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Record W3169792049 · doi:10.1109/tec.2021.3085748

Fourier-Based Modeling of an Induction Machine Considering the Finite Permeability and Nonlinear Magnetic Properties

2021· article· en· W3169792049 on OpenAlexaff
Aida Mollaeian, Animesh Kundu, Mohammad Sedigh Toulabi, Michael Udo Thamm, Seog Kim, Jimi Tjong, Narayan C. Kar

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2021
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsStatorFinite element methodIsotropyNonlinear systemMagnetic fluxFourier seriesMagnetic fieldFourier transformTorqueFourier analysisControl theory (sociology)MechanicsComputer sciencePhysicsEngineeringMathematical analysisMathematicsMechanical engineeringStructural engineering

Abstract

fetched live from OpenAlex

Performance prediction of induction machines (IMs) is highly dependent on the accuracy of the material characteristics and geometry of the machine during the modeling stage. To reduce the complexity of an IM model, normally the slotting effects are neglected. Likewise, the permeability of the stator and rotor cores are assumed to be infinite leading to an ideal set of partial differential equations (PDEs) for homogenous and isotropic materials. In this paper, the permeability of the stator and rotor cores are assumed not to be infinite and the slotting effects are taken into consideration to propose a more accurate and realistic model of an IM to reduce the discrepancies between the performance expectations and actual results. Fourier-based (FB) magnetic field approach is used to fulfill this aim via anisotropic layer theory (ALT) enabling the proposed model to include distortion of magnetic flux in the slotted regions. Air-gap flux density, core losses, efficiency and torque of an IM are predicted via the FB model and are validated through the finite element analysis (FEA) and experimental studies.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.374

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 head0.017
GPT teacher head0.188
Teacher spread0.171 · 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 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

Citations16
Published2021
Admission routes1
Has abstractyes

Explore more

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