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Factors Influencing the Optimization of Magnetically Coupled Coil Structures-Analysis and Discussions

2023· article· en· W4388016280 on OpenAlexaff
Lavinia Bobaru, Dragoș Niculae, Georgiana Rezmeriță, Marilena Stănculescu, Mihai Iordache, Oana Drosu, Sorin Deleanu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsNorthern Alberta Institute of Technology
Fundersnot available
KeywordsElectromagnetic coilWireless power transferSoftwareComputer scienceElectromagnetic fieldReliability (semiconductor)WirelessElectronic engineeringProcess (computing)ExtractorElectromagnetic simulationPower transmissionField (mathematics)ElectromagneticsPower (physics)Mechanical engineeringElectrical engineeringEngineeringPhysicsTelecommunications

Abstract

fetched live from OpenAlex

The paper explores the factors influencing the optimization of magnetically coupled coil structures through in-depth analysis and discussions. Optimizing the wireless power transfer system (WPTS) is crucial as it enhances the efficiency, reliability, and overall performance of the system, leading to more effective and sustainable wireless energy transmission solutions. The optimization process involves considering multiple aspects, such as coil structure, configuration, number of turns, coil shape, working frequencies, electromagnetic properties of the medium, and coil distance. To identify the relevant parameters (R, L, C, and G) for a system consisting of two magnetically coupled coils used WPTS, advanced electromagnetic field numerical simulation software is employed. Among the widely utilized software tools for estimating the parameters of electromagnetic systems is the ANSYS Q3D EXTRACTOR, available in both 2D and 3D versions.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.207

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.216
Teacher spread0.204 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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