Evaluation of computational models for electromagnetic force calculation in transformer windings using finite-element method
Bibliographic record
Abstract
Computer simulations are currently one of the most used methods on transformer’s short circuit analysis. For them to be effective, an accurate characterization of the transformer core and geometric representation of windings is essential. Hence, this work investigated the influence of core characterization and different geometric representations on magnetic flux density (MFD) and electromagnetic forces (EF) calculated during short circuits. A comparative study using simulations based on the finite-element method (FEM) were carried out for a 180 MVA transformer model. First, the influence of the nonlinear characteristic of the core B-H curve on EF was analyzed. Then, three two-dimensional (2D) axisymmetric and one three-dimensional (3D) representations were compared. Results indicate there is no significant difference in EF with a core represented by a constant value of permeability. Also, 2D-axisymmetric geometric representations underestimate radial forces and diverge significantly on axial forces in comparison with the 3D representation. Differences up to 99% between the calculated total axial forces were obtained for the analyzed cases. In addition, representations with greater level of detail result in magnetic force density up to 5.5 times greater than that obtained with the simplified representation.
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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 | 0.001 | 0.002 |
| 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) | 0.003 | 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".