A Data-Driven Approach for Design Knowledge Extraction of Synchronous Reluctance Machines Using Multi-Physical Analysis
Bibliographic record
Abstract
Using a data-driven approach, this paper provides electromagnetic, structural, acoustic, and thermal guidelines for designing synchronous reluctance machines. Design spaces of different rotor geometries are created and simulated using finite element packages to evaluate the self- and mutual-correlation of different physical performances, such as average torque, torque ripple, efficiency, power factor, saliency ratio, sound pressure level, mechanical stress, and average winding temperature. Then, a statistical analysis is conducted to extract knowledge and guidelines for relating the design and objective spaces. It is demonstrated that not all the objectives must be incorporated into the design process since some of them are non-conflicting. Hence, a motor designer can numerically evaluate which design variables should be changed and by how much in order to fulfill the design specifications. Multiple designs are selected based on different multi-physical requirements. Useful guidelines for selecting the appropriate motor speed and voltage ratings are proposed while considering structurally reliable designs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".