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Record W3005456864 · doi:10.1109/tdei.2019.008344

Temperature and electric field distributions along a form wound coil of an inverter-fed rotating machine with micro-varistor stress grading system

2020· article· en· W3005456864 on OpenAlexaff
Alireza Naeini, E.A. Cherney, Shesha Jayaram

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2020
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsElectromagnetic coilMaterials scienceVoltageVaristorElectric fieldImpulse (physics)Electrical conductorConductivityElectrical engineeringElectronic engineeringComposite materialEngineeringPhysics

Abstract

fetched live from OpenAlex

Temperature and electric field along the stress grading system of a form wound coil under repetitive impulse voltage are studied using 2D axisymmetric and 3D models in COMSOL̅ 5.3a. The validity of the 3D model is shown by comparing the results with the measured temperature profile along the stress grading system. The evaluation of both temperature and electric field shows that using a stress grading system based on a micro-varistor characteristic reduces the maximum temperature; however, with an increase in the electric field, which can be reduced to an acceptable level. An optimization on the initial conductivity of the micro-varistor characteristic confirms that desired electric field and temperature rise are achievable by selecting an optimized conductivity characteristic. 2D and 3D simulations show that a proposed stress grading system, which is a combination of an optimized stress grading tape conductivity and minimum conductive armor tape length, has a good temperature and electrical performances under repetitive impulse voltage.

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 categoriesnone
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 score0.573
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.200
Teacher spread0.190 · 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.

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

Citations6
Published2020
Admission routes1
Has abstractyes

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