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Record W2474183556 · doi:10.1109/wpt.2016.7498860

Minimizing printed spiral coil losses for inductive link wireless power transfer

2016· article· en· W2474183556 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsElectromagnetic coilEddy currentInductanceWireless power transferSpiral (railway)Maximum power transfer theoremPower (physics)Materials scienceAcousticsElectrical engineeringMechanicsMechanical engineeringElectronic engineeringEngineeringPhysicsVoltage

Abstract

fetched live from OpenAlex

This work aims for the design of printed spiral coils (PSC) with high quality factors. This consists in minimizing coil losses represented by proximity and eddy currents losses. For this purpose, specific geometric parameters characterizing spiral coils are shown to have a direct impact on increasing such losses. As a result, to minimize proximity effect, high ratios between the interspace separating two adjacent traces and the trace width are recommended to be used. In addition, to reduce eddy current losses, an empirical equation is developed to determine the optimal inner diameter sizes of the coils. The obtained numerical results confirmed that, using the proposed design constraints, the quality factor of the coils improves by 40 % in average with only 12 % decrease of the coil inductance value. Analytical formulation of the coil quality factor is obtained. Comparing analytical to simulation results, the obtained errors are reduced from 43 % to only 5 % when the recommended design constraints are applied.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.873

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 head0.016
GPT teacher head0.219
Teacher spread0.203 · 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

Quick stats

Citations15
Published2016
Admission routes1
Has abstractyes

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