Synthesis of High-Performance PM Motors with Concentrated Windings
Bibliographic record
Abstract
The windings concentrated around the teeth offer obvious advantages for the electrical machines with radial air-gap, because the volume of copper used in the end-windings can be reduced. The Joule losses are decreased and the efficiency is improved. These machines are still limited to applications of subfractional power and they generally present a reduced number of phases. In the three-phase machines, the concentrated winding is too often restricted to a winding with a short pitch of 120 electrical degrees, i. e., to a winding with performances reduced compared to the traditional structures. But there is a significant number of three-phase structures that can support a concentrated winding if the number of poles is increased. In this article, the authors present a synthesis of the structures of three-phase machines with concentrated windings. In the first part, the structures with a regular distribution of the slots are presented. A systematic method is proposed to determine the windings and the performances are discussed. In the second part, the authors present original structures of three-phase machines with concentrated windings that use an irregular distribution of the slots. A specific method to identify these structures is described, and a comparative analysis of the performances of the original and traditional structures is performed by using a field calculation software.
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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 | 0.001 |
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".