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Record W1976874617 · doi:10.1109/tdei.2007.344622

Performance of nanofillers in medium voltage magnet wire insulation under high frequency applications

2007· article· en· W1976874617 on OpenAlexaff
Saeed Ul Haq, Shesha Jayaram, E.A. Cherney

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2007
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsUniversity of Waterloo
FundersIndian Institute of Science
KeywordsMaterials scienceScanning electron microscopeSurface roughnessComposite materialWeibull distributionPartial dischargeDielectricSurface finishMagnetVoltageElectrical engineeringOptoelectronics

Abstract

fetched live from OpenAlex

The paper presents the experimental results on accelerated aging of enameled wires with and without nanofiller in the coating under partial discharge (PD) activity. The residual life of the wires, and the surface roughness as measured by a scanning electron microscope (SEM), are used to evaluate the effects of the nanofiller. The surface erosion in the specimens without nanofiller is considerably greater, which is 1120-1270 nm, under pulse aging; whereas, in specimens with ~1%, by wt, fumed silica (SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) nanofiller it was measures to be 230-250 nm. An evaluation of the residual life of the wires, aged under high frequency AC waveforms, reveal that the wires with SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> achieved a life that is twice the life of conventional wires. The two-parameter Weibull distribution of dielectric strength data show that the value of the shape parameter, beta, increases from 2.6 to 11.4 for the coatings with nanofillers indicating that the material becomes more homogeneous and exhibits fewer defects. Finally, the paper discusses possible reasons for the reduction in surface erosion and improvements in breakdown strength of enameled wires filled with various types and concentrations of nanofillers

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
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 head0.011
GPT teacher head0.233
Teacher spread0.222 · 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 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

Citations38
Published2007
Admission routes1
Has abstractyes

Explore more

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