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Record W2571717129 · doi:10.1109/cefc.2016.7816397

3-D sub-domain analytical model to calculate magnetic flux density in induction machines with semi-closed slots under no-load condition

2016· article· en· W2571717129 on OpenAlexaff
Aida Mollaeian, Eshaan Ghosh, Himavarsha Dhulipati, Jimi Tjong, Narayan C. Kar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsStatorFinite element methodRotor (electric)Magnetic fluxMagnetic potentialLaplace transformMagnetic fieldPoisson's equationSquirrel-cage rotorMathematical analysisInduction motorMathematicsMechanicsPhysics

Abstract

fetched live from OpenAlex

In this paper, a novel 3-D sub-domain analytical model is developed to determine magnetic flux distribution in a squirrel-cage induction machine with skewed rotor bars under no-load condition. The analytical method is based on the resolution of 3-D Laplace and Poisson's equations in cylindrical coordinates using separation of variables method to calculate the magnetic vector potential for corresponding sub-domain. The proposed model is sufficiently general to be used for any slot/pole combination including slotting and tooth-tips for both stator and rotor, which were usually neglected in previous 2-D solutions due to complexity of the differential equations. To evaluate the performance of the proposed 3-D analytical model, calculated magnetic field distribution is compared with those obtained from the 3-D finite element analysis (FEA) and experimental results.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.212
Teacher spread0.203 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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