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Axial Dual-Flux-Modulator Magnetic Gear Mitigating Yoke Flux Leakage

2023· article· en· W4391807960 on OpenAlexaff
Aran Shoaei, Qingsong Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMagnetic flux leakageMagnetic gearMagnetic fluxCoaxialTorqueLeakage (economics)Yoke (aeronautics)Torque densityHarmonicsMagnetMagnetic circuitMagnetic fieldPhysicsElectrical engineeringMechanicsEngineeringVoltage

Abstract

fetched live from OpenAlex

In order to improve the torque capability of coaxial magnetic gears (CMGs), spoke type PM arrangement can be adopted in both high-speed and low-speed rotors. This configuration concentrates the magnetic flux within iron pole shoes, resulting in a more effective flux density distribution in the air-gap. Nonetheless, spoke-type structure is prone to a significant leakage flux, particularly at the outer boundary of the low-speed rotor. To address this issue, this paper presents a novel structure known as the axial flux dual-flux-modulator CMG (AF-DFM CMG) in which, an auxiliary slotted flux-modulator is incorporated to mitigate the leakage flux in the outer rotor. This not only reduces leakage flux but also introduces more harmonics in the torque transmission, revealing the nested magnetic gearing effect of the iron pole shoes. Additionally, the inner rotor adopts a Halbach PM arrangement to create a more sinusoidal air-gap flux density distribution and decrease the thickness of the back iron yoke. The performance of the proposed structure is theoretically described and then verified by 3D finite element analysis (3DFEA). The results demonstrate the superiority of the AF-DFM CMG over conventional surface-mounted and spoke-type configurations, with respective improvements of 67% and 40% in torque capability. This highlights the remarkable potential of the new design.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.010
GPT teacher head0.204
Teacher spread0.195 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2023
Admission routes1
Has abstractyes

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