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Record W4252874055 · doi:10.32920/ryerson.14653575.v1

Modeling of the Lightning Return Stroke Current at a Tall Structure Using the Derivative of the Heidler Function

2021· preprint· en· W4252874055 on OpenAlexaff
Kristofer Bitner

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsToronto Metropolitan UniversityASTER
Fundersnot available
KeywordsWaveformElectric power transmissionElectrical impedanceTowerLossless compressionDerivative (finance)Lightning (connector)Reflection (computer programming)Discontinuity (linguistics)Reflection coefficientAcousticsMathematical analysisSeries (stratigraphy)PhysicsComputational physicsElectrical engineeringMathematicsComputer scienceOpticsEngineeringGeologyVoltageStructural engineeringAlgorithm

Abstract

fetched live from OpenAlex

Traditionally tall structures have been modeled as simple lossless transmission lines. This model is inadequate for the CN Tower, which may be modeled as a series of transmission lines with different characteristic impedances resulting in a reflection coefficient at each discontinuity. Analysis shows that these vary significantly and are related to the ratio of the current derivative peak to the current derivative 10%-90% risetime, suggesting that they are frequency dependent. The magnitude of the reflection from the return stroke front, if it does exist, is much smaller that was previously proposed. An alternative approach to modeling, based on modeling the current derivative, is proposed and it is found to provide a better match with the measured waveforms. The CN Tower is modeled as a series of uniform lossless transmission lines and the channel is represented by the MTLL model. The features of the measured magnetic field waveform are well reproduced.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.235
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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