A Study of the Relationship between Acoustic Noise and Torque Pulsation in Permanent Magnet Synchronous Motors
Bibliographic record
Abstract
Acoustic noise and torque pulsation, also known as 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 cycle of the electric machine. 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, attempts to explore 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 relationship between the two quantities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".