DESIGN OPTIMIZATION AND ANALYSIS OF AFPM SYNCHRONOUS MACHINE INCORPORATING POWER DENSITY, THERMAL ANALYSIS, AND BACK-EMF THD
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Bibliographic record
Abstract
This paper presents the design and analysis of an inside-out axial-flux permanent-magnet (AFPM) synchronous machine optimized by genetic algorithm (GA) based sizing equation, finite element analysis (FEA) and finite volume analysis (FVA). The preliminary design is a 2-pole-pair slotted TORUS AFPM machine. The designed motor comprises sinusoidal back-EMF waveforms, maximum power density and the best heat removal. The GA is used to optimize the dimensions of the machine in order to achieve the highest power density. Electromagnetic field analysis of the candidate machines from GA with various dimensions is then put through FEA in order to obtain various motor characteristics. Based on the results from GA and FEA, new candidates are introduced and then put through FVA for thermal behavior evaluation of the designed motors. Techniques like modifying the winding configuration and skewing the permanent magnets are also investigated to attain the most sinusoidal back-EMF waveform and reduced cogging torque. The performance of the designed 1 kW, 3-phase, 50 Hz, 4-pole AFPM synchronous machine is tested in simulation using FEA software. It is found that the simulation results fully agree with the designed technical specifications. It is also found from FVA results that the motor temperature reaches
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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 it