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Record W2987912440

Análisis de la Eficiencia en la Disipación de Calor de la Bobina de Baja Tensión de un Transformador de Potencia Funcionando en Modo ONAN/ONAF

2019· article· es· W2987912440 on OpenAlexaboutno aff
Jonathan J. Dorella, Mario A. Storti, Gustavo Ríos Rodríguez, Luciano Garelli

Bibliographic record

VenueMecánica Computacional (Asociación Argentina de Mecánica Computacional) · 2019
Typearticle
Languagees
FieldEngineering
TopicPower Transformer Diagnostics and Insulation
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhysicsPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

La vida útil y la fiabilidad de un transformador de potencia dependen en gran medida de los modos de refrigeración, ya que el calor excesivo es la principal causa del deterioro de la vida útil del aislamiento. Por esta razón, el enfriamiento óptimo es necesario para evitar la degradación tanto del aceite como del papel que constituyen el aislante en las bobinas. Los principales objetivos de este trabajo son analizar la capacidad de enfriamiento del actual diseño de la bobina de baja tensión de un transformadores de potencia de 66 [MVA], 225/26.4 [kV] funcionando en modo ONAN/ONAF de la empresa Hydro-Québec. A partir de ello, se presentarán mejoras en el diseño actual de la geometría del canal y se evaluará en relación a otro diseño de canales. La distribución de temperatura, la velocidad del aceite y la eficiencia en la capacidad de extracción del calor se estimarán a partir de los resultados obtenidos mediante simulaciones CFD en paralelo con el software Code_Saturne basado en volúmenes finitos.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.055

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.226
Teacher spread0.223 · 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 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
Published2019
Admission routes1
Has abstractyes

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