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Record W3111241800 · doi:10.1615/tsfp7.2360

NONLINEAR RECEPTIVITY TO OBLIQUE VORTICAL MODES IN FLOW PAST AN ELLIPTIC LEADING EDGE

2011· article· en· W3111241800 on OpenAlexaff
Lars-Uve Schrader, Luca Brandt, Catherine Mavriplis, Dan S. Henningson

Bibliographic record

VenueProceeding of Seventh International Symposium on Turbulence and Shear Flow Phenomena · 2011
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFreestreamOblique caseBoundary layerPhysicsVorticityNonlinear systemLeading edgeAmplitudeVortexReceptivityTurbulenceStreakSwept wingMechanicsOpticsReynolds numberAerodynamics

Abstract

fetched live from OpenAlex

Boundary layer receptivity to pairs of unsteady oblique freestream vortical modes is studied in direct numerical simulation of flow over a flat plate with an elliptic leading edge. The freestream is perturbed by three types of oblique Fourier modes, differing in the magnitude of the three vorticity components. The vortical modes excite steady boundary layer streaks, and the associated receptivity mechanism is nonlinear in the forcing amplitude. Leading edges with two different aspect ratios are considered. It is found that the streak amplitudes obtained are largely unaffected by the leading edge bluntness. Whereas linear receptivity is the predominant mechanism at low forcing frequencies, the nonlinear mechanism becomes important when high-frequency vortices are present in the freestream. Nonlinear receptivity is therefore expected to contribute significantly to the excitation of boundary layer streaks by freestream turbulence.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.234
Teacher spread0.215 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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