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Record W2151133188 · doi:10.5194/angeo-31-1689-2013

Evaluating the applicability of the finite element method for modelling of geoelectric fields

2013· article· en· W2151133188 on OpenAlexaff
Bo Dong, D. W. Danskin, Risto Pirjola, D. H. Boteler, Zhishui Wang

Bibliographic record

VenueAnnales Geophysicae · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsNatural Resources Canada
FundersNational Natural Science Foundation of China
KeywordsFinite element methodElectric fieldGeomagnetically induced currentEarth's magnetic fieldElectric power transmissionMagnetic fieldGeophysicsElectric power systemMechanicsGeomagnetic stormPhysicsComputational physicsPower (physics)Electrical engineeringEngineeringStructural engineering

Abstract

fetched live from OpenAlex

Abstract. Geomagnetically induced currents in power systems are due to space weather events which create geomagnetic disturbances accompanied by electric fields at the surface of the Earth. The purpose of this paper is to evaluate the use of the finite element method (FEM) to calculate the magnetic and electric fields to which long transmission lines of power systems on the Earth are exposed. The well-known technique of FEM is used for the first time to simulate magnetic and electric fields applicable to power systems. Several test cases are modelled and compared with known solutions. It is shown that FEM is an effective modelling technique that can be applied to determine the electric fields which affect power systems. FEM enables an increased capability beyond the traditional methods for modelling electric and magnetic fields with layered earth conductivity structures, as spatially more complex structures can be considered using FEM. As an example results are presented for induction, due to a line current source, in adjacent regions with different layered conductivity structures. The results show the electric field away from the interface is the same as calculated for a single region; however near the interface the electric field is influenced by both regions.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.810
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.077
GPT teacher head0.325
Teacher spread0.248 · 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

Citations18
Published2013
Admission routes1
Has abstractyes

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