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Record W2898391674 · doi:10.1115/fedsm2018-83478

Wing Tip Vortex Development Under a Grid Generated Turbulent Flow

2018· article· en· W2898391674 on OpenAlexaff
Kamal Ben Miloud, Marouen Dghim, Hachimi Fellouah, Mohsen Ferchichi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsRoyal Military College of CanadaUniversité de Sherbrooke
Fundersnot available
KeywordsVortexWingtip vorticesHorseshoe vortexPhysicsTurbulenceStarting vortexVorticityMechanicsReynolds numberVortex ringVortex generatorParticle image velocimetryVortex stretchingWake turbulence

Abstract

fetched live from OpenAlex

The interaction of a NACA 0012 wingtip vortex with a grid-generated flow was investigated in this paper. The experiments were conducted in the near and mid-wakes regions at three free stream turbulence (FST) levels of 0.5% (empty wind tunnel), 3% and 6%, and at two Reynolds numbers, based on the wing chord length, of 2 × 105 and 3 × 105. Stereoscopic Particle Image Velocimetry (SPIV) and hot wire measurements were carried out at four downstream positions, namely x/c = 0.5, 2.5, 5 and 7. Streamwise velocity contours showed that the wingtip vortex decayed with increased FST and downstream distance. In the vortex core region, the streamwise velocity decelerated while the vortex adopted a wake-like profile. FST was found to decrease the vortex circulation, to increase the vortex radius, and to increase the vortex meandering amplitude. By increasing the Reynolds number, the grid cases showed a small variation of the vortex radius and vorticity peak, particularly at downstream positions of 5 and 7. With meandering correction, the turbulence level within the vortex core were found to be reduced as the artificial turbulence induced by the vortex meandering was removed.

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 score0.000
Threshold uncertainty score0.001

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 communication0.0000.000
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 head0.011
GPT teacher head0.197
Teacher spread0.186 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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Same topicFluid Dynamics and Turbulent FlowsFrench-language works237,207