Design of PM/Reluctance Synchronous Machines Based on Additively Manufactured Hilbert Structures
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-4431.vid Electric motors of high specific powers are essential in realizing the electrification of aeronautical propulsion systems. A novel lightweight high-performance rotor design is proposed for permanent magnet/reluctance synchronous motors featuring a thin-wall structured Hilbert geometry that is enabled by recent advances in additive manufacturing of soft magnetic materials. The thin-wall Hilbert geometry exhibits a degree of structural magnetic anisotropy that offers a unique opportunity for enhancing the performance of electric motors which rely on non-isotropic magnetic reluctance for generating torque, including both synchronous reluctance motors (SynRMs) and permanent magnet-assisted synchronous reluctance motors (PM-assisted SynRMs). To demonstrate the benefits of the present design, electromagnetic simulations are performed using 3D finite element analysis and based on that a conceptual rotor design is proposed following the analysis. In addition to motor torque enhancements as a result of increased saliency ratio, the proposed design also yields beneficial reductions in motor weight and eddy current losses while providing passages for integrating high-performance cooling systems, all of which are key enablers for achieving high specific-powers needed for aircraft electric propulsion.
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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 | 0.000 |
| 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 | 0.001 |
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".