Calculation of Resistances and Inductances in Multi-Conductor Systems Including Solid and Litz Wires Using the 2-D Boundary Element Method
Bibliographic record
Abstract
Multi-conductor systems can be modeled as multi-port networks made of lumped parameters. In the general case, the computation of such parameters requires the knowledge of the electric and magnetic fields. During the last decades, the finite-element method (FEM) has been widely used to compute the field quantities. Due to the large number of degrees of freedom involved, the application of FEM is prohibitive in cases including a large number of conductors and when a frequency scan is required, which requires a mesh adapted to the frequencies of interest. In this article, the boundary element method (BEM) is instead explored. The BEM formulations of the 2-D magnetic-harmonic problems in multi-conductor systems made of solid and Litz wires are presented in this article. The proximity effects in the Litz wires are considered by means of a complex permeability. The voltages and currents are included in the mathematical formulation of the problem, from which the frequency-dependent resistances and inductances per unit length (p.u.l.) can be directly found. The approach proposed is fast, easy to use, and requires no post-processing steps.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".