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Record W2765493458 · doi:10.1115/fedsm2017-69204

POD Analysis of the Wake Development of a Pivoted Circular Cylinder Undergoing Vortex Induced Vibrations

2017· article· en· W2765493458 on OpenAlexaff
Erik Marble, Chris Morton, Serhiy Yarusevych

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of CalgaryUniversity of Waterloo
Fundersnot available
KeywordsWakeVortex sheddingPhysicsParticle image velocimetryCylinderReynolds numberMechanicsVortexOscillation (cell signaling)Water tunnelGeometryClassical mechanicsMathematicsTurbulence

Abstract

fetched live from OpenAlex

Vortex Induced Vibrations (VIV) of a pivoted circular cylinder with two degrees of freedom are investigated experimentally, focusing on quantifying the wake topology. Experiments are performed in a water tunnel for a pivoted cylinder with a fixed mass ratio of 10.8, moment of inertia ratio of 87.0–109.5, and a diameter-based Reynolds number of 3100. The reduced velocity was varied from 4.42 to 9.05 by varying the natural frequency of the structure. Velocity measurements were performed via time-resolved, two-component Particle Image Velocimetry (PIV), synchronized with cylinder displacement measurements. Time and phase-averaging are employed to analyze the wake development and relate it to the structural response. Proper Orthogonal Decomposition (POD) is utilized to gain insight into the development of coherent structures in the cylinder wake. The observed shedding patterns agree well with the Morse & Williamson [1] shedding map except for the cases at the boundary between 2P and non-synchronized shedding. The results show that the cylinder follows an elliptical trajectory with equal frequency of oscillation in streamwise and transverse directions. For the 2P regime, the tilt and direction of trajectory affect the formation and development of vortices in the wake. This results in a distinct asymmetry about the wake centerline in time-averaged statistics.

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 score0.371
Threshold uncertainty score0.284

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 head0.020
GPT teacher head0.240
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 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
Published2017
Admission routes1
Has abstractyes

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