Design of an Iron Loss Tester for the Evaluation of Assembled Stator Cores of Electric Machines
Bibliographic record
Abstract
The manufacturing processes of an electric machine can lead to a deviation between predicted and actual iron losses. This work presents an iron loss tester for measuring the iron losses produced by an assembled stator core including the effects of manufacturing and actual flux distribution. The tester is designed with accessible excitation and measurement windings with a toroidal configuration enabling a high degree of flexibility in emulating various flux distributions in AC electric machines. A prototype of the proposed tester is built and the experimental results of a manufactured stator are obtained under different pole configurations showing the magnetization characteristics in different regions of the tester/stator along with the total measured iron loss (IL). A noticeable deviation between experimental and FE simulation results is present at high frequencies. As the saturation level in steel laminations is reduced, the deviation in total IL is increased. Finally, the specific IL obtained through the tester is compared against standard Epstein frame results. Importing the experimental IL data obtained through the tester to the FE model led to a close match between simulation and experimental results.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".