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Record W3035350591 · doi:10.18280/rcma.300202

Numerical and Analytical Study of Fatigue and Degradation in Multilayer Composite Plates

2020· article· fr· W3035350591 on OpenAlexvenueno aff
Amin Moslemi Petrudi, Masoud Rahmani

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2020
Typearticle
Languagefr
FieldMaterials Science
TopicMaterial Properties and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsComposite numberDegradation (telecommunications)Materials scienceStructural engineeringComposite materialEngineeringElectronic engineering

Abstract

fetched live from OpenAlex

Composite laminates are widely used in civil engineering, aerospace, shipbuilding and the military industry because of their high strength and rigidity to their high weight ratio, good fatigue resistance and high energy absorption fineness. The mechanism of degradation in composites is very complex and one or more degradation modes may occur in the composites at the same time. In this paper, multi-layer composite degradation with stress concentration due to internal hole is investigated numerically and analytically. The miso-scale model is used to investigate the problem. Fortran and Ansys software have been used to analyze the problem. The usermat code is embedded in Fortran software and linked to Ansys software. The present paper solves the stress induced multilayers in the present study, and compares the results of this method with those of other researchers. Stress and damage contours have been reported. it is shown Change of the diameter of the central open-hole not only the effect on possible damage, damaging rate, progressive damage but also effect on the strength of the composite laminates is also indicated.

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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.143
GPT teacher head0.324
Teacher spread0.182 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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Same venueRevue des composites et des matériaux avancésSame topicMaterial Properties and ApplicationsFrench-language works237,207