Efficiently computing the electrical parameters of cables with arbitrary cross-sections using the method-of-moments
Bibliographic record
Abstract
In a recent work, a proximity- and skin-effect aware formulation known as the surface-volume-surface electric field integral equation discretized with 2-D method-of-moments (MoM) was optimized to efficiently extract the frequency dependent series impedance matrix of cables with arbitrary shapes. However, it was only applied to sector-shaped and coaxial cables due to the constraints on computing the shunt admittance matrix using closed-form approximations. This work presents formulation, discretization, and optimization techniques, for fast computation of the shunt admittance matrix of arbitrary-shaped cables by discretizing the problem of the quasi-electrostatics using 2-D MoM. With the proposed MoM techniques and optimization strategies, it is possible to accurately compute all the electrical parameters of arbitrary-shaped cables required in electromagnetic transient programs (EMTP) using today's typical computer power and with reasonable computational times. This provides an efficient modeling tool for any desired cable design. Frequency domain solutions of the proposed technique are compared against the finite-element method as well as the classical approximate formulas available for pertinent cable models. The resulting time domain transient simulations in EMTP are also investigated.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".