Application of Reduced-Order Models to the Analysis of 3-D Electrical Machines
Bibliographic record
Abstract
Three-dimensional analysis is a crucial tool for designing and optimizing electrical machines, especially those with significant 3-D effects, such as an axial flux permanent magnet (AFPM) machine. Building high-fidelity surrogate models of such topologies requires 3-D FEA. However, where the computational budget is limited for large or medium-scale parametric machine optimization, replacing 3-D FEA with models that confidently and quickly compute the 3-D performance can be extremely helpful. To resolve the 3-D issues, this article attempts to build effective high-speed reduced order models with an accuracy approaching a full 3-D model. A hybrid modeling approach is presented that couples the$2^{\frac {1}{2}}$-D modeling approaches with simple and effective magnetic equivalent circuit (MEC) models incorporating the 3-D effects. The proposed approach provides a satisfactory compromise regarding computation time and accuracy.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".