Mid-Range Wireless Power Transfer at 100 MHz Using Magnetically Coupled Loop-Gap Resonators
Bibliographic record
Abstract
We describe efficient four-coil inductive power transfer (IPT) systems that operate at 100 MHz. The magnetically coupled transmitter and receiver were made from electrically small and high- Q loop-gap resonators (LGRs). In contrast to the commonly used helical and spiral resonators, the LGR design has the distinct advantage that electric fields are strongly confined to the capacitive gap of the resonator. With negligible fringing electric fields in the surrounding space, the IPT system is immune to interference from nearby dielectric objects, even when they are in close proximity to the transmitter and/or receiver. We experimented with both cylindrical and split-toroidal LGR (TLGR) geometries. Although both systems performed well under laboratory conditions, the toroidal geometry has the additional advantage that the magnetic flux is weak everywhere except within the bore of the LGR and in the space directly between the transmitter and the receiver. Furthermore, we show that the TLGR system can be operated efficiently at a fixed frequency for a wide range of transmitter-receiver distances. The experimental results are complimented by 3-D finite-element simulations that were used to investigate the electromagnetic field profiles and surface current density distributions. Finally, we demonstrate the use of our IPT system at powers up to 32 W and discuss possible applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".