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Record W2902600180 · doi:10.1109/ceidp.2018.8544776

Effectiveness of Stress Grading System Builds on the Heat Production in a Form-Wound Coil of an Inverter-fed Rotating Machine

2018· article· en· W2902600180 on OpenAlexaff
Alireza Naeini, E.A. Cherney, Shesha Jayaram

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsElectromagnetic coilJoule heatingElectrical conductorVoltageMaterials scienceMechanical engineeringMechanicsComposite materialElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Limiting the temperature rise in the stress grading system in a medium voltage form-wound coil of a rotating machine driven by an adjustable speed drive is essential to prolong the life of a motor. The common way of increasing the electrical conductivity of the system, thereby to lower joule heat production and temperature rise, is to increase the build of the tape system. Simulation studies on various stress grading system builds on the heat production has been evaluated in this study. A good correlation between measured and simulated temperature profiles under pulse voltage condition confirms the validity of the simulation. The stress grading system has been simulated using the 2D axisymmetric module in COMSOL® 5.2a. The results show that increasing the number of layers of conductive armor tape can reduce the heat production in CAT region but by increasing the number of layers of stress grading tape leads to increase in heat production in SGT region. The study suggests that use of other non-linear tape materials for SGT may be needed to effectively lower the temperature rise in the stress grading system.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.232
Teacher spread0.218 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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