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Effect of coupled rotational and transverse vibration on the vortex structures around a rectangular cross-section

2025· article· en· W4413077657 on OpenAlexaff
M Emadeldin, Mostafa Rashed, Atef Mohany, Mahmoud Shaaban

Bibliographic record

VenueJournal of Physics Conference Series · 2025
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsVortex sheddingTransverse planeVibrationPhysicsVortexRotation (mathematics)MechanicsVortex-induced vibrationTurbulenceAmplitudeWakeNormal modeNatural frequencyClassical mechanicsAcousticsStructural engineeringOpticsGeometryEngineeringReynolds numberMathematics

Abstract

fetched live from OpenAlex

Abstract Rectangular structures subjected to turbulent flows are subject to vortex shedding, which induces dynamic forces that depend on the flow conditions and structural motion. Structures with rotational and transverse structural modes may be excited by vortex shedding to oscillate, inducing vibration and fatigue problems. This work investigates the coupled torsional and transverse vibration of a rectangular cross-section with an aspect ratio of 4. To illustrate the effect of mode coupling, three cases are considered: rotation only, rotation and transverse vibration with the same natural frequencies, and rotation and transverse vibration with different natural frequencies. Results show that allowing the structure to vibrate with the two modes with the same natural frequency results in higher vibration amplitudes. Flow fields indicate that higher vibration amplitudes are associated with a wider wake, a distinctive vortex shedding pattern, a widespread turbulent field, and a longer vortex reattachment length on the side of the structure.

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 score0.004

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 head0.006
GPT teacher head0.240
Teacher spread0.234 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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