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Comparative Analysis of Permanent Magnet Eddy Current Losses in Conventional and Alternating-Flux-Barrier Spoke-Type Vernier Machine

2025· article· W4416963271 on OpenAlexaff
John Mushenya, Mehdi Moradi, Azeem Khan, Pragasen Pillay

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsVernier scaleMagnetTorqueFinite element methodRotor (electric)Eddy currentHarmonic analysisHarmonicDemagnetizing field

Abstract

fetched live from OpenAlex

The Alternating-Flux-Barrier Permanent Magnet Vernier Machine (AFBPMVM) is a promising topology for direct-drive wind power generation owing to its improved torque density relative to that of the traditional Spoke-Type Permanent Magnet Vernier Machine (STPMVM). Whereas the AFBPMVM's rich harmonic spectrum, and particularly the enhanced amplitude of its high-speed modulated harmonic, has been cited as the underlying reason for its improved torque capability, its modified rotor circuit can potentially amplify the Permanent Magnet Eddy-Current Losses (PM-ECLs). This may lead to excessive heat generation and consequent thermal demagnetization of the Permanent Magnets (PMs) if the necessary mitigation strategies are not implemented. This paper presents a comparative analysis of PM-ECLs in STPMVM and AFBPMVM. Finite Element Analysis (FEA), Semi-Analytical and experimental results are presented to highlight the ‘normal’ and ‘worst-case’ scenarios for PM-ECLs. The paper also discusses manufacturing considerations to mitigate PM-ECLs.

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)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.015
GPT teacher head0.286
Teacher spread0.271 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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