Design, Analysis, and Optimization of Ironless Stator Permanent Magnet Machines
Bibliographic record
Abstract
This paper presents a methodology for the design, analysis, and graphical optimization of ironless brushless permanent magnet machines primarily for generator applications. Magnetic flux in this class of electromagnetic machine tends to be 3-D due to the lack of conventional iron structures and the absence of a constrained magnetic flux path. The proposed methodology includes comprehensive geometric, magnetic and electrical dimensioning followed by detailed 3-D finite element (FE) modeling of a base machine for which parameters are determined. These parameters are then graphically optimized within sensible volumetric and electromagnetic constraints to arrive at improved design solutions. This paper considers an ironless machine design to validate the 3-D FE model to optimize power conversion for the case of a low-speed, ironless stator generator. The machine configuration investigated in this paper has concentric arrangement of the rotor and the stator, solenoid-shaped coils, and a simple mechanical design considered for ease of manufacture and maintenance. Using performance and material effectiveness as the overriding optimization criteria, this paper suggests optimal designs configurations featuring two different winding arrangements, i.e., radial and circumferentially mounted. Performance and material effectiveness of the studied ironless stator designs are compared to published ironless machine configurations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".