MUTUAL INDUCTANCE CALCULATION BETWEEN MISALIGNMENT COILS FOR WIRELESS POWER TRANSFER OF ENERGY
Bibliographic record
Abstract
Abstract—In this paper we present a detailed theoretical analysis of lateral and angular misalignment effects in RF coils. Radio-frequency (RF) coils are used extensively in the design of implantable devices for transdermal power and data transmission. A design procedure is established to maximize coil coupling for a given configuration to reduce the effects of misalignment on transmission efficiency. Formulas are derived for the mutual inductance between all possible coil configurations including the coils of cross section, thin solenoids, pancakes and filamentary circular coils whose axes are laterally and angularly displaced. Coils are in air. In this approach we used the filament method and the mutual inductance between filamentary circular coils placed in any desired position. We completely describe all mathematical procedures to define coil positions that lead to relatively easy method for calculating the mutual inductance between previously mentioned coils. The practical coils in implantable devices fall into two categories: disk coils (pancakes) and solenoid coils. From the general approach for calculating the mutual inductance between coils of rectangular cross section with lateral and angular misalignments the mutual inductance between misalignment solenoids and disks will be calculated easily and accurately. 1.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".