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Record W4407705511 · doi:10.1063/5.0247636

Vortex shedding from a yaw-oscillating circular cylinder in subcritical flow

2025· article· en· W4407705511 on OpenAlexafffund
Vahid Nasr Esfahani, Ronald Hanson, Alis Ekmekci

Bibliographic record

VenuePhysics of Fluids · 2025
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of TorontoYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsVortex sheddingMechanicsVortexCylinderKármán vortex streetFlow (mathematics)Classical mechanicsFlow visualizationTurbulenceReynolds numberGeometry

Abstract

fetched live from OpenAlex

This study investigates vortex shedding characteristics from a circular cylinder subjected to yaw oscillations. The study considers two length-to-diameter ratios (13 and 20) and two Reynolds numbers (5×103 and 1.5×104). Yaw oscillations ranging from θ=0° to 30° are considered at various reduced frequencies from 0.25 to 4. Vortex shedding behavior at the mid-span of the cylinder is measured using hot-wire anemometry. Planar particle image velocimetry (PIV) measurements at the mid-span are used to visualize the near wake. It is shown that an increase in the reduced frequency leads to a shift in the frequency range of vortex shedding toward lower values. Vortices are shed nearer to the base of the cylinder, and vortex shedding becomes more disorganized with increasing reduced frequency. Based on phase-averaged analysis of vortex shedding, the validity of the independence principle (IP) diminishes during the first half cycle beyond θ=15° at low reduced frequencies and beyond θ=20° at moderate reduced frequencies. In the return cycle, the deviation from the IP increases with increasing reduced frequency. The IP prediction is re-established at low yaw angles during the return cycle and for low reduced frequencies. Increasing the length-to-diameter ratio enhanced the organized vortex shedding and elevated the shedding frequency for both static and yaw-oscillating cylinders.

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: none
Teacher disagreement score0.521
Threshold uncertainty score0.552

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 head0.007
GPT teacher head0.233
Teacher spread0.226 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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