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Record W4409448191 · doi:10.1115/1.4068440

Determination of Vibroacoustic Paths Contributions in Power Transformers Using ESEA-FEM Hybrid Methodology

2025· article· en· W4409448191 on OpenAlexaff
Karlo Petrović, Antonio Petošić, Tomislav Župan

Bibliographic record

VenueJournal of vibration and acoustics · 2025
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsSiemens (Canada)
Fundersnot available
KeywordsFinite element methodTransformerStructural engineeringVibrationComputer scienceEngineeringMechanical engineeringAcousticsElectronic engineeringElectrical engineeringPhysicsVoltage

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.833
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.316
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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