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Record W2123073280 · doi:10.1109/tmag.2009.2023229

Design of the Magnetic Components for High-Performance Multilevel Half-Bridge Inverter Legs

2009· article· en· W2123073280 on OpenAlexafffund
C. Chapelsky, John Salmon, Andrew M. Knight

Bibliographic record

VenueIEEE Transactions on Magnetics · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInductorTopology (electrical circuits)AmplifierFerrite coreFerrite (magnet)InverterInductanceComputer scienceElectronic engineeringBandwidth (computing)Materials scienceElectrical engineeringEngineeringElectromagnetic coilTelecommunications

Abstract

fetched live from OpenAlex

Designs for the inductive components of a novel multilevel half-bridge inverter topology are presented, for an experimental high-bandwidth audio amplifier application. The design of the coupled-inductor component is examined to give a compact design by using a gapped-ferrite toroid with minimal impact to system losses over the full range of the amplifier output. The impact of using the topology with the coupled inductor on the design of the output filtering inductor is also examined to allow a significant reduction of the physical size of this component when using lossy powdered-iron materials to design a highly linear low-pass filter. In combination with the ferrite-core coupled inductor, the total magnetic weight of the experimental design is shown to be reduced at 20 g total, compared to the required filter inductor at 30 g for the same design by using a standard inverter topology with similar total losses. In addition, the analysis presented points to methods which can be used to further optimize the size and losses of these components.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.923
Threshold uncertainty score0.804

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 head0.025
GPT teacher head0.213
Teacher spread0.188 · 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 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

Citations21
Published2009
Admission routes2
Has abstractyes

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