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Record W2064095304 · doi:10.1109/tpwrd.2006.877091

Assessment of the Current Intensity for Preventing Ice Accretion on Overhead Conductors

2007· article· en· W2064095304 on OpenAlexafffundabout
Zsolt Péter, M. Farzaneh, Lszl I. Kiss

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2007
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsUniversité du Québec à Chicoutimi
FundersUniversité du Québec à Chicoutimi
KeywordsIcingElectrical conductorConductorJoule heatingElectric power transmissionWind tunnelOverhead lineIntensity (physics)Overhead (engineering)MechanicsElectrical engineeringMaterials scienceEnvironmental scienceMeteorologyEngineeringPhysicsOptics

Abstract

fetched live from OpenAlex

This paper concerns the determination of the electric current requirements for an anti-icing technique based on the Joule effect. The minimum current intensity needed for preventing ice accretion depends on several conductor parameters, including external diameter, electrical resistance, as well as surface geometry (number and diameter of external strands). It depends also on meteorological conditions, such as air temperature, wind velocity, and liquid water content. The study comprises the elaboration of a mathematical model and the laboratory experiments for validation. This research work is mainly concerned with power-line conductor and atmospheric parameters. Therefore, four different types of single A1/S1 power-line conductors are investigated. The analytical model was validated with the experiments performed in the wind tunnel of CIGELE Icing Research Pavilion at the University of Quebec, Chicoutimi. In order to complete the mathematical model, it is necessary to assess the overall heat transfer coefficient (HTC) for stranded conductors. The HTC measurements are presented for conductors with different surface geometries.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0010.000

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 head0.022
GPT teacher head0.279
Teacher spread0.256 · 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 designBench or experimental
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

Citations57
Published2007
Admission routes3
Has abstractyes

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