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Record W3193352825 · doi:10.1063/5.0059013

Illuminating the complex role of the added mass during vortex induced vibration

2021· article· en· W3193352825 on OpenAlexafffund
Zhicheng Wang, Dixia Fan, Michael S. Triantafyllou

Bibliographic record

VenuePhysics of Fluids · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsQueen's University
FundersQueen's UniversityDalian University of TechnologyCompute Canada
KeywordsPhysicsVortex-induced vibrationVortex sheddingAdded massReynolds numberCylinderVibrationVortexMechanicsAmplitudeFlow (mathematics)Classical mechanicsOpticsAcousticsTurbulenceGeometry

Abstract

fetched live from OpenAlex

The role of the added mass coefficient in vortex induced vibration (VIV) of the bluff body is complex and elusive. It is certain that decoding the relationship between the added mass and the vibration pattern will benefit the prediction and prevention of VIV. We present a study on VIV of a long flexible cylinder and forced vibration of a rigid cylinder, in a combination of experimental optical measurements and high-fidelity numerical simulation. We focus on uniform flow over a uniform cylinder at a fixed Reynolds number, Red = 900, but systematically varied the motion amplitude in the in-line (Axd) and cross-flow direction (Ayd), as well as the phase angle (θ) between the motions. We show that θ∈[π2,3π2] is associated with negative added mass coefficients in the cross-flow direction (Cmy < 0), and there is a strong correlation between the vortex shedding mode of “2P” or “P+S” and Cmy < 0.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 studies0.0000.001
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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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 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

Citations52
Published2021
Admission routes2
Has abstractyes

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