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Effect of Voltage Level on the Performance of Silicone Rubber in the Inclined Plane Tracking and Erosion Test

2022· article· en· W4311944541 on OpenAlexaff
Basharat Mehmood, Refat Atef Ghunem, Ayman El‐Hag, Mohammad K. Hassan, Leena Al‐Sulaiti, Ahmed Abdala

Bibliographic record

Venue2022 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP) · 2022
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsNational Research Council CanadaUniversity of Waterloo
FundersQatar National Research Fund
KeywordsSilicone rubberComposite materialMaterials scienceInclined planeNatural rubberErosionVoltageHigh voltageTracking (education)Electrical engineeringGeologyEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

This paper investigates the effect of the test voltage level on the performance of silicone rubber in inclined plane tracking and erosion test. Silicone rubber composites, filled with either alumina tri-hydrate or ground silica to 30 wt% or 50 wt%, are tested in inclined plane tracking and erosion test under 2.5 kV, 3.5 kV, and 4.5 kV. The degradation patterns of the tested silicone rubber surfaces are found dependent on the test voltage level during the inclined plane tracking and erosion test, as the dry-band arcing on silicone rubber tends to form tracks under relatively mild test voltages and deep erosion under the critical test voltage. These findings confirm the importance of employing the critical voltage while evaluating the erosion resistance of silicone rubber in the inclined plane tracking and erosion test. In addition, the critical test voltage of silicone rubber is found dependent on the amount of filler added to the composite.

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)0.002
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.033
GPT teacher head0.261
Teacher spread0.228 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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