Wireless-Power-Transfer Planar Spiral Winding Design Applying Track Width Ratio
Bibliographic record
Abstract
With the increase of wireless power transfer (WPT) systems for home and industrial electronic applications, planar spiral winding design techniques are gaining attention due to their low profile, reproducibility, and manufacturability. The major design goal for windings in WPT systems is a high quality factor (Q) for a given inductance. This paper establishes a new method to improve and analyze Q by using a nonunity track-width-ratio geometrical arrangement. The results are applicable for a variety of planar spiral winding families and make full use of the ability to change the width of the traces to improve Q for wireless powered applications. In order to simplify the analysis and provide generality, a unified dimensional system framework is also proposed, covering the racetrack geometry and its derivatives-circular, rectangular, generalized octagonal, and traditional racetrack windings. The resulting dimensional system provides an accurate geometrical description of the windings to obtain high Q and a simple set of manufacturing specifications. Details and derivations of this new method to calculate inductance, dc resistance, and ac resistance estimation to obtain improved Q are presented. A comprehensive set of experimental measurements confirms the validity of the dimensional system and the improvements in Q for wireless powered 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 | 0.001 |
| 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 | 0.001 | 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".