A Nonlinear Finite-Element Analysis Tool to Prevent Rupture of Power Transformer Tank
Bibliographic record
Abstract
High-energy internal failures of transformers are catastrophic events which are hardly predictable. For this reason, a full-scale controlled experiment represents a valuable learning opportunity to gather accurate information about sequence of events during the very short time in which the failure occurs. Controlled parameters include tank design, material properties, experimental load and measurements. In this paper, we present a detailed investigation using nonlinear finite-element analysis of a 210-MVA transformer high-pressure experiment. We begin by evaluating the relationship between internal arcing pressure rise and tank expansion characteristics. Since this relationship is not linear due to geometric and material nonlinearities, an iterative process is proposed to ensure result accuracy. Stress–strain material properties are retrieved by tension experiments of specimens extracted from the tested tank to enable accurate comparison of numerical and experimental results. It is shown in this paper that nonlinear material parameters have a small influence on the tank pressure rise, but a significant one on large strain prediction and therefore the true stress-strain curve is recommended. In addition, the ductile rupture criterion based on the ultimate plastic strain of the material correlates with the experimental and explicit dynamic analysis results. This can ensure a certain design margin for tank rupture prevention.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".