New Methodology for Representing Soil Ionization in FDTD Simulations of Grounding Electrodes
Bibliographic record
Abstract
Soil ionization has been represented in finite-difference time-domain (FDTD) simulations by the variation in resistivity in the cells depicting the soil. This approach represents the dynamics of soil ionization and its effect on the resistance of grounding electrodes, but it has a high computational cost as it requires discretizing the working volume into small cells. This article proposes a new method for representing the soil ionization effect on grounding electrodes in FDTD simulations. The electrode resistance is calculated based on the injected current using a dynamic soil ionization model, considering equipotential surface patterns and analytical expressions of the variation in soil resistivity. The resistance variation is then represented in FDTD by an equivalent radius. The method allows the use of coarse meshes and therefore fast simulations, while still considering the dynamics of soil ionization. Application examples with unipolar and bipolar injected current, single vertical rods of different lengths, and four parallel rods are used to validate the proposed method and compare it with the existing one. Results show good accuracy in all cases and a gain of up to 539 times in computing speed compared to the existing method, proving to be an efficient alternative for representing the phenomenon.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".