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Record W2766348844 · doi:10.23919/tems.2017.7961341

EMF waveform optimization using the permanent magnet volume-integration method

2017· article· en· W2766348844 on OpenAlexaff
Maxime R. Dubois, João Pedro F. Trovão

Bibliographic record

VenueCES Transactions on Electrical Machines and Systems · 2017
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsWaveformHarmonicsCylinderMagnetMechanicsElectromotive forceElectromagnetic coilSurface (topology)PhysicsHarmonicAcousticsVolume (thermodynamics)Mechanical engineeringGeometryMathematicsVoltageEngineeringThermodynamics

Abstract

fetched live from OpenAlex

The emf expression can be derived with the PM volume-integration method, allowing easier optimization and prediction of the emf harmonic content. An analytical expression is developed for predicting the electromotive force (emf) waveforms and flux linkage resulting from the motion of permanent magnets (PM) in the case of two cylinders, where the outer cylinder carries a surface-mounted winding and the inner cylinder carries the PMs. The expressions are based on the PM Volume-Integration Method, which uses a volume integral calculated over the magnet volume, rather than the usual surface integral over the coil surface. The specific case of surface-mounted arc PM with radial magnetization is analyzed. An outer cylinder with infinitely thin winding distribution on its inner surface is considered. The chording factor, slot factor and spread factor are included in the analytical expression. The emf waveform and related harmonics are predicted analytically and validated by comparing with a finite element analysis and with experiment.

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 score0.991
Threshold uncertainty score0.605

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 studies0.0010.000
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 head0.259
Teacher spread0.243 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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