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Record W2796342410 · doi:10.1017/jfm.2018.188

Experimental investigation of unsteady separation in the rotor-oscillator flow

2018· article· en· W2796342410 on OpenAlexafffund
Marc‐Étienne Lamarche‐Gagnon, Jérôme Vetel

Bibliographic record

VenueJournal of Fluid Mechanics · 2018
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsMechanicsPhysicsCylinderFlow (mathematics)VortexFlow separationParticle image velocimetryPlane (geometry)Classical mechanicsTurbulenceGeometryMathematics

Abstract

fetched live from OpenAlex

Visualisations of various types of flow separation are presented in an experimental set-up that translates a rotating cylinder parallel to a wall. Particle image velocimetry is used to measure the two velocity components in a plane perpendicular to the cylinder where the flow is two-dimensional. To spatially resolve the flow close to the wall, a high-viscosity fluid is used. For a periodic translation, the fixed separation is compared to the theory of Haller (J. Fluid Mech., vol. 512, 2004, pp. 257–311), while for non-periodic translations, a method is proposed to extract the moving separation point captured by a Lagrangian saddle point, and its finite-time unstable direction (separation profiles). Intermediate cases are also presented where both types of separation, fixed and moving, are either present simultaneously or appear successively. Some results issued from numerical simulations of an impinging jet show that all the cases observed in the rotor-oscillator flow are not restricted to high-viscosity fluid motions but may also occur within any vortical flow.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.239
Teacher spread0.227 · 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 designBench or experimental
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

Citations3
Published2018
Admission routes2
Has abstractyes

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