ATP Modeling of Tall-Structure Lightning Current: Estimation of Return-Stroke Velocity Variation and Upward-Connecting Leader Length
Bibliographic record
Abstract
Electromagnetic Transients Program is used to model the lightning current distribution within the CN Tower and the attached channel. The tower is modeled by either three or five transmission line sections connected in series. On the assumption of the absence of an upward-connecting leader, the lightning channel is represented by a transmission line with a continuously expanding length. The presented model takes into account reflections within the tower and the lightning channel. Through rigorous analytical analysis, the model is used not only for estimating the return-stroke velocity variation profile, but also for determining the length of the upward-connecting leader. This detailed information is obtained using several lightning current derivative signals that were recorded at the tower. The model allows for the computation of the current at any point along the current path (the tower and the attached lightning channel), which is required for the calculation of the associated electromagnetic field. The simulation results are found to be in good agreement with measurements. They also verify that the return-stroke velocity initially increases rapidly with height, reaching a peak, and then decreases less rapidly with height. Moreover, the presented five-section model of the tower matches very well with the measured current derivative signal, especially when the estimated return-stroke velocity profile and the upward-connecting leader length are taken into consideration. The proposed model is also experimentally verified based on the comparison between the computed and measured magnetic fields. The simulated magnetic field waveform reproduces important details of the measured magnetic field signal, including the initial split peak that results from first channel-front reflection in the presence of an upward-connecting leader.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".