Optimisation‐based procedure for characterising switched reluctance motors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study introduces an optimisation‐based procedure for characterising switched reluctance machine (SRM) performance and studies the optimisation to determine the conduction angles in SRM drives. The objectives employed in the optimisation cases are maximising average output torque, maximising the ratio of average torque over root mean square (RMS) value of phase current, and minimising RMS value of net torque ripple. Combinations of these objectives are used in four different cases, which are formulated either as single‐ or multi‐objective problems. These cases are then compared in terms of output torque, torque ripple, and efficiency. One method of the four is selected and the performance of the motor over the entire operating range is characterised based on optimised turn‐on and turn‐off angles. Experimental results are used to verify the motor performance obtained from the optimisations for selected operating points.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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) | 0.000 | 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 it