Efficient Corona Modeling for FDTD Simulations
Bibliographic record
Abstract
The existing corona model for the Finite-difference time-domain (FDTD) method represents the gas ionization process as the radial expansion of a conductive region around the wire. Despite being a simplified representation of the phenomena involved, the model has a high computational cost by requiring the discretization of the area near the wire into small cells. Furthermore, no physical law is proposed for determining this region's conductivity, which may vary from application to application. Its evaluation against experimental results, in turn, requires running time-consuming simulations. In this article, a new methodology to represent corona in FDTD simulations is presented. The method is based on representing the variation in wire capacitance under corona by an equivalent radius, which is obtained from measured charge-voltage curves. This approach is simple to implement in the FDTD method and does not require grid refinement, allowing for fast simulations. The proposed method is validated with measurements of the charge-voltage curve and the current injected into a 44 m horizontal conductor. Results show good accuracy of the method, with mean absolute errors of 2.4% and 2.9% for negative and positive polarities, respectively, considering measured charge-voltage curves, and a 100 times reduction in computational time compared to existing model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".