Sensitivity Analysis for Multi-Objective Optimization of Switched Reluctance Motors
Classification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".
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
The main issue of the switched reluctance motor (SRM) is its noise and vibration caused by high torque ripples on the rotor’s shaft. Many methods have been developed for improving the torque characteristic of the SRM. For example, design optimization is one of the promising approaches to the noise and vibration reduction of the SRM. Particularly, topology optimization (TO) of the stator and rotor can be highly beneficial to addressing the torque ripple issue. However, the TO of the SRM appears to be computationally demanding. To overcome this issue, this study proposes a method aiming to reduce the computational complexity of the TO through the reduction of the design space. Particularly, this paper presents a sensitivity analysis of a list of unique design parameters of the SRM and their influence on the average torque of the motor and the torque ripple of the motor. By applying the sensitivity analysis, the design space of the TO could be reduced, leading to a considerable decrease in the TO computational burden. Additionally, valuable conclusions on the geometrical parameters’ influences on the SRM torque and torque ripple have been drawn.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.000 | 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