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Computing Tower-Footing Grounding Impedance and GPR curves of Grounding Electrodes Buried in Multilayer Soils

2019· article· en· W3000010622 on OpenAlexaff
Anderson Ricardo Justo de Araújo, Jaimis S. L. Colqui, Sérgio Kurokawa, Claudiner M. Sexias, Behzad Kordi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCounterpoiseGroundElectrical impedanceTowerGround-penetrating radarContext (archaeology)EngineeringTransmission towerAcousticsElectrical engineeringElectronic engineeringStructural engineeringGeologyPhysicsTelecommunicationsRadar

Abstract

fetched live from OpenAlex

Grounding systems are essential to dissipate fault currents into soil to guarantee safe conditions to personnel and equipment. In this context, several approaches have been employed to calculate tower-footing grounding impedance either in the frequency or time domain. In the Transmission Line Model (TLM), vertical or horizontal electrodes are represented by distributed parameters along its length. and its impedance are calculate by analytical equations. However, for counterpoise electrodes there are no analytical formulae to estimate its tower-footing grounding impedance. To tackle this issue, numerical methods have been employed to assess the tower-footing grounding impedance in any various arrangements. In this article, a comparison between the grounding impedance obtained by the TLM and numerical Method of Moments (MoM) is carried out in order to verify the accuracy of the numerical method. Then, grounding impedances of counterpoise electrodes and the Grounding Potential Rising (GPR) are calculated for different configurations of a multi-layer soil. It can be noted that grounding impedance of counterpoise electrodes as well as the peaks of the GPR curves are strongly modified by the presence of multi-layer soil and electrode length.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.654

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 science0.0000.000
Research integrity0.0000.000
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 head0.004
GPT teacher head0.228
Teacher spread0.223 · 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 designObservational
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
Published2019
Admission routes1
Has abstractyes

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