Design and Evaluation of PM Vernier Machine for Urban Air Mobility Propulsion Applications
Bibliographic record
Abstract
For aircraft propulsion motors, the torque and power density requirements are highly demanding and beyond what is currently achievable. This article intends to thoroughly examine the feasibility of a surface PM vernier machine (SPMVM) for electrical vertical takeoff and landing (eVTOL) applications, where very high specific torque (torque per mass) is required. It was shown that, in contrast to conventional PM machines, the performance of SPMVM is quite sensitive to certain design parameters, including stator slot geometry and PM dimensions. The implications of various design characteristics of SPMVM are discussed, which ultimately guides the necessary design philosophy in order to attain higher specific torque levels as well as improved power factor. The achievable specific torque, efficiency, and power factor were also shown to vary with the choice of the slot–pole combination. Following the outlined design guidelines, two DD SPMVMs featuring distinct slot–pole combinations have been designed, together with a conventional PM machine serving as a reference model, all rated at 204 kW at 1300 r/min. A comprehensive comparison of the electromagnetic performance between the designed SPMVMs and the reference model is presented. The designed SPMVMs can attain a specific torque of approximately 50 Nm/kg, nearly double the specific torque obtainable from a conventional PM machine. To further assess the feasibility of the designed SPMVMs, a thermal analysis of the designed machines is also conducted.
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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 | 0.001 |
| 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 | 0.001 | 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".