High‐dimensional optimal design of dual‐rotor synchronous reluctance machines based on data‐driven torque decomposition
Bibliographic record
Abstract
Abstract The multi‐objective optimal design of double‐sided stator dual‐rotor synchronous reluctance machines (DSS‐DRSynRMs) is a challenging high‐dimensional problem. The objective of this paper is to present a new optimal design method based on data‐driven models and the principle of torque decomposition addressing the aforementioned issue. For this purpose, a 26‐parameter optimisation problem is solved by employing the proposed method consisting of three sequential phases. Through the proposed method, the combination of artificial neural network (ANN) and recently introduced waveform targeting surrogate model (WTSM) strategy is investigated to mitigate the computational complexity of the optimisation process. Furthermore, the electromagnetic performance of the final optimal design has been comprehensively analysed showing a significant reduction in torque ripple rate and improved torque density. Moreover, the computational efficiency of the proposed method has been compared to the popular multi‐level multi‐objective optimisation method. From the discussion, it can be found that the proposed method provides a reduced computation time and wider search space.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".