Sizing and detailed design procedure of external rotor synchronous reluctance machine
Bibliographic record
Abstract
Finding an optimally designed synchronous reluctance machine with desired performance among all possible combinations of the stator and rotor with different shapes of barriers requires long execution time, which is an overwhelming task. Selecting the number of flux barriers, for instance, in accordance with the pre‐designed stator has a significant effect on the torque ripple. To tackle these issues, this study presents a comprehensive design procedure of an external rotor synchronous reluctance machine suitable for an electric bike application that includes considerations such as electro‐magnetic and mechanical aspects. Dimensions of the internal stator with tapered slots are calculated. Additionally, a global parameter, insulation ratio in the q ‐axis, is used to link the microscopic parameters. Seeking for the optimal design through the insulation ratio effectively reduced the number of geometric parameters involved in the rotor shape optimisation. The preliminary rotor design is used to run multi‐objective optimisation to provide further improvement to the average torque and torque ripple. Utilising the finite‐element method, thermal and structural analyses are conducted to guarantee the safe operation of the designed motor under a steady‐state condition. Finally, measurement results of a 250 W‐fabricated motor are compared with the predicted results, which validate the effectiveness of the proposed method.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".