A Study of the Relationship between Acoustic Noise and Torque Pulsation in Permanent Magnet Synchronous Motors
Bibliographic record
Abstract
Acoustic noise and torque ripple are some of the undesirable performance indices in the design of electric motors. In fact, knowing the relationship between the two quantities can be very crucial, especially in the optimization process. For instance, where an increase in torque ripple is known to exacerbate the noise and vibrations, the two quantities could be considered as non-conflicting objectives. What this means is that minimizing one quantity could result in the optimization of the other without necessarily treating the two as mutually exclusive objectives. This paper, therefore, explores the correlation, if any exists, between torque ripple and the acoustic noise performance of the electric motor. The procedure involves exploring the design space of a 10 pole 12 slot surface-mounted permanent magnet synchronous motor to compute the torque ripple and the corresponding acoustic noise related to thousands of different motor configurations. Then, a statistical analysis is performed to establish the correlation strength between torque ripple and acoustic noise.
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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 | 0.000 |
| 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".