General formulation of core loss in soft magnetic materials including effects of eddy current and magnetostriction
Bibliographic record
Abstract
A general formulation of the energy dissipation in soft magnetic materials was established, and the contributions from both eddy currents and magnetostriction were compared using this formulation. The energy dissipation of soft magnetic materials has been discussed based on the Steinmetz equation, considering contributions from coercivity and eddy current. However, recently, it was reported that magnetostriction can also generate energy dissipation due to changes in strain corresponding to the domain-wall motion. Hence, a modification of the Steinmetz equation is indispensable for better understanding the mechanism of energy dissipation in soft magnetic materials. Our general formulation elucidates the origin of magnetic energy dissipation across various soft magnetic materials. The results provide a novel explanation for the mechanisms underlying the damping of the domain-wall motion. • The general equation of the energy loss of the soft magnetic materials was formulated considering the effects of coercivity, eddy current, and magnetostriction. • Our general formulation reproduced experimental energy losses. • Prediction of the dominant loss mechanisms in practical materials can develop efficient magnetic cores. • Excess losses are significantly decreased by the increase in the number of domain walls in the soft magnetic materials with the small magnetostriction. • The results provide design criteria for soft magnetic materials considering the effects of magnetostriction and eddy current.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".