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Record W2063768366 · doi:10.1063/1.4865838

Linear stability of the Moore-Saffman model for a trailing wingtip vortex

2014· article· en· W2063768366 on OpenAlexafffund
Jan Feys, S. A. Maslowe

Bibliographic record

VenuePhysics of Fluids · 2014
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsVortexLaminar flowWakeInstabilityMechanicsVortex ringJet (fluid)Flow (mathematics)Wingtip vorticesVortex sheetLinear stabilityClassical mechanicsVorticityHorseshoe vortex

Abstract

fetched live from OpenAlex

This paper presents an investigation of the stability of a trailing vortex using mean flow profiles given by an approximate solution of the Navier-Stokes equations. The axial and tangential velocity profiles obtained from this solution, deduced by Moore and Saffman [“Axial flow in laminar trailing vortices,” Proc. R. Soc. London, Ser. A 333, 491–508 (1973)], agree well with experiments involving wings at slight angles of attack. In particular, the Moore-Saffman profiles better describe the jet-like and wake-like axial flows near the center of the vortex than does the much-studied Batchelor vortex. We determine solutions numerically for these profiles and find that they are well suited to describe the flow at short and intermediate distances behind the wingtip. Growth rates for unstable perturbations are presented for different values of n, the wingtip loading parameter. These growth rates are shown to be somewhat larger than those obtained for the Batchelor vortex, and instability persists for larger values of the swirl. The largest amplification rates were found to occur near n = 0.5, the value corresponding to elliptic loading. This is within the range 0.44 < n < 1.0, where the core axial flow is wake-like. For n < 0.44, the flow in the vortex core is jet-like and the growth rates of unstable perturbations become progressively smaller, with all modes damped for n ≈ 0.25.

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 score0.320
Threshold uncertainty score0.412

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 head0.017
GPT teacher head0.216
Teacher spread0.199 · 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

Citations13
Published2014
Admission routes2
Has abstractyes

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