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Record W4323640942 · doi:10.5281/zenodo.7709901

Interfacing Techniques for Electromagnetic Field and Circuit Simulation Programs

2009· article· en· W4323640942 on OpenAlexfundno aff
IEEE Task Force on Interfacing Techniques for Simulation Tools

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2009
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInterfacingElectromagnetic fieldField (mathematics)Computer scienceElectrical engineeringElectronic engineeringEngineeringComputer hardwarePhysicsMathematics

Abstract

fetched live from OpenAlex

Interfacing of disparate simulation programs is increasingly undertaken to gain a deeper understanding of their functionality and exploit their merits for modeling complex systems. In this paper, techniques for interfacing field and circuit equations of low-frequency electromagnetic apparatus are reviewed, analyzed, and classified into two main categories of direct and indirect methods according to the coupling method. Each category includes a vast array of techniques employing different methods for circuit modeling. The field analysis is restricted to 2-D finite-element method, which is a widely used numerical technique for modeling magnetic behavior of power apparatus. The main features and problems associated with each technique are summarized. Methods for coupling of mechanical equations are addressed in a separate section. A comprehensive list of references is also included at the end of this paper to provide further information to the readers.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.022
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0220.005

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 head0.019
GPT teacher head0.237
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2009
Admission routes1
Has abstractyes

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