Accurate modeling of thin wires in the FDTD method
Bibliographic record
Abstract
In many electromagnetic problems analyzed numerically with the finite-difference time-domain (FDTD) method, thin wires need to be modeled. A wire is considered thin when its diameter is less than the selected mesh size. It certainly is possible to select a sufficiently small mesh, so that the wire diameter occupies one or more computational cells, but this approach open results in a very fine discretization and excessive computational resources. We have performed a detailed numerical evaluation of the input impedance and the resonant frequency of a dipole antenna, and compared the results with with those obtained with the method of moments (MoM) based code, the Numerical Electromagnetic Code, NEC. But the results are obtained by use of an incorrect (not physics based) normalization factor. These limitations of available subcell wire models provided motivation for our work. In this article we describe a new algorithm and its implementation. Dipole parameters (the input impedance, resonant frequency and resistance at resonance) computed with the new algorithm are compared with those obtained with the standard algorithm, and modified one, as well as with the reference solution.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".