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Record W2766636596 · doi:10.1115/fedsm2017-69127

Vortex-Induced Vibration of a Circular Cylinder With Low Mass Ratio Near a Plane Wall

2017· article· en· W2766636596 on OpenAlexafffund
Sina Daneshvar, Chris Morton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWakeParticle image velocimetryPhysicsVortex sheddingCylinderMechanicsVorticityVortexAmplitudePotential flow around a circular cylinderVortex-induced vibrationBoundary layerOpticsClassical mechanicsFlow (mathematics)GeometryReynolds numberTurbulenceOpen-channel flowMathematics

Abstract

fetched live from OpenAlex

Vortex induced vibration of a circular cylinder with low mass ratio in vicinity of a wall boundary is investigated experimentally in a water tunnel facility. Simultaneous measurements of the flow field via planar Particle Image Velocimetry and amplitude response have been carried out across a wide range of reduced velocities and cylinder-wall gap ratios (S* = S/D). For S* ≥ 3, both the amplitude response and the wake development are not significantly affected by the presence of the wall boundary. As S* is decreased below 3, the amplitude response decreases until S* ≈ 0.5, where the cylinder begins to periodically impact the wall. For all S* ≤ 0.5, the cylinder continues to impact the wall in a periodic fashion, and the reduced velocity range over which this occurs increases. Mean field and RMS field statistics revealed strong asymmetric wake development for S* < 3. Proper Orthogonal Decomposition of the velocity data was used to investigate the energy distribution in the coherent wake structures, and to filter the incoherent fluctuations via construction of a Reduced Order Model. Reconstructions of instantaneous vorticity fields obtained from the ROM illustrate the changes in vortex shedding patterns with the cylinder response.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.572
Threshold uncertainty score0.357

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 head0.008
GPT teacher head0.205
Teacher spread0.197 · 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 routes2
Has abstractyes

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