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Record W2153999037 · doi:10.1061/41016(314)190

Modeling and Prediction of Failure of Transmission Lines Due to High Intensity Winds

2008· article· en· W2153999037 on OpenAlexaffabout
Horia Hangan, Eric Savory, Ashraf El Damatty, Jon Galsworthy, Craig A. Miller

Bibliographic record

VenueStructures Congress 2008 · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsThunderstormTowerStormElectric power transmissionComputer scienceLine (geometry)MeteorologyWind engineeringTornadoTransmission lineEnvironmental scienceMarine engineeringStructural engineeringEngineeringPhysicsTelecommunicationsElectrical engineering

Abstract

fetched live from OpenAlex

The high intensity winds (HIW) associated with thunderstorm outflows (downbursts, tornados) have profile shapes that differ greatly from the straight line winds in conventional atmospheric boundary layers that form the basis of the design wind loads for electricity transmission line systems. Evidence from line failures, such as Manitoba Hydro's (MH) on 5th September 1996, that caused failure of 19 towers and a 5-day line outage, suggests that many are the result of HIW, due to a combination of extreme wind speed and an off-design wind load distribution. Whilst the complete upgrading of any given line system to withstand the severest storms is not feasible, some critical locations (e.g. major river / road / rail crossings) could benefit from remediation based on a thorough understanding of HIW loads. The authors have completed the first phase of a comprehensive research program aimed at quantifying these HIW wind fields, their loading on transmission line tower and line components and the resultant response of the transmission line system. The main outcomes from that phase were 1 Formulation of a computational fluid dynamics (CFD) model of downburst and tornado wind fields validated by small-scale laboratory experiments and a theoretical / analytical model 2 Derivation of a loading model for downburst winds and application of this model to fully non-linear finite element (FE) models of a MH Type A guyed tower / line system. Identification of some of the critical members and failure mechanisms. A second phase of work is currently in progress that is concerned with the validation and implementation of the previously developed concepts, encompassing 1 Design and construction of a large scale downburst simulator (based on a 2-D wall jet concept) 2 Testing of tower/line physical models in the large-scale downburst simulator, with comparison to loads derived from atmospheric boundary layer wind profile model testing. 3 Improving the downburst and the tornado numerical models by introducing atmospheric parameters and comparison with Doppler radar atmospheric data.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.205
Teacher spread0.194 · 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".

Quick stats

Citations13
Published2008
Admission routes2
Has abstractyes

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