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Record W2159960860 · doi:10.4271/2014-01-9004

On Electromagnetic Shielding in Composite Aircraft: An Applied Electromagnetic Approach

2014· article· en· W2159960860 on OpenAlexaff
Mohammad Danish Khan

Bibliographic record

VenueSAE International Journal of Aerospace · 2014
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Measurements
Canadian institutionsBombardier (Canada)
Fundersnot available
KeywordsElectromagnetic shieldingComposite numberElectromagnetic compatibilityElectromagnetic fieldAerospace engineeringPhysicsEngineeringMaterials scienceElectronic engineeringElectrical engineeringComposite material

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">Effectiveness of current electromagnetic shielding techniques in composite aircraft has been called into question several times before and is a subject of interest and research in the aerospace industry. We present an analytical approach to understand the issues in detail. It was found using first order approximations that a panel with Aluminum expanded metallic foil (ExMF) along with carbon fiber plies used on composite aircraft provides fair albeit lower electromagnetic shielding from source frequencies of a few KHz until well into MHz range when compared to an aluminum panel used on metallic aircraft.</div><div class="htmlview paragraph">This was primarily attributed to relatively large skin depth value at low frequencies which was due to: a) low electrical conductivity of aluminum ExMF when compared to bulk aluminum; b) low electrical conductivity of resin impregnated carbon fiber layers and; c) very thin layer of aluminum ExMF used.</div><div class="htmlview paragraph">It was also found that as source frequency goes higher (between 800 MHz to 18 GHz range), fields penetrate through the aluminum ExMF and although they may get attenuated by the CFRP plies however, shielding effectiveness at higher frequencies still remains lower for composite aircraft when compared to metallic aircraft.</div></div>

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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