High Precision Modeling of Nonlinear Lossy Magnetic Devices
Why this work is in the frame
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Bibliographic record
Abstract
The paper proposes a high efficiency and totally feasible tool that permits a rigorous study of magnetic devices, being intended firstly to the research and design activity. The method uses the most modern tools in circuit analysis, being a result of a long-time experience of authors. It deals with an accurate modeling that permits to find the distribution of magnetic fluxes everywhere in the core, at any time. The modeling is made through an equivalent electric circuit described by the same differential-algebraic equation system as the studied device in transient regime. Therefore, this circuit is analyzed using dedicated software, like SPICE. Such software has special capabilities to prevent convergence problems that usually occur in nonlinear DAE solving, as well as performing optimizations of certain parameters. The method permits not only the rigorous study of the magnetic device, but additionally a high precision analysis of complex equipments that contain magnetic devices
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it