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Record W4387506421 · doi:10.23977/jeeem.2023.060503

Simulation of Electric Field Distribution in Intermediate Cold-Shrink Joints of 110kV Cables

2023· article· en· W4387506421 on OpenAlexvenueno aff
Runzhong Miao, Lun Wang, Jia-Hao Yin

Bibliographic record

VenueJournal of Electrotechnology Electrical Engineering and Management · 2023
Typearticle
Languageen
FieldEngineering
TopicThermal Analysis in Power Transmission
Canadian institutionsnot available
Fundersnot available
KeywordsElectric fieldField (mathematics)Division (mathematics)Process (computing)EngineeringComputer simulationJoint (building)Electric power transmissionTransmission (telecommunications)Structural engineeringElectrical engineeringComputer scienceSimulation

Abstract

fetched live from OpenAlex

With the development of the city, the city network cable rate gradually increased, power cables in the transmission and distribution network in the status of the rising, how to ensure the stable operation of the cable has become a growing concern of the subject. In order to ensure the safe and stable operation of the cable, in its design and manufacture before the need to understand its electric field distribution between the head, so as to better ensure the stable operation of the cable. This paper is based on ANSYS software to build the calculation model of the electric field of cable intermediate joints. Systematically on the cold shrink type intermediate joint for stress cone control occasions under the electric field numerical simulation. The specific simulation process includes geometric configuration, area division, mesh division, boundary assignment, driving calculation and solving the electric field poles. The results of the simulation show that the cold shrinkable cable joints have a good effect of electric field stress evacuation and a good effect on the improvement of the electric field.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.003
GPT teacher head0.202
Teacher spread0.198 · 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 designSimulation or modeling
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
Published2023
Admission routes1
Has abstractyes

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