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Record W4415748240 · doi:10.1109/tpwrd.2025.3626700

Prediction of Aeolian Vibrations in Conductors Equipped With Bretelles

2025· article· W4415748240 on OpenAlexaff
Shima Zamanian, Sébastien Langlois

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2025
Typearticle
Language
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsElectrical conductorDissipationVibrationConductorTransient (computer programming)Electric power transmissionNonlinear systemTransmission lineBending

Abstract

fetched live from OpenAlex

Bretelles (damper-loops) are made of slack conductor pieces that are used to mitigate aeolian vibration amplitudes. Under cyclic and dynamic excitation, significant flexural hys teresis is induced by inter-strand friction within the wires of the bretelle, resulting in the dissipation of substantial amounts of energy. This study proposes a comprehensive method for modeling and predicting aeolian vibrations in transmission line conductors equipped with bretelles. This method employs a direct transient approach to predict the energy dissipation of bretelles and it uses a nonlinear numerical model that was developed for slack cable based on experimental bending tests. By incorporating empirical equations for wind power input and conductor self-damping power dissipation, and utilizing the Energy Balance Principle, vibration amplitudes expected on conductors are predicted. The predicted vibrational response and maximum amplitudes compared well with the laboratory and experimental line data available in the literature. The developed method allows for optimizing the geometrical and mechanical properties of bretelles when coupled with conductors, eliminating the need for additional experimental tests.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.868
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.210
Teacher spread0.198 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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