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Record W2011342707 · doi:10.1115/omae2008-57068

Wake Fields Behind Risers Undergoing Vortex-Induced Vibration

2008· article· en· W2011342707 on OpenAlexaff
Jie Xu, Don Spencer, Alex Gardner, David Molynuex, Wei Qiu, Neil Bose, Rodney Masters, John Shanks

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsWakeCylinderVortex-induced vibrationVortexParticle image velocimetryVortex sheddingReynolds numberVibrationMechanicsPhysicsAmplitudeTransverse planeWake turbulenceFlow visualizationKármán vortex streetFlow (mathematics)AcousticsStructural engineeringOpticsEngineeringTurbulenceMechanical engineering

Abstract

fetched live from OpenAlex

This paper presents measurements of the wake field behind three riser models obtained by using a Digital Particle Image Velocimetry (DPIV). The three riser models were a circular rigid cylinder, a cylinder fitted with fairings and a cylinder fitted with strakes. The models were free to vibrate in the cross-flow direction when towed in a uniform flow. The range of tested Reynolds number based on the diameter of the cylinder was from 3×104 to 2.5×105. The measurement results showed that the transverse vibration amplitudes for both the faired and straked cylinder were far less than those of the bare cylinder. Based on the wake flow field comparison between the models tested it was found that the modes of vortex shedding observed behind the bare cylinder did not occur behind either the faired or straked cylinder. This reveals that the vibrations responses of the cylinders are directly related to their wake modes. Strong, regularly shed vortices induce large amplitude vibration and weak, scattered vortices lead small amplitude or no vibration. The different wake patterns are presented for the three 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.014
GPT teacher head0.199
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2008
Admission routes1
Has abstractyes

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