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Record W2903132432 · doi:10.1109/ceidp.2018.8544739

Comparison of the Volume and Surface Approaches to Compute Temperature and Electric Field Along the Stress-Grading on Stators Bars

2018· article· en· W2903132432 on OpenAlexaff
G. Kone, C. Volat

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsMultiphysicsFinite element methodElectric fieldSurface stressBoundary value problemRotational symmetryStatorMechanicsMaterials scienceFinite volume methodMechanical engineeringPhysicsSurface (topology)Structural engineeringEngineeringGeometryMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

This paper describes the comparison between volume and surface approaches to compute electric field and temperature distributions along the stress-grading system on Stators Bars (Roebel Type) under 60 Hz sinusoidal ac voltage. For the surface approach, a specific boundary condition available in the commercial FEM software Comsol Multiphysics <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> was used to model the stress-grading system whereas for the volume approach, the thickness of each component is taken into account. The investigations were focused on the comparison of the axial electric field and surface temperature distributions along the stress-grading system using 2D axisymmetric FEM model of a stator bar (Roebel Type). The results obtained demonstrate that the surface approach can be a good alternative to the volume approach to compute the electric field and temperature distributions along the stress grading system. Thus, the numerical surface approach study provides an alternative to calculate the axial electric field and surface temperature distributions along the stress-grading system without numerical instability. In addition, the numerical results obtained using surface approach was confirmed experimentally.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.191

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 head0.047
GPT teacher head0.256
Teacher spread0.208 · 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 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

Citations2
Published2018
Admission routes1
Has abstractyes

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