Comparative Study on the Radial Force and Acoustic Noise Harmonics of an Interior Permanent Magnet, Induction, and Switched Reluctance Motor Drive
Bibliographic record
Abstract
The demand for electrical machines has been spurred by rising fuel costs, technological advancements, and urbanization. Consequently, there is an increased focus on developing machines with greater power density, improved efficiency, and enhanced vibroacoustic characteristics while promoting a more sustainable supply chain. This paper offers a thorough comparison of three distinct motor topologies: switched reluctance motor (SRM), interior permanent magnet synchronous motor (IPMSM), and induction motor (IM). The study begins with electromagnetic analysis of the motors, covering torque, output power, losses, and efficiency calculations. Subsequently, the investigation delves into radial force density waveforms, extracting the dominant radial force density harmonics and presenting patterns for each motor’s dominant radial force harmonics. Following this, a 3D assembly of the motors is introduced for modal analysis, determining their mode shapes and natural frequencies. Finally, vibroacoustic analysis is conducted for the proposed motors, with observed differences being discussed. These analyses are conducted solely for the rated operating point, as transitioning to other operating points does not alter the pattern of radial force harmonics or the correlation between acoustic noise and radial force harmonics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".