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Record W3031459355 · doi:10.1109/tdei.2020.008588

New approach for assessment of positive streamer penetration of long air gaps under impulse voltages

2020· article· en· W3031459355 on OpenAlexaff
F.A.M. Rizk

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2020
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsTheratechnologies (Canada)
Fundersnot available
KeywordsImpulse (physics)VoltageConductorElectric fieldPenetration (warfare)MechanicsElectrical conductorAir gap (plumbing)Penetration depthComputational physicsMaterials sciencePhysicsOpticsEngineeringClassical mechanics

Abstract

fetched live from OpenAlex

The paper comprises a new systematic approach to determine the critical ambient field needed for positive streamer propagation in nonuniform field gaps. It is shown that such critical streamer penetration field is a function of the basic value previously established for uniform field gaps and the degree of ambient field nonuniformity. The latter quantity is defined by the ratio of the maximum to mean ambient electric fields along the streamer propagation path, which can be determined either analytically or by charge simulation. Such functional dependence of the critical streamer penetration gradient on field nonuniformity has been determined for different rod topologies of rod-plane gap as well as for single and bundle conductor plane gaps. The model is used to determine critical streamer penetration fields and streamer lengths under different impulse voltage levels for different gap configurations and for a tall ground structure exposed to ambient impulse field. The findings of this method are compared to available experimental results and the agreement is found satisfactory.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.031
GPT teacher head0.289
Teacher spread0.258 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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