MétaCan
Menu
Back to cohort
Record W4391097095 · doi:10.1109/tec.2024.3356988

Sub-Domain Model for Induction Motor With More Accurate Realization of Tooth-Saturation

2024· article· en· W4391097095 on OpenAlexafffund
Rajendra Kumar, Narayan C. Kar

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2024
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Windsor
FundersFedDev OntarioCanada Research Chairs
KeywordsEddy currentSaturation (graph theory)Induction motorControl theory (sociology)Finite element methodMagnetic coreSquirrel-cage rotorTorqueRelative permeabilityPermeability (electromagnetism)Computer scienceEngineeringControl engineeringElectronic engineeringElectromagnetic coilPhysicsMathematicsElectrical engineeringVoltageArtificial intelligenceChemistry

Abstract

fetched live from OpenAlex

Incorporation of magnetic saturation and its aftereffects have been challenging for sub-domain based analytical motor models. The proposed model utilizes the direct phenomenal impact of magnetic saturation on spatial variation of the core's permeability to formulate the effective tooth width. Implementation of the proposed method does not need an additional domain or magnetic vector-potential dependent term to include the saturation and hence, provides an efficient approach. The developments derived from the proposed method are seen in better estimation of motor's power factor, breakdown torque, and additional iron loss due to tooth pulsation and surface eddy currents. Prediction capability of the model is demonstrated with the experimental and finite element results of an 11 kW, four-pole squirrel cage induction motor for a wide range of operations.

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: none
Teacher disagreement score0.958
Threshold uncertainty score0.502

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 head0.011
GPT teacher head0.208
Teacher spread0.197 · 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

Citations7
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Energy ConversionSame topicElectric Motor Design and AnalysisFrench-language works237,207