Multiobjective Shape Optimization of Segmented Pole Permanent-Magnet Synchronous Machines With Improved Torque Characteristics
Bibliographic record
Abstract
Magnet segmentation is an effective and simple technique for cogging torque reduction in high power permanent-magnet (PM) synchronous machines; however, it deteriorates air gap flux density and decreases the output torque. Therefore, a multiobjective optimization framework is necessary for cogging torque minimization, and to diminish its adverse effect on the output torque in segmented-pole permanent-magnet synchronous machines (PMSMs). This can be fulfilled by proper selection of widths and displacements of the magnet segments. Finite-element analysis (FEA) is an accurate method for this purpose. However, it is very time consuming where finding optimal configuration needs a lot of simulations. Thus, an analytical based design optimization is very useful and eases the design process. In this paper, a novel semianalytical model for cogging torque computation in PMSMs is proposed. Based on the proposed model, a multiobjective optimization framework is developed. The particle swarm optimization (PSO) method is applied to find the optimum machine design. To show the effectiveness of the proposed method, two prototype segmented magnet PMSMs with two and three PM blocks per pole are optimized respectively. Performance characteristics are compared to the initial machine design and segmented PMSMs with design parameters chosen according to previous analytical models and initial uniform pole machines using FEA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".