DESIGN OPTIMIZATION AND ANALYSIS OF AFPM SYNCHRONOUS MACHINE INCORPORATING POWER DENSITY, THERMAL ANALYSIS, AND BACK-EMF THD
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
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 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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".