A Data-Driven Approach For Design Knowledge Extraction of Synchronous Reluctance Machines Using Multi-Physical Analysis
Bibliographic record
Abstract
This paper provides electromagnetic, structural, and acoustic design guidelines for synchronous reluctance machines using a data-driven approach. A design space of different rotor geometries is created and simulated using a finite element package to evaluate the self- and mutual-correlation of different physical performances, such as average torque, torque ripple, iron power loss, saliency ratio, sound pressure level, and mechanical stress caused by centrifugal and magnetic forces. Then, a statistical analysis is conducted to extract knowledge for relating the design and objective spaces with each other. 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. Lastly, multiple designs are selected based on different 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 | 0.001 |
| 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".