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Record W2109058186 · doi:10.1177/1056789514532667

Nonlinear numerical predictions of three-dimensional orthogonal woven composite under low-cycle tension using multiscale repeating unit cells

2014· article· en· W2109058186 on OpenAlexaff
Xiwen Jia, Zihui Xia, Bohong Gu

Bibliographic record

VenueInternational Journal of Damage Mechanics · 2014
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsUniversity of Alberta
FundersFundamental Research Funds for the Central UniversitiesCentral University Basic Research Fund of ChinaFoundation for the Author of National Excellent Doctoral Dissertation of the People's Republic of China
KeywordsMaterials scienceComposite materialViscoelasticityFinite element methodConstitutive equationFiberStiffnessNonlinear systemTension (geology)Matrix (chemical analysis)Ultimate tensile strengthStiffness matrixComposite numberStress (linguistics)Structural engineeringEngineering

Abstract

fetched live from OpenAlex

This paper reports the low-cyclic tensile responses of three-dimensional orthogonal woven composites based on the micro/meso-scale repetitive unit cells with elastic/viscoelastic models. The repetitive unit cell models for the resin impregnated fiber tows and the three-dimensional orthogonal woven composites at the fabric microstructure level were established. In the micro/meso-repetitive unit cells model, a nonlinear viscoelastic relationship with switch rule is introduced to characterize the mechanical behavior of the resin for the loading/unloading conditions. The fiber/fiber tows are characterized with linear elastic models. And damage initiation and postdamage constitutive models are also included for matrix/fiber and matrix impregnated fiber tows in the finite element analysis. The fatigue model with the mechanical parameters transferred from micro-scale to meso-scale model is numerically simulated with user-defined material subroutine and incorporated into commercial finite element software ABAQUS/Standard. The calculated results manifest the multiscale fatigue behaviors of three-dimensional orthogonal woven composites, such as the global stiffness degradation, decrease of peak stress, and even local damage behavior. The fatigue behaviors are also verified with the data in experimental and indicated that the model is an efficient strategy to predict the low-cyclic fatigue behaviors of polymer matrix composites.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.866

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 science0.0010.000
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 head0.017
GPT teacher head0.257
Teacher spread0.240 · 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
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

Citations13
Published2014
Admission routes1
Has abstractyes

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