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Record W4297144285 · doi:10.1063/5.0115610

Effect of harmonic inflow perturbation on the wake vortex dynamics of a cylinder undergoing two-degree-of-freedom vortex-induced vibration near a plane boundary

2022· article· en· W4297144285 on OpenAlexafffund
Maziyar Hassanpour, Chris Morton, Robert J. Martinuzzi

Bibliographic record

VenuePhysics of Fluids · 2022
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsWakeVortex sheddingMechanicsReynolds numberInstabilityVortexInflowCylinderVortex-induced vibrationPerturbation (astronomy)Classical mechanicsTurbulenceGeometry

Abstract

fetched live from OpenAlex

The influence of inflow perturbations on the wake dynamics and structural response is investigated for a cylinder undergoing vortex-induced vibrations (VIV) in oscillatory flows in the proximity of a solid boundary. Numerical simulations are conducted at a Reynolds number of 200, based on the cylinder diameter and free-stream velocity, for perturbation frequencies fp up to four times the natural shedding frequency fo. Three response regimes are identified: a lock-on regime at fp=2fo, with maximum cylinder displacement and forces, a force-amplification regime for 1.8<fp<2.3 characterized by shedding frequency entrainment, and a weakly coupled regime. The wake and structural response dynamics differ from those for unperturbed VIV in uniform flow. The primary mechanism underlying these differences is due to the symmetric instability of the shear layers forced by the perturbations. This instability results in the shedding of vortex pairs at fp in the cylinder base region, which interact with the Kármán formation process and, in the amplification regimes, reinforce the natural instability at 2fo. These mechanisms give rise to distinct wake topology, which is then related to the structural dynamics.

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 score0.197
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.227
Teacher spread0.214 · 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

Citations8
Published2022
Admission routes2
Has abstractyes

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