MétaCan
Menu
Back to cohort
Record W2092809320 · doi:10.1109/ecce.2010.5617812

Applying Response Surface Methodology to planar transformer winding design

2010· article· en· W2092809320 on OpenAlexafffund
Samuel R. Cove, Martin Ordonez, John E. Quaicoe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Noise Suppression
Canadian institutionsMemorial University of Newfoundland
FundersEnergy Council of Canada
KeywordsTransformerInductancePlanarConvertersLeakage inductanceParametric statisticsElectronic engineeringPower electronicsElectromagnetic coilNonlinear systemCapacitanceTopology (electrical circuits)Computer scienceEngineeringElectrical engineeringVoltagePhysicsMathematics

Abstract

fetched live from OpenAlex

Planar transformers provide a light-weight and low profile solution for power electronic converters with highly reproducible parameters. Their parasitic inductances, capacitances, and resistances are difficult to model due to the complex winding arrangement along with their nonlinear and multivariate nature. This paper provides a methodology for determining parametric models for the leakage inductance, inter and intra-winding capacitances, and resistance of planar transformers using a variety of winding arrangements. A Central Composite Design based on the Design of Experiment (DoE) methodology is employed to provide the parametric models using a small number of experimental runs. Results from physical experimentation on a planar ER18/3.2/10 core set are provided and show excellent correlation between modeled results and confirmation testing. The methodology can be employed to characterize and design planar transformers for specific applications (for example soft switching or resonant converters), and to predict their performance as part of different power electronics topologies.

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)0.001
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 score0.033
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.046
GPT teacher head0.279
Teacher spread0.233 · 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

Citations5
Published2010
Admission routes2
Has abstractyes

Explore more

Same topicElectromagnetic Compatibility and Noise SuppressionFrench-language works237,207