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Record W4285537938 · doi:10.1049/icp.2022.0412

Influence of aging on prebreakdown characteristics of ester liquids with experimental and statistical analysis

2022· article· en· W4285537938 on OpenAlexaff
T. Jayasree, U. Mohan Rao, I. Fofana, S. Brettschneider, Esperanza Mariela Rodriguez-Celis, Patrick Picher

Bibliographic record

VenueIET conference proceedings. · 2022
Typearticle
Languageen
FieldEngineering
TopicPower Transformer Diagnostics and Insulation
Canadian institutionsHydro-QuébecUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsMaterials scienceElectrodeDegradation (telecommunications)ThermalComposite materialStatistical analysisStress (linguistics)RADIUSVoltageChemistryThermodynamicsElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

This study emphasizes the pre-breakdown phenomenon of low pour point synthetic esters with thermal degradation for potential usage in cold climatic regions. It is to be mentioned that two synthetic esters having a pour point less than -60°C are chosen for this work. The investigations are aimed at understanding the pre-breakdown behaviour of these very low pour point liquids at room temperature. The testing is performed using a point-plane electrode configuration under AC stress in a hermetically sealed test cell. To understand the influence of thermal degradation, the insulating liquids are subjected to a controlled thermal stressing under sealed conditions. The measurements are reported at different thermal aging duration (2 weeks, 4 weeks, and 6 weeks) at a tip of radius 5.46 μm and 10.75 μm with an inter-electrode distance of 10 mm. The measurements include partial discharge inception voltage, breakdown-streamer-inception voltage, and acidity of the liquids at different aging conditions. The influence of thermal degradation on the pre-breakdown characteristics of the fluids is reported. In addition, a two-way ANOVA (with interaction) statistical analysis has been performed on the experimental data.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.393

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2022
Admission routes1
Has abstractyes

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