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A Complementary Asymmetrical Triangular Coil Set for Wireless Power Transfer Applications

2025· article· en· W4412986769 on OpenAlexaff
Joel Adubofuor, Kin Lung Jerry Kan, Sheldon S. Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsWireless power transferElectromagnetic coilComputer scienceSet (abstract data type)Electrical engineeringPower (physics)WirelessElectronic engineeringTelecommunicationsEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents the design and analysis of a new bipolar right-angled Double-D (DD) coil as the transmitter and a concentric rectangular spiral coil as the receiver. Additionally, a bipolar-bipolar configuration with DD coils for both Tx and Rx, as well as a proposed unipolar-unipolar coil consisting of a concentric rectangular Rx and two asymmetrically placed rightangled triangular Tx coils, are investigated. A comprehensive comparison of these coil configurations is conducted to evaluate misalignment tolerance and coupling efficiency. The proposed coil design, with the vector synthesis strategy, improves magnetic flux density and power transfer efficiency while generating a highly concentrated magnetic field. It also ensures effective flux circulation and improved misalignment tolerance along both the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$y$</tex> axes. The two asymmetrical coils form a unified unipolar structure, and directly provide misalignment data to the circuitry, facilitating real-time adjustments. Finite element analysis (FEA) is utilized in Ansys Maxwell to validate the design, demonstrating complementary power delivery and enhanced wireless power transfer performance.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.988
Threshold uncertainty score0.836

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 head0.013
GPT teacher head0.254
Teacher spread0.241 · 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 designNot applicable
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

Citations1
Published2025
Admission routes1
Has abstractyes

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