Adjoint-Based Design Optimization of Nonlinear Switched Reluctance Motors
Bibliographic record
Abstract
This work investigates the application of the adjoint variable method (AVM) to switched reluctance motors (SRMs). A MATLAB toolbox developed by the authors estimates the sensitivities of the required objective function with respect to different geometric design parameters using at most one adjoint simulation. In this work, the AVM evaluates the sensitivities of the x and y components of the magnetic flux density, the phase flux linkage, and the electromagnetic torque of switched reluctance motors with respect to teeth height, yoke thickness, teeth pole arc angle, and teeth taper angle of both stator and rotor. The nonlinearity of the motor magnetic material is taken into consideration. The estimated sensitivities using AVM are compared with those obtained using the more accurate but time intensive central finite differences (CFD). An interior-point optimization algorithm utilizes the sensitivities of the electromagnetic torque of an SRM to maximize the motor static torque profile. Structural mapping technique is used to control the geometric design parameters through the optimization process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".