Axial Dual-Flux-Modulator Magnetic Gear Mitigating Yoke Flux Leakage
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".