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Record W2608325409 · doi:10.2514/1.j055501

Characterization of Vortex Dynamics in a Laminar Separation Bubble

2017· article· en· W2608325409 on OpenAlexafffund
Andrew Lambert, Serhiy Yarusevych

Bibliographic record

VenueAIAA Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLaminar flowAirfoilVortexMechanicsVortex sheddingReynolds numberBubbleFlow separationAngle of attackChord (peer-to-peer)Starting vortexPhysicsMaterials scienceClassical mechanicsBoundary layerAerodynamicsVortex ringTurbulenceComputer science

Abstract

fetched live from OpenAlex

The development of coherent structures within the laminar separation bubble that forms on a NACA 0018 airfoil at an angle of attack of 8 deg and a chord-based Reynolds number of 100,000 is investigated experimentally. A combination of high-speed flow visualizations and time-resolved surface pressure measurements is used to enable quantitative analysis of vortex dynamics. The amplification of disturbances in the separated shear layer leads to the formation of rollup vortices that are shed upstream of the mean reattachment location. The experimental results show that these structures produce distinct, periodic surface pressure fluctuations that can be used to detect, track, and characterize the rollup vortices. A vortex tracking technique is developed and validated in the present investigation. The developed methodology is used to investigate temporal and spatial variation in vortex shedding parameters and to study attendant vortex interactions, which involve merging of two or more consecutive vortices. The results provide new insight into vortex dynamics in laminar separation bubbles, and the developed technique can serve as an efficient tool for future studies and active flow control implementation.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

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)0.0010.000

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 head0.007
GPT teacher head0.228
Teacher spread0.221 · 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 designObservational
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

Citations30
Published2017
Admission routes2
Has abstractyes

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