A Review of Optimal Conditions for Achieving Maximum Power Output and Maximum Efficiency for a Series–Series Resonant Inductive Link
Bibliographic record
Abstract
This paper presents the conditions for achieving maximum power output and maximum efficiency in a series- series (SS) resonant inductive link. It has been shown that output power depends on the nature of supply: current source or voltage source, resulting in different values for the same resonant link setup. For both types of sources, conditions for maximum power transfer with respect to different parameters have been derived. Efficiency, on the other hand, is independent of the nature of supply; it depends only on circuit parameters. Conditions for maximum efficiency with respect to circuit parameters have also been derived. It has been shown that an SS resonant inductive link is best suited for current-sourced input, since a voltagesourced link behaves poorly when deviated from ideal tuning conditions. Moreover, for a voltage-sourced link, there exists an optimum value of mutual inductance at which maximum power transfer occurs. However, for a current-sourced link, output power increases with increase in coupling coefficient. The modeling and analysis has been supported with detailed simulation and experimental results.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".