Optimal Design and Simulation of an Axial Flux Machine for Unmanned Aerial Vehicles
Bibliographic record
Abstract
Abstract Currently, unmanned aerial vehicles (UAVs) are present in numerous sectors of the economy, which require them to carry out services with agility, safety and precision. This equipment can be designed in the multi-rotor concept, using several electric motors, normally with permanent magnets and radial flux. Another alternative would be to use axial flux electric motors, which have a potentially higher power density and help to reduce the weight of the UAV. In addition, the use of grain-oriented steel in the mo-tor stator contributed to reducing magnetic losses and increasing aircraft efficiency. Thus, this work proposes the development of a permanent magnet axial flux motor, using grain-oriented steel in a yokeless and segmented armature. The technical specifications for a UAV application in agriculture are presented, as well as the mathematical modeling required for optimized motor design. Finally, the developed project is validated through 3D finite element electromagnetic field simulations, demonstrating the feasibility of using rectangular teeth to simplify the stator assembly 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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".