Radial Force Shaping for Acoustic Noise Reduction in Switched Reluctance Machines
Bibliographic record
Abstract
Robustness, simple construction, and low cost are some of the advantages of switched reluctance machines (SRMs). These are all desirable characteristics of an electric motor, especially in the automotive sector, where high-temperature and high-speed operation, and low cost are always in demand. However, the acoustic noise generation by conventionally controlled SRMs can prevent its use in applications where acoustic comfort is required. Acoustic noise is radiated by the stator frame when a vibration mode is excited by the respective spatial order at a forcing frequency that is close to the stator's modal natural frequency. The excitation surface wave is the radial force density waveform, which is a function of time and spatial position. In this paper, a phase radial force shaping method is proposed by using harmonic content analysis. A generic function for the radial force shape is identified, whose parameters are calculated by an optimization algorithm to minimize the torque ripple for a given average torque. From the phase radial force profile, a current reference is obtained. The proposed methodology is experimentally validated with a four-phase 8/6 SRM through acoustic noise measurements at different speed and load conditions.
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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) | 0.001 | 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) | 0.001 | 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".