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Record W3036845659 · doi:10.18280/jesa.530202

Design and Characteristics of Axial Magnetic Gear Using Rectangular Magnet

2020· article· fr· W3036845659 on OpenAlexvenueno aff
Sudirman Syam, Sri Kurniati, Ruslan Ramang

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2020
Typearticle
Languagefr
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMagnetMagnetic gearMechanical engineeringElectropermanent magnetMaterials scienceEngineeringPhysicsElectrical engineeringPermanent magnet synchronous generator

Abstract

fetched live from OpenAlex

This experimental study examines the characteristics and performance of axial magnetic gear by using a variation of the rectangular neodymium-iron-boron (NdFeB) magnetic layer which is assembled on an acrylic disc.The aim is to reduce magnetic reluctance which can increase torque and facilitate the manufacture of magnetic gear.In addition, it can reduce the use of NdFeB permanent magnets instead of sectoral magnets.An appropriate method for predicting the transmitted torque produced by axial magnetic gears with four rectangular magnetic layers is demonstrated using the output power approach.The results show that the performance of axial magnetic-gear with 4 layers tends to be similar to the performance of a direct drive.Tests on the 2400 rpm rotation with the loading of 200, 300 and 400 ohms respectively showed a maximum torque of 2.24 (Nm) 10 -3 , 1.56 (Nm) 10 -3 , and 1.1 (Nm).The results of this paper appear to be useful for the development of axial magnetic-gear industrial applications.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 science0.0010.000
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 head0.023
GPT teacher head0.224
Teacher spread0.201 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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Same venueJournal Européen des Systèmes AutomatisésSame topicElectric Motor Design and AnalysisFrench-language works237,207