Wheeler Cap Method Efficiency Estimation Errors at Antenna Characteristic Modes
Bibliographic record
Abstract
The Wheeler method is often advocated as a simple and quick means to calculate antenna efficiency. It comprises impedance measurements with and without a conducting cavity around the antenna. Although Wheeler proposed it for electrically small antennas, its application has been expanded to include wideband and multiport antennas. Numerous errors have been identified in the method, including from shield cavity (cap) resonances, shield losses, changes in the antenna current, and non-cavity mode efficiency dips. These dips in the frequency domain have not received the same attention as those due to cavity resonances, and they represent a serious defect with the method near antenna resonance frequencies - where an antenna typically operates. This paper corrects the traditional Wheeler circuit-parameter formulas for efficiency estimates by accounting for the difference in the antenna currents between the two impedance measurements. Simulation is used to decompose the losses, including the non-cavity mode efficiency dips. For a wire antenna on a large ground plane, the change in the wire loss is shown to be the primary source of error at non-cavity mode efficiency dips - occurring at the antenna's characteristic modes. At lower frequencies, where the antenna is electrically small, the error primarily stems from the cap and from changes in losses in the ground plane. For a patch antenna, the error is shown to depend on the dielectric loss. These results demonstrate that the Wheeler method is not suitable for general antennas over their full frequency range.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".