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Record W4300585751 · doi:10.1615/tsfp10.540

Merging of coherent structures in a separation bubble

2017· article· en· W4300585751 on OpenAlexaff
John W. Kurelek, Serhiy Yarusevych

Bibliographic record

VenueProceeding of Tenth International Symposium on Turbulence and Shear Flow Phenomena · 2017
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAirfoilVortexLaminar flowPhysicsReynolds numberParticle image velocimetryBubbleMechanicsWind tunnelFlow separationVortex sheddingFlow visualizationAngle of attackParticle tracking velocimetryAcousticsAerodynamicsClassical mechanicsFlow (mathematics)Boundary layerTurbulence

Abstract

fetched live from OpenAlex

This work examines the spatio-temporal evolution and interaction of coherent structures in a laminar separation bubble (LSB) when left to develop naturally and excited acoustically. The investigation is carried out in a wind tunnel using a NACA 0018 airfoil model at a chord Reynolds number of 125000 and an angle of attack of 4°. Excitation is provided by an external acoustic source and planar, time-resolved Particle Image Velocimetry is used to characterize both the streamwise and spanwise flow development. It is shown that the separation bubble is receptive to acoustic disturbances applied at the first subharmonic of the most unstable disturbance frequency in the natural LSB, leading to the inception, growth, and decay of velocity fluctuations in the separated shear layer. These velocity disturbances are shown to manifest through periodic vortex merging − a process that otherwise occurs randomly in the natural flow. Assessment of the spanwise flow topology reveals that structures merge in a non-uniform manner along the span, with localized merging occurring away from where streamwise-oriented bulges develop in the two vortices involved in the pairing process.

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 score0.221
Threshold uncertainty score0.418

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 head0.012
GPT teacher head0.261
Teacher spread0.249 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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