Prediction of Iron Losses Using Jiles–Atherton Model With Interpolated Parameters Under the Conditions of Frequency and Compressive Stress
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Bibliographic record
Abstract
The operating conditions, such as the frequency of excitation and mechanical stress in electrical machines, severely affect the magnetic behavior of ferromagnetic cores, which translates into increased iron losses. The physics-based hysteresis models, such as the Jiles-Atherton (JA) model, can incorporate the effects of operating conditions on iron losses and can be embedded in the finite-element simulations. In this paper, we have implemented the JA model to predict iron losses, and the effect of the frequency of the excitation waveform and compressive stress on the JA model parameters has been investigated. A simple approach is proposed to predict iron losses for any value of frequency and compressive stress using the original JA model equation. This approach not only reduces the computational complexity of the problem, but also reduces the amount of material information required.
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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