliver an air jet directed longitudinally through a hole in the hub at the joint of the airfoils. Tests were done without the jet and with the air jet at jet velocities of 10 and 20 m/s. Planar PIV was used to measure the velocity field in the vicinity of the vortex core. The measurements were taken at 3 chords behind the vortex generator.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.206
Teacher spread0.197 · 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
Published2014
Admission routes1
Has abstractyes

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