Improving the Swelling Phenomenon in the Dynamic Jiles-Atherton Hysteresis Model Using Magnetic Viscosity
Bibliographic record
Abstract
Iron losses are a major source of inefficiency in electrical devices, such as transformers and rotating machines.Accurately estimating iron losses is essential for optimizing the performance, energy efficiency, and cost-effectiveness of these devices.The swelling phenomenon is a significant factor that affects the accuracy of iron loss estimation.This phenomenon appears in the saturation region of the hysteresis loop under high-frequency regimes and can significantly affect the magnetic properties of the device.This paper presents a novel methodology for addressing the swelling phenomenon and improving the accuracy of iron loss estimation.The methodology is based on the Jiles-Atherton model, which is a well-established model for describing the hysteresis phenomenon in ferromagnetic materials.The methodology is improved by incorporating a novel formulation of the excess field based on magnetic viscosity.The Bertotti approach is used to account for the dynamic effects of the swelling phenomenon.The fmincon algorithm is used to identify the parameters of the Jiles-Atherton model in both quasi-static and dynamic regimes.This algorithm is a MATLAB®-based constrained optimization method that is used to find the set of parameters that minimizes the error between the simulated and measured hysteresis loops.The obtained results show that the proposed methodology is able to accurately estimate iron losses under both quasi-static and dynamic regimes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".