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Record W2003295001 · doi:10.1139/l00-089

Nonlinear dynamic behaviour of guy cables in turbulent winds

2001· article· en· W2003295001 on OpenAlexvenueno aff
Bruce F. Sparling, A. G. Davenport

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2001
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsNonlinear systemCatenaryAeroelasticityMechanicsVibrationStructural engineeringFinite element methodAerodynamicsHarmonic balanceHarmonicTurbulenceCoupling (piping)PhysicsDisplacement (psychology)EngineeringAcoustics

Abstract

fetched live from OpenAlex

Large amplitude cable vibrations are difficult to predict using linear theory due to the presence of sag in the suspended profile. A numerical study was therefore undertaken to investigate the dynamic behaviour of inclined cables excited by imposed displacements. To model the nonlinear nature of cable response, a time domain finite element approach was adopted using nonlinear catenary cable elements. Two types of horizontal displacement patterns were enforced at the upper end of the guy. In the first phase of the study, harmonic displacement histories with a wide range of forcing frequencies were considered. In the second phase, random enforced displacements were used to simulate the motion of a guyed mast in gusty winds. The influence of aerodynamic drag and damping forces was investigated by performing analyses under still air, steady wind, and turbulent wind conditions. It was found that nonlinear coupling of related harmonic response components was significant at certain critical frequencies, particular when the excitation was harmonic and acted in the plane of the guy. Positive aerodynamic damping was shown to effectively suppress resonant and nonlinear coupling response.Key words: cables, structural dynamics, wind loading, finite element method, nonlinear analysis, guyed towers.

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 score0.180
Threshold uncertainty score0.834

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.005
GPT teacher head0.183
Teacher spread0.178 · 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

Citations5
Published2001
Admission routes1
Has abstractyes

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