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Record W4405280101 · doi:10.1029/2024rs008068

Finite Element Method Modeling of the Wire Thickness of a Monopole on a Circular Ground Plane

2024· article· en· W4405280101 on OpenAlexaff
Christopher G. Hynes, Rodney G. Vaughan

Bibliographic record

VenueRadio Science · 2024
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGround planeRADIUSMonopole antennaFinite element methodMagnetic monopoleElectrical impedanceImpedance matchingPhysicsAntenna (radio)Plane (geometry)Random wire antennaWavelengthGeometryInput impedanceElectrical lengthOpticsAcousticsDipole antennaComputational physicsMathematicsElectrical engineeringEngineeringComputer scienceSlot antennaAntenna efficiency

Abstract

fetched live from OpenAlex

Abstract Simulations and measurements of the input impedance and matching of a cylindrical monopole at the center of a circular ground plane are presented. The design parameters are the monopole length ( to ), the monopole radius ( to ), and the ground plane radius ( to ), where is the wavelength. Using new numerical results from the Finite Element Method (FEM), previous theoretical impedance results for an infinitesimally thin element are shown to be inaccurate for monopoles of practical thicknesses since there can be a strong dependence on the wire thickness—even for electrically very thin wires. The FEM offers convenient modeling for the wire thickness and the results match well with physical experiments. To obtain good antenna impedance matching to a 50 impedance, that is, dB, for any ground plane radius greater than (an arbitrary lower bound) and any practical wire monopole radius, the simulations show that a monopole length of can be used.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.020
GPT teacher head0.255
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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