A Generalized Prandtl–Ishlinskii Model for Hysteresis Modeling in Electromagnetic Devices
Bibliographic record
Abstract
The cores of several types of electromagnetic devices (EMDs) experience a time-varying magnetic field. Thus, the hysteresis phenomenon can be observed between flux density and magnetic field strength. Hysteresis loop in magnetic material represents the core losses in the material core. Thus, a number of hysteresis models have been developed in the literature for core loss calculations in EMDs. In 2009, the Generalized Prandtl-Ishlinskii (GPI) hysteresis model was developed for the characterization of nonlinearities in smart material-based actuators. The GPI model has shown highly competent features compared with other hysteresis models. Therefore, this paper introduces the GPI model as an efficient hysteresis model for core loss estimation in magnetic cores and efficiency prediction in EMDs. The proposed hysteresis model is constructed with envelope functions and rate-dependent play operators. The envelope functions develop the Prandtl-Ishlinskii (PI) model to characterize hysteresis loops with saturation, while the rate-dependent thresholds support modeling of frequency-dependent hysteresis.
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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 | 0.002 | 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".