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Modelling and Electrothermal Simulation of Temperature Rise in a SF6-Free MV Circuit-Breaker

2025· article· en· W4410297711 on OpenAlexaff
Mactar Thiam, Arianne Lemo, Karl-Igor Pierre, A. Skorek

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicVacuum and Plasma Arcs
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsCircuit breakerTransient recovery voltageElectrical engineeringResidual-current deviceMaterials scienceComputer scienceVoltageEngineeringPower factor

Abstract

fetched live from OpenAlex

Active research for replacement of SF6 is more complex and requires the validation of many criteria to ensure proper operation of the circuit-breaker under normal conditions. Among these criteria, there is the temperature rise of the conductors which must not exceed a limit in accordance with the IEC or IEEE standard. After completion of dielectric type tests, it was proved that pure nitrogen at 2.5 bar can replace SF6 at 1.5 bar in a VOX 38kV outdoor circuit breaker by meeting the dielectric requirements. This article deals with the simulation of the temperature rise in different elements of the circuit breaker with these two gases: SF6 and N2. The coupling of computational fluid dynamics and computational electro magnetics and non-isothermal flow are used to determine the temperature rise of the breaker. Simulation tests were performed at rated nominal current 2000 A, 50 Hz with specifications of IEEE and IEC standards and finally, the requirements were met with SF6 at 1.5 bar whose results are in good agreement with those of the experimental tests carried out and allowing to predict the results with N2 at 2.5 bar with numerical model build. The results with N2 show a better thermal behavior of all the conductors of the breaker than with SF6 and conclude that pure nitrogen at 2.5 bar can replace SF6 at 1.5 bar in a 38kV outdoor circuit-breaker.

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 score0.135
Threshold uncertainty score0.251

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 head0.008
GPT teacher head0.226
Teacher spread0.218 · 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
Published2025
Admission routes1
Has abstractyes

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