Numerical investigation of transient potential distribution along stress-grading on stators bars (Roebel Type)
Bibliographic record
Abstract
This paper presents the potential distribution along the stress-grading system (Roebel Bar Type) under 50 Hz sinusoidal AC voltage based on the finite element method (FEM). In order to reduce the number of mesh elements and simulation time, a comparative study of two different approaches was examined using commercial software based on the Finite Element Method. The first one is a volume approach which takes into account the thickness of the stress-grading system. The second is the surface approach where the stress-grading system is treated as a specific boundary condition. The investigations have focused on the comparison of the surface potential and electric field distributions along the stress-grading system as well as the number of mesh elements and simulation time using 2D-axisymetric finite element method. The results obtained showed that the surface approach is the best method as it provides the same results as the volume approach with less mesh elements. In addition, a good agreement was found qualitatively as well as quantitatively between numerical and experimental results. Thus, the numerical study provides an alternative to calculate the surface potential distribution along the stress grading with sufficient accuracy using surface approach.
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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 | 0.001 |
| 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) | 0.001 | 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".