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Record W2027224293 · doi:10.1177/1056789508101201

A Coupled Damage-plasticity Model for Energy Absorption in Composite

2009· article· en· W2027224293 on OpenAlexfundno aff
Xinran Xiao

Bibliographic record

VenueInternational Journal of Damage Mechanics · 2009
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsnot available
FundersUniversity of British Columbia
KeywordsPlasticityMaterials scienceComposite numberFinite element methodSofteningStructural engineeringConstitutive equationMechanicsComputer scienceComposite materialPhysicsEngineering

Abstract

fetched live from OpenAlex

Predicting the energy absorption of composite structures requires a constitutive model that is capable of representing post-peak softening and irreversible strains, i.e., a coupled damage-plasticity model. The development of such models requires a general damage-plasticity framework. This article examines the merits and limitations of the continuum damage mechanics (CDM) framework and the plasticity framework. Based on the physical evidence of damage accumulation process in composites and the fundamentals of the two theories, a simple coupling method was proposed. This method employs a perfect plastic flow rule within a CDM framework. The capability of this method was examined by incorporating plasticity into the Matzenmiller-Lubliner-Taylor (MLT) model, a classic CDM model for composites. The coupled MLT-plasticity model was implemented as a user defined material law in explicit finite element code LS-DYNA ® , and subsequently numerical tests were conducted. It was demonstrated that the proposed method can extend an existing composite CDM model into a coupled damage-plasticity model seamlessly with only a few extra parameters, while retaining its computational efficiency. The capability of the coupled CDM-plasticity model in energy absorption prediction was validated in axial impact simulations of a composite tube reinforced with 1-ply carbon fiber tri-axial braid.

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: none
Teacher disagreement score0.690
Threshold uncertainty score0.689

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 integrity0.0000.000
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 head0.016
GPT teacher head0.259
Teacher spread0.243 · 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

Citations18
Published2009
Admission routes1
Has abstractyes

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