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Record W2171312088 · doi:10.1049/iet-epa.2007.0391

Performance improvement of permanent magnet machines by modular poles

2009· article· en· W2171312088 on OpenAlexaff
Arash Hassanpour Isfahani, Sadegh Vaez‐Zadeh, M.A. Rahman

Bibliographic record

VenueIET Electric Power Applications · 2009
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMemorial University of Newfoundland
FundersUniversity of Tehran
KeywordsThrustMagnetModular designControl theory (sociology)Finite element methodEngineeringComputer scienceMechanical engineeringControl engineeringStructural engineering

Abstract

fetched live from OpenAlex

Modular permanent magnet poles have recently been proposed to enhance the performance of permanent magnet machines and improve the exploitation of used magnet materials. An optimisation method for these poles is proposed for use in linear permanent magnet synchronous machines. The main objective of the optimisation is to select proper dimensions and material properties of modular permanent magnet poles to enhance the machine developed thrust. The optimisation is carried out based on a mathematical model of the machine obtained analytically. In particular, the developed thrust of linear permanent synchronous machines with modular poles is given by the model. Genetic algorithm is then employed to optimise pole parameters where the ratio of thrust ripples to average thrust is chosen as an objective function. Extensive investigations carried out by analytical and finite element methods confirm that substantial lower thrust ripples are produced with almost the same average thrust.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.002
GPT teacher head0.187
Teacher spread0.184 · 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 designBench or experimental
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

Citations21
Published2009
Admission routes1
Has abstractyes

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