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Record W4387587512 · doi:10.1109/temc.2023.3315135

A Single MTLN Model Including Electrical Wiring and Current Return for Low-Frequency Common-Mode EM-Coupling Simulation

2023· article· en· W4387587512 on OpenAlexfundno aff
Jean‐Philippe Parmantier, Isabelle Junqua, Wilfrid Quenum, S. Bertuol

Bibliographic record

VenueIEEE Transactions on Electromagnetic Compatibility · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsnot available
FundersOffice National d'études et de Recherches AérospatialesNatural Sciences and Engineering Research Council of Canada
KeywordsTransmission lineElectronic engineeringEngineeringCoupling (piping)Lossy compressionElectric power transmissionElectrical engineeringElectromagnetic compatibilityTopology (electrical circuits)Computer scienceMechanical engineering

Abstract

fetched live from OpenAlex

This article addresses the problem of multiconductor-transmission-line-networks (MTLN) modeling of complex electrical wiring systems in the presence of imperfect grounds (lossy, finite dimensions, sparse…) in order to account for common-mode effects. First, the theoretical principle of a ground junction representing such grounds and that combines a low- and a high-frequency asymptotic approximations is derived on a simple transmission line model. Then, this article shows how this ground junction concept can itself be modeled as an explicit MTLN model in the case of manmade complex ground return networks. A validation is presented on a scale-one full carbon composite business jet cabin fuselage equipped with such an opportunistically designed ground return network and a prototype but realistic wiring. The different steps and drivers of the modeling process are highlighted. Comparisons between measurement and simulations for local voltage injections at equipment ports show the need to account for the current return model and the relevance of the MTLN model for low frequency EM coupling analysis.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.309
Teacher spread0.273 · 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.

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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

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