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Micro Mechanical Model of Filament Wound Composite Pipe With Damage Analysis

2006· article· en· W2003857649 on OpenAlexaff
Chuwei Zhou, Zihui Xia, Qiaoling Yong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDelamination (geology)StiffnessFilament windingMaterials scienceComposite numberFinite element methodStructural engineeringComposite materialProtein filamentEpoxyEngineering

Abstract

fetched live from OpenAlex

Filament winding (FW) is one of the most common techniques for manufacturing composite pipes. The material properties and failure mechanism of composite pipes depend largely on winding pattern. In this study a micro mechanical approach for filament wound composites (FWCs) is pursued. A diamond-shaped repeated unit cell (RUC) is first constructed which characterizes the micro architecture of FWC pipe, such as winding angel, shift between successive circuits and the area of local undulation region. The micro mechanical model is embedded into commercial FEM code of ABAQUS as user-defined subroutine thus the link between the analyses in macro engineering structural scale and in micro material structural scale is established. By averaging micro stiffness constants over the cell macro ones needed for engineering structural analysis can be obtained. On the other hand, the macro structural analysis provides average stresses/strains of the cell locating at any concerned region of the macro structure for local stress and damage analysis. Effects of tow undulation caused by tow crossover on micro stresses are taken into accounted. The model is applied to glass/epoxy wound pipes with various winding angles and winding shifts. Mechanical properties are predicted and damage evolutions are simulated. The effects of delamination damage, usually introduced by lateral low velocity impact, on stiffness and ultimate strength of FWC pipe are also investigated.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.613

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 science0.0000.000
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 head0.010
GPT teacher head0.209
Teacher spread0.199 · 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

Citations11
Published2006
Admission routes1
Has abstractyes

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