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Record W4252289486 · doi:10.1109/ias.1989.96894

Modeling of effects of laminations on steep fronted surge propagation in large AC motor coils

2003· article· en· W4252289486 on OpenAlexaff
W.W.L. Keerthipala, P.G. McLaren

Bibliographic record

VenueConference Record of the IEEE Industry Applications Society Annual Meeting · 2003
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsUniversity of Manitoba
FundersTehran University of Medical Sciences and Health Services
KeywordsSurgeElectromagnetic coilElectrical impedanceFlux (metallurgy)VoltageMagnetic fluxPhysicsAcousticsElectrical engineeringMechanical engineeringMechanicsComputer scienceEngineeringMaterials scienceMagnetic field

Abstract

fetched live from OpenAlex

The modeling of machine windings for steep-fronted surge propagation has been based on an assumption that the slot walls act as flux barriers at the frequencies of interest (1 to 10 MHz). The assumption is valid for a solid slot environment, because the magnetic flux cannot penetrate beyond the skin depth, delta , which is of the order of 10/sup -6/ m for the frequencies concerned. However, this is not the case for a laminated slot environment, which can allow magnetic flux penetration on steep-fronted surge propagation in large AC motor coils are examined. Experimental data are reported showing that solid-slot models cannot accurately replace the laminated environments. Maximum interturn voltages observed for the solid slot model are smaller (by a factor of about 2/3) than those for the laminated slot environment. The differences between solid and laminated environments is attributed to the effect of laminations, which can be approximately modeled using the concept of surface impedance.>

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)0.001
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.250
Teacher spread0.231 · 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

Citations3
Published2003
Admission routes1
Has abstractyes

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