Reducing torque ripple of switched reluctance machines by relocation of rotor moulding clinches
Bibliographic record
Abstract
Switched reluctance machines (SRMs) are of great interest because of their simplicity, low-cost, reliability, robustness, fault-tolerance and extended-speed constant-power operation. However, conventional SRMs suffer from high torque ripples. There exist several methods, which have been proposed to reduce torque ripples. One of the proposed methods is to change the geometric structure of the machine. However, analysis of the state-of-the-art designs show that, despite achieving favourable results in applications, the moulding pins of the machines are normally neglected. A motor that gives positive results may get affected negatively by its random moulding during its manufacturing. In this paper, mitigation of torque ripples in short-pitched SRMs (SPSRMs) and fully-pitched SRMs (FPSRMs) are investigated. Three-phase SPSRM and FPSRM are chosen for this study and the effects of the geometric points of moulding pins in the machines are studied comparatively. Maxwell 2D program is used for the analysis and two different models are compared for both SPSRM and FPSRM. The obtained results show that the torque ripples of the two machines are lower when moulding pins are closer to the rotor position. It is reduced by 2.56% at 10 Amps in the proposed SPSRM and 12% at 6 Amps in the proposed FPSRM. It is also observed that the applied method is more effective in reducing torque ripples of FPSRMs than SPSRMs.
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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 | 0.000 |
| 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) | 0.001 | 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".