3-D Sub-Domain Analytical Model to Calculate Magnetic Flux Density in Induction Machines With Semiclosed Slots Under No-Load Condition
Bibliographic record
Abstract
In this paper, a novel 3-D sub-domain analytical model is developed to determine magnetic flux distribution in single-cage induction machines (IMs) with skewed rotor bars under no-load condition in an effort to more detailed analysis of spatial harmonics. The proposed model, along with an optimization algorithm, is as an alternative solution to finite-element analysis (FEA) in optimizing the geometry of IMs. The analytical method is based on the resolution of 3-D Laplace and Poisson's equations in cylindrical coordinates using the separation of variables method to calculate the magnetic vector potential for corresponding sub-domain. The proposed model includes the effect of slotting and tooth tips for the stator and rotor slots, which is usually neglected in a 2-D analysis due to the complexity of differential equations. Also, the proposed 3-D model can be used for any slot-pole combination in addition to considering the asymmetrical effect in the axial direction, which is a source of noise, vibration, and excessive losses in IMs. To evaluate the performance of the proposed 3-D analytical model, calculated magnetic-field distribution is compared with the results obtained from the 3-D FEA.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".