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Record W3206510922 · doi:10.1109/07ias.2007.152

Parallel CFD Analysis of Conjugate Heat Transfer in a Dry Type Transformer

2007· article· en· W3206510922 on OpenAlexaff
Carlos R. Ortiz, A. Skorek, Michel Lavoie, Pierre Bénard

Bibliographic record

VenueConference record · 2007
Typearticle
Languageen
FieldEngineering
TopicInduction Heating and Inverter Technology
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Trois-RivièresPolytechnique Montréal
Fundersnot available
KeywordsTransformerMechanicsComputational fluid dynamicsElectromagnetic coilHeat transferFluentNatural convectionAirflowMechanical engineeringComputer scienceElectrical engineeringEngineeringPhysicsVoltage

Abstract

fetched live from OpenAlex

In this paper we present the conjugate heat transfer analysis in a 167 kVA dry type transformer using the parallel version of the CFD code Fluent 6.0. The RNG kappa-epsiv model is proposed to compute the turbulent aspect of the convective airflow inside the transformer metal tank, for ANAN (air natural air natural) cooling conditions. An experimental approach was used to assess Joule losses in the low/high voltage windings and Eddy currents losses in the magnetic core. The resulting mathematical model was solved using 14 compute nodes on a distributed machine.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.248
Teacher spread0.222 · 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

Citations3
Published2007
Admission routes1
Has abstractyes

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