MétaCan
Menu
Back to cohort
Record W2037628623 · doi:10.1017/s0022112006008986

Wakes and vortex streets generated by translating force and force doublet: laboratory experiments

2006· article· en· W2037628623 on OpenAlexaff
Y. D. Afanasyev, Vasily Korabel

Bibliographic record

VenueJournal of Fluid Mechanics · 2006
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsStrouhal numberPhysicsVortex sheddingReynolds numberMechanicsParticle image velocimetryVortexForcing (mathematics)Kármán vortex streetClassical mechanicsFlow visualizationCylinderDimensionless quantityVorticityFlow (mathematics)TurbulenceGeometryMathematics

Abstract

fetched live from OpenAlex

Wakes and vortex streets such as those occurring behind towed or self-propelled bodies are generated by moving localized forces in a viscous fluid at moderate values of the Reynolds number, $\hbox{\it Re}\,{\sim}\,10^{2}$ . The forcing is provided by an electromagnetic method and allows us to create a ‘virtual’ body without introducing any solid objects into the fluid. Characteristics of stable and unstable wakes, in particular the shedding frequency, are measured in the space of control parameters, namely the magnitude of the forcing and the speed of translational motion of the forcing. The results for a single force presented in the dimensionless form of the Strouhal number demonstrate quantitative similarity to those for the classical flow around a cylinder. The problem considered here has an extra degree of freedom compared to the problem of the flow around a cylinder and exhibit a wider array of different regimes. These regimes are documented in both our visualization experiments and particle image velocimetry measurements.

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)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.005
GPT teacher head0.197
Teacher spread0.192 · 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

Citations28
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Fluid MechanicsSame topicFluid Dynamics and Vibration AnalysisFrench-language works237,207