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Record W4205942446 · doi:10.3390/en15030762

Influence of Ester Liquids on Dielectric Strength of Cellulose Kraft Paper

2022· article· en· W4205942446 on OpenAlexaff
Gottapu Tirupati Naidu, U. Mohan Rao, Sudabattula Suresh

Bibliographic record

VenueEnergies · 2022
Typearticle
Languageen
FieldEngineering
TopicPower Transformer Diagnostics and Insulation
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsWettingDielectricKraft paperMineral oilMaterials scienceComposite materialDielectric strengthElectrical insulation paperDiffusionCelluloseVoltageElectrical engineeringChemistryThermodynamicsOrganic chemistryMetallurgyOptoelectronics

Abstract

fetched live from OpenAlex

Generally, impregnation of solid insulation is performed to increase the dielectric strength and reduce the dielectric losses of solid insulants. This increase in dielectric strength depends on the oil’s diffusion and dielectric properties. This paper investigates the diffusion behavior of mineral oil and ester fluids (synthetic, natural, and mixed) to understand the influence of oil diffusion on paper breakdown voltage. To better understand this phenomenal influence, cellulose insulation paper of different thicknesses has been considered. Wetting characteristics of various oil-paper insulation systems were investigated with and without thermal stressing. Thermal aging was carried out as per modified ASTM D1934 at 110 °C, 140 °C, 160 °C, and 185 °C respectively for 2 weeks. The wetting characteristics and influence of different oils on paper breakdown voltage were explicitly reported. It is inferred that paper wetting characteristics are attributable to the type of oil, the thickness of paper, and the aging factor of oil-paper insulation. Importantly, the increase in paper dielectric strength and diffusion behavior for ester fluids is found to be superior to that of the mineral insulating oils.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.004
GPT teacher head0.188
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations10
Published2022
Admission routes1
Has abstractyes

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