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Record W2159067364 · doi:10.1109/tdei.2010.5492240

AC modeling and anisotropic dielectric properties of stress grading of form-wound motor coils

2010· article· en· W2159067364 on OpenAlexaff
Emad Sharifi, Shesha Jayaram, E.A. Cherney

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2010
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsUniversity of Waterloo
FundersIsfahan University of TechnologyNiroo Research InstituteIndian Institute of Science
KeywordsDielectricAnisotropyMaterials scienceFinite element methodElectrical conductorElectric fieldConductivityStress (linguistics)Modeling and simulationElectronic engineeringComposite materialStructural engineeringPhysicsEngineeringOpticsOptoelectronicsSimulation

Abstract

fetched live from OpenAlex

The paper addresses the ac modeling and anisotropic dielectric properties of stress grading and conductive armor tapes that are two crucial components in form-wound motor coils. The limitations of the conventional dc conductivity modeling and the necessities of using ac conductivity modeling and anisotropic properties of these materials at high frequencies and high electric field are discussed. The method of measurement of the dielectric parameters at high frequency and high electric field for 2D modeling is also presented. The transient FEM simulations from conventional modeling and the proposed ac modeling are compared to experimentally obtained results. Good agreement is only obtained from the latter modeling.

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)0.000
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: none
Teacher disagreement score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 communication0.0000.000
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 head0.021
GPT teacher head0.234
Teacher spread0.213 · 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

Citations10
Published2010
Admission routes1
Has abstractyes

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