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Record W2221365135

A domain decomposition method for waveguide horn problems using the finite element method

2007· article· en· W2221365135 on OpenAlexaff
P. Paul, J.P. Webb

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsMcGill University
Fundersnot available
KeywordsDomain decomposition methodsFinite element methodBoundary (topology)Antenna (radio)DiscretizationDisjoint setsBoundary value problemPerfect conductorMathematicsDomain (mathematical analysis)Boundary element methodMathematical analysisBoundary knot methodAlgorithmComputer scienceOpticsPhysicsTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

A Domain Decomposition Method (DDM) for solving electromagnetic radiation problems using the finite element method is proposed. To minimize the size of the computational domain, disjoint scatterers and/or radiators which are relatively far from each others are bounded separately. An iterative Absorbing Boundary Condition (ABC) is applied upon an outer boundary of arbitrary shape which may be conformal to the surface of each scatterer and radiator. The boundary condition of each subdomain is updated iteratively using the contribution of the scattered field from the subdomain itself and also from the other subdomains, which makes it fully coupled. An accurate waveguide port boundary condition is employed to model horn antenna feeds. Unlike most DDMs, the proposed method uses an arbitrary shaped truncation boundary placed very close to the scatterers and radiators of each disjoint subdomain to minimize the computational domain; and more importantly, the method produces a solution that converges to the true solution (if we ignore discretization error) as the iteration proceeds. A numerical example is provided to demonstrate the correctness of the feed modelling by computing antenna input impedance. Results are provided to validate the multi-domain approach and compare it with the single domain results.

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)0.003
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: Methods · Consensus signal: Methods
Teacher disagreement score0.503
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.029
GPT teacher head0.369
Teacher spread0.340 · 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
GenreMethods

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
Published2007
Admission routes1
Has abstractyes

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