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Record W1589689609 · doi:10.1109/intmag.2002.1001052

2D vs. 3D models to predict equivalent circuit parameters for high frequency transformers

2003· article· en· W1589689609 on OpenAlexaff
J.D. Lavers, E.D. Lavers

Bibliographic record

VenueIEEE International Digest of Technical Papers on Magnetics Conference · 2003
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTransformerFerrite coreEquivalent circuitInductorSpiceComputer scienceElectronic engineeringFinite element methodElectrical engineeringEngineeringVoltageElectromagnetic coil

Abstract

fetched live from OpenAlex

Summary form only given. Ferrite core inductors and transformers are key components in high frequency switch mode power supplies. Finite element software tools have recently become available to extract the equivalent circuit parameters for such devices and to generate Spice-compatible models for circuit analysis and design. However, the existing extraction tools are based on 2D (pot core) approximations of actual 3D core configurations. While this approximation may be reasonable for certain core geometries, it leads to errors in others. The purpose of this paper is to quantitatively assess the magnitude of these errors in representative high frequency designs. It is shown that because of 3D effects, the extracted parameters can be significantly in error for certain geometries. This, in turn, has a major impact on the predicted electrical performance of the full power supply switching circuit.

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: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.004

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 head0.054
GPT teacher head0.260
Teacher spread0.206 · 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
Published2003
Admission routes1
Has abstractyes

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