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Record W2036694842 · doi:10.2514/1.13139

Tip Vortex Behind a Wing Undergoing Deep-Stall Oscillation

2005· article· en· W2036694842 on OpenAlexaff
David M. Birch, T. Lee

Bibliographic record

VenueAIAA Journal · 2005
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsMcGill University
Fundersnot available
KeywordsStall (fluid mechanics)VortexWingVortex sheddingAngle of attackMechanicsHorseshoe vortexSwept wingPhysicsOscillation (cell signaling)Aerospace engineeringVortex liftWingtip vorticesStarting vortexAerodynamicsLift coefficientVorticityVortex ringEngineeringReynolds numberTurbulenceBiology

Abstract

fetched live from OpenAlex

The flow structure of a tip vortex in the near field of a NACA 0015 wing with an effective aspect ratio of 5.04 undergoing a deep-stall oscillation with a(t) = 18 deg + 6 deg sin ωt at Re = 1.86 × 10 5 was investigated. The wing oscillation imposed a strong discrepancy in contour shapes and magnitudes between the pitch-up and pitch-down phases of the oscillation cycle. The vortex was more organized and nearly axisymmetric for x/c > 0.5 during pitch-up than during pitch-down. The peak tangential velocity and vorticity and the strength and size of the vortex increased with a(t), except in the vicinity of α max , and had higher values during pitch-up than during pitch-down. The axial flow was always wake like and the velocity deficit decreased with α(t), while exhibiting a sharp increase and decrease on the upstroke and downstroke in the vicinity of α max , respectively. The tangential velocity decreased slightly with the downstream distance. The vortex size increased rather significantly with x/c during pitch-down while remaining virtually unchanged during pitch-up. The inner region of the vortex exhibited a self-similar structure, similar to that of a stationary wing. The induced drag increased with a(t) and had a local maximum at 20 deg during pitch-up.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.470

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 head0.006
GPT teacher head0.195
Teacher spread0.189 · 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 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

Citations11
Published2005
Admission routes1
Has abstractyes

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