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Record W7020057872

Joint Wind and Ice Effects on Transmission Lines in Mountainous Terrain

2020· article· en· W7020057872 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2020
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsIcingTerrainWind speedJoint (building)Electric power transmissionAccretion (finance)AmplitudeTransmission (telecommunications)
DOInot available

Abstract

fetched live from OpenAlex

Atmospheric icing on mountainous terrain can produce catastrophic damages to transmission lines when incoming particles impinge and accrete on the cable surface of the system. The first challenge in wind-ice loading is determining joint statistics of wind and ice accretion on transmission lines. This study analyzes the weather characteristics for a specific site of study using 15 years of historical data to use as inputs for ice accretion modeling. The joint wind and ice hazard is characterized by simulating 500 years of icing events from the fitted probability distributions of ice accretion and wind on ice velocities. The second challenge of wind and ice loading is to deal with the wind induced vibrations when the iced conductors present complex asymmetrical shapes. The vertical galloping, characterized by high amplitude and low frequency motions, produce extra tension to the transmission towers which is not considered in the Canadian standard (CSA-C22.3) for the design of wind and ice loads for overhead transmission lines. For the dynamic analysis, the Den Hartog’s principle is applied to identify potential instabilities and the linear theory of free vibrations of a suspended cable is performed for the estimation of the extra tension produced by the free stream velocity acting on the one-degree-of-freedom iced conductor. The static and dynamic loading resulting from the present study are compared with the wind and ice design cases based on the Canadian standard (CSA-C22.3).

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.254
Teacher spread0.215 · 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 designObservational
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
Published2020
Admission routes1
Has abstractyes

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