Design Optimization of Axial Flux Permanent Magnet Brushless DC Micromotor Using Response Surface Methodology and Bat Algorithm
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Bibliographic record
Abstract
This paper presents design optimization technique of Axial Flux Permanent Magnet (AFPM)Brushless DC (BLDC)micromotor. The objective of the optimization process is to minimize the motor volume and improve joules efficiency with the constraints of minimum required torque and maximum back EMF using response surface modeling and Bat Algorithm (BA). Finite element computations have been used for numerical experiments on geometrical design variables in order to evaluate the coefficients of a second-order empirical model for the response surface representation. BA is used as an optimization technique to minimize volume and improve joules efficiency in separate optimization problem in terms of design variables. The optimization results were compared with other metaheuristic algorithms, including Genetic Algorithms (GA), and Particle Swarm Optimization (PSO). The bat algorithm shows a potential competitive against GA and PSO.
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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 | 0.001 | 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) | 0.000 | 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