Accuracy of time domain extension formulae of core losses in non‐oriented electrical steel laminations under non‐sinusoidal excitation
Bibliographic record
Abstract
This study presents a comparative study on the accuracy of three iron loss prediction models. The models are based on the decomposition of core or iron losses into the hysteresis and the eddy current loss components. The time domain extensions of two frequency domain models have been used to predict the iron losses due to a number of non‐sinusoidal waveforms with and without the presence of minor loops. A third model, by Boglietti, that has been proposed recently to predict core losses for non‐sinusoidal and pulse‐width modulated (PWM) waveforms has also been studied. The unknown coefficients of each model have been determined by data fitting iron losses obtained from Epstein frame experiments for induction levels and fundamental frequencies up to 1.6 T and 2 kHz, respectively. Core losses due to PWM waveforms have been measured at various fundamental and switching frequencies in unipolar and bipolar modes. The experimentally measured iron losses have been compared to those predicted using the three models and the accuracy and applicability of each model have been discussed.
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 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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".