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Designing of Multilayer Planar Spiral Air-Core Inductor for Power Electronic Applications

2022· article· en· W4286304427 on OpenAlexaff
Mohammad Khakroei, Mohsen Mostafaei, Mansour Arefian, Afshin Rezaei‐Zare, Majid Najafi Zarmehri

Bibliographic record

Venue2022 30th International Conference on Electrical Engineering (ICEE) · 2022
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsYork University
Fundersnot available
KeywordsInductanceInductorPlanarFinite element methodSpiral (railway)Power (physics)Electronic engineeringElectronic circuitComputer scienceCore (optical fiber)Topology (electrical circuits)Electrical engineeringEngineeringMechanical engineeringPhysicsTelecommunicationsVoltage

Abstract

fetched live from OpenAlex

The air core inductor has diverse types, one of which is the multilayer planar spiral air-core inductor (MPSACI). In spite of many potential applications for such an inductor in power electronic circuits and systems, a limited number of research works can be found in the technical literature dealing with the precise design of the MPSACI. Hence, a method for accurate designing of an MPSACI has been presented in this paper. In the proposed method, self and mutual inductances are precisely calculated, and the total inductance is obtained based on these two inductances. First, an analytical calculation of the MPSACI is presented, and then the associated results are validated by the 3D finite element method (FEM) and experimental inductance measurements on a prototype MPSACI. Finally, other design parameters are investigated to present all aspects of a comprehensive design of the MPSACI.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.030
GPT teacher head0.259
Teacher spread0.228 · 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 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

Citations2
Published2022
Admission routes1
Has abstractyes

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