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Record W4386108418 · doi:10.1109/tpwrd.2023.3307907

Modelling the Flexural Hysteresis Behaviour of Bretelle Dampers Based on a Quasi-Static Bending Test

2023· article· en· W4386108418 on OpenAlexafffund
Shima Zamanian, Sébastien Langlois, Alex Loignon, Alireza Ture Savadkoohi, Marc François

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2023
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStructural engineeringBeam (structure)HysteresisDamperBendingDissipationNonlinear systemVibrationMaterials scienceFlexural strengthConductorMechanicsEngineeringPhysicsAcousticsComposite material

Abstract

fetched live from OpenAlex

Bretelle dampers are made of slack conductor pieces that are used to mitigate aeolian vibration amplitudes. Under cyclic and dynamic excitation, inter-strand friction in slack conductors causes a significant flexural hysteresis leading to the dissipation of high amounts of energies. The objective of this work is to study the flexural hysteresis of three types of slack conductors based on quasi-static bending tests and to reproduce their nonlinear hysteresis behavior using two different approaches; an analytical model using a linear Euler-Bernoulli beam coupled with a Bouc-Wen model, and a finite element model using a superposition of multifiber beam elements with material nonlinearity. The parameters of both models are identified based on the bending test results for different levels of deformation. The developed models in this study can provide a fast tool for manufacturers to identify the dynamical behavior of slack conductor and to optimize their damping properties. Furthermore, the bretelle damper model can be integrated into a conductor model in order to study the vibrational behavior of transmission lines equipped with bretelle dampers.

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: none
Teacher disagreement score0.800
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.220
Teacher spread0.202 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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