Sizing of the Motor Geometry for an Electric Aircraft Propulsion Switched Reluctance Machine Using a Reluctance Mesh-Based Magnetic Equivalent Circuit
Bibliographic record
Abstract
The switched reluctance motor (SRM) is a promising candidate for electric propulsion systems. In the design process of an SRM, the motor geometry needs to be determined. Using the finite element method (FEM) might be time-consuming for the sizing of the motor geometry. As an alternative, electromagnetic models based on a magnetic equivalent circuit (MEC) can be utilized for the sizing of an SRM. MEC models require fewer computational resources and can help determine the electromagnetic performance with reasonable accuracy. Using the conventional MEC method for SRM sizing might be challenging since the flux pattern inside the motor should be changed for different motor dimensions. In order to address this challenge, this paper applies a reluctance mesh-based MEC technique to determine the geometry of a three-phase 12/16 SRM for a high-lift motor in the NASA Maxwell X-57 electric aircraft. A comprehensive reluctance mesh-based MEC model is developed for this purpose. Both the static and dynamic characteristics of the SRM geometry are evaluated using the reluctance mesh-based MEC method. The determined geometry is verified using the results computed from FEM.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".