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Record W4389440236 · doi:10.1063/5.0174172

Relationship between wake and cylinder dynamics for a cylinder undergoing modulated vortex-induced vibrations

2023· article· en· W4389440236 on OpenAlexafffund
Maziyar Hassanpour, Chris Morton, Robert J. Martinuzzi

Bibliographic record

VenuePhysics of Fluids · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsMcMaster UniversityUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsWakeMechanicsVortex sheddingCylinderVortex-induced vibrationInflowClassical mechanicsVortexAdded massVibrationReynolds numberTurbulenceGeometryAcoustics

Abstract

fetched live from OpenAlex

Vortex-induced vibrations (VIV) in the initial branch are investigated for a 2-degree-of-freedom circular cylinder placed near a plane boundary (Re=UDν=200, where U is the inflow average streamwise velocity, D is the cylinder diameter, and ν is the kinematic viscosity) with imposed sinusoidal perturbations of the free stream at resonant, 2fo, and near-resonant conditions, 2.2fo (fo is the natural shedding frequency). The cylinder exhibits a quasi-periodic response, which challenges the comprehension of its relationship with the wake dynamics obtained through conventional VIV models. The total force acting on the cylinder is decomposed into a vortex-induced force, FV, linearly coupled to VIV, and a force induced by the effective mass of the cylinder, FS, which is non-linearly coupled to VIV. The proposed semi-empirical model reveals that the time-varying nature of the effective mass in FS drives the non-linear response. The model's physical consistency is verified against simulation results. While focusing on VIV in the initial branch, the validity of the proposed model is expected to extend to other branches of response, offering a promising avenue for developing a robust predictive model for VIV under various flow conditions.

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 score0.418
Threshold uncertainty score0.739

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 head0.040
GPT teacher head0.272
Teacher spread0.231 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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