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Record W4230929265 · doi:10.1109/intmag.2015.7156698

Adaptive meshing for eddy current calculations

2015· article· en· W4230929265 on OpenAlexaff
Delphine Dupuy, D. Pedreira, Demmy Verbeke, Vincent Leconte, P. Wendling, Loïc Rondot, Vincent Mazauric

Bibliographic record

Venue2015 IEEE Magnetics Conference (INTERMAG) · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsCenter for Diagnosis and Research on Alzheimer's Disease
Fundersnot available
KeywordsEddy currentTransient (computer programming)Computer scienceCurrent (fluid)Power (physics)Energy (signal processing)Electronic engineeringMechanicsEngineeringElectrical engineeringPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Eddy currents are at the origin of losses and signal distorsions in power electrical. In order to address their considerable impacts on both the energy efficiency and the performance requirement, eddy current modeling and its accuraccy are discussed from a thermodynamic approach. Whereas adaptive meshing strategies were extensively used in static cases [1,2], some valuable results are carried out in an induction machine case-study, providing a general validation for transient regimes [3].

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)0.003
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: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.143
GPT teacher head0.336
Teacher spread0.193 · 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
GenreMethods

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

Citations1
Published2015
Admission routes1
Has abstractyes

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