The study on the effects of the height of the core’s window on the leakage reactance of transformers using finite element analysis
Bibliographic record
Abstract
The short-circuit transformer’s behaviour can be easily predicted from its leakage reactance. The relationship between the height of the core window and leakage reactance of the transformers according to almost all of the analytical methods is assumed to be constant under different heights of the core window. This approach is not significantly accurate during severe cases such as those has small differences between the core height and winding height. Finite element analysis is computer software which can help manufacturers and engineers to speed up the process of optimising electrical machines. In this work, a finite element method is used to see the effect of the window height on the leakage reactance of the transformer under the different heights of the window of the core. The three-dimensional methods are used to evaluate the leakage reactance of the transformer for different heights of the core window. The results show that the transformer’s leakage reactance widely varies during the change in the height of the core window for the same geometry of the windings and electrical parameters of the transformer. Hence using analytical methods could result in inaccurate results. Moreover, considering the same ratio between the window height and windings height is no longer valid for all of the transformers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".