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Record W3203728181 · doi:10.1049/ell2.12323

Modes analyses of cylindrical waveguides using the MFCM

2021· article· en· W3203728181 on OpenAlexaff
Kai Wang, Fengqi Yu, Teng Liang, Qingfeng Zhang, Qinyu Zhang, Jean‐Jacques Laurin, Ke Wu

Bibliographic record

VenueElectronics Letters · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsPolytechnique Montréal
FundersChina Postdoctoral Science Foundation
KeywordsMaterials scienceOpticsComputer scienceElectronic engineeringPhysicsAcousticsEngineering

Abstract

fetched live from OpenAlex

Abstract An efficient modes analyses technique for isotropic or anisotropic material filled 2D metallic waveguides with an arbitrary contour using the multifilament current method (MFCM) is presented. The ideal PEC boundary of a 2D waveguide is replaced by a shell with a high conductivity and electrical small thickness. The thin lossy shell not only can well approximate the boundary condition of PEC waveguide wall therefore without altering the initial waveguide modes, but also can let the external excitation penetrate through to excite the inside modes, resulting in a high internal field intensity at the frequency of each mode. In this case, the modes are revealed by the peaks of field intensity responses, and the spurious modes which existed in traditional source‐free modes determination techniques can be completely avoided. Based on this idea, a generalized impedance boundary condition (GIBC) is formulated to represent the lossy waveguide wall and further utilized in the MFCM for simulating the internal field intensity over frequency. Three different configurations of a 2D waveguide are considered. The computed modes are compared with that obtained from commercial software, and an excellent agreement is achieved, yet an competitive advantage on simulation performances is observed by using the proposed technique.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.501

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 head0.027
GPT teacher head0.266
Teacher spread0.239 · 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 designBench or experimental
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

Citations7
Published2021
Admission routes1
Has abstractyes

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