Impact of Soft Magnetic Composite Material for Traction Applications using 3D FEA
Bibliographic record
Abstract
This paper focuses on analyzing the impact of Soft Magnetic Composites (SMC) for traction applications. Three different SMC materials are compared for the same machine specification. Eddy current loss density is plotted using a 3D FEA analysis for all three different materials. The magnet and copper losses are plotted along with the total iron losses. Efficiency maps are presented for the three designs for a maximum speed range of 10000 rpm. This paper also presents a comparison of the SMC stator with the laminated stator design which is designed to fit into the same frame. In all the cases the SMC stator is designed with 60% copper fill factor, whereas the laminated stator is designed with 40% copper fill factor. The SMC stator is designed with a 3D flux carrying capability to improve the torque density by eliminating the end winding. The core loss of an SMC material is tested using a toroidal measurement setup.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".