Determination of Vibroacoustic Paths Contributions in Power Transformers Using ESEA-FEM Hybrid Methodology
Bibliographic record
Abstract
Abstract In this article, a combination of experimental statistical energy analysis and finite element method (ESEA-FEM hybrid) is used to determine the amount of vibroacoustic energy transferred through the transformer experimental model structural parts and through the cooling oil. A vibroacoustic transmission path analysis was conducted separately for the winding and core vibrations in two different operating conditions, the short-circuit (SC) and the open-circuit (OC) tests. Along with these two conditions for the oil-filled tank, the same methodology was applied to the empty tank in the OC operating condition. The findings indicate that structural transmission is dominant. A fluid-borne noise component is 5 dBA less than the structure borne from the windings as a vibration source and 10.8 dBA less from the core as a vibration source. The presented methodology is novel in its statistical determination of the quantity of sound power transferred along each path in the power transformers, offering potential applications and insight into noise reduction strategies and numerical analysis and verification.
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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 | 0.001 | 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".