Advanced testing and modeling of magnetic materials including a new method of core loss separation for electrical machines
Bibliographic record
Abstract
This paper presents a new method for the separation of core loss components (hysteresis and eddy current) in laminations exposed to high frequency excitations. Accurate separation of core losses is achieved by calculating the hysteresis losses at each frequency taking into account the non-uniform flux distribution inside the lamination. The results highlight that the assumption of constant hysteresis energy loss per cycle is only valid at low frequencies, where skin effect is negligible. The developed model is then used to study the effect of the annealing process on core loss components in laminations exposed to high frequency excitations. Core loss measurements are performed on different laminations at several frequencies in the range of 20 Hz - 4000 Hz. A comparison of the separated core loss components shows that a huge reduction in the hysteresis loss is achieved by annealing, while the annealing process increases the eddy current loss component at high frequencies and high flux densities. The results are then analyzed by comparing the separated eddy current loss with an analytical eddy current loss model that accounts for the non-uniform distribution of the magnetic field.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".