Prediction of acoustic noise and vibration of a 24/16 traction switched reluctance machine
Bibliographic record
Abstract
This study presents a numerical modelling approach for the prediction of vibration and acoustic noise for a 24/16 traction switched reluctance machine (SRM). The numerical modelling includes the simulation of electromagnetic force in JMAG, the calculation of natural frequencies and the simulation of vibration and acoustic noise in ACTRAN. Considerations in the modelling of geometries, meshing and contacts of the 24/16 SRM are discussed to ensure the accuracy of the numerical simulation. Two‐dimensional fast Fourier transform (FFT) is applied to the radial nodal force at the stator pole tip to analyse the dominant harmonics. FFT is also applied to the simulated surface displacement of the housing and the sound pressure at 2000 rpm to analyse their dominant frequency components. The dominant harmonics for the vibration and acoustic noise at 2000 rpm are confirmed. The numerical modelling method presented in this study can also be applied to the other SRMs and electric machines to predict the vibration behaviour and the radiated acoustic noise.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".