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Record W2807237239 · doi:10.15866/ireme.v12i2.13995

Particle-Based Modelling of In-Plane Shear in Textiles

2018· article· en· W2807237239 on OpenAlexaff
Reza Samadi, F. Robitaille

Bibliographic record

VenueInternational Review of Mechanical Engineering (IREME) · 2018
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsShear (geology)Materials scienceMechanicsDisplacement (psychology)Displacement fieldStructural engineeringComposite materialEngineeringPhysicsFinite element method

Abstract

fetched live from OpenAlex

This paper investigates the prediction of the constitutive behavior of textiles subjected to in-plane shear loading, using a particle-based modelling method. Plain weave textiles where the fibers are represented as a series of conjoined particles were modelled using discrete mechanics, as an alternative to traditional continuum mechanics. The configurations of individual fibers were obtained from first principles, using a modified Metropolis algorithm to minimize the strain energy terms. A series of simulations replicating articulated frame in-plane shear tests were performed in order to characterize the in-plane shear behavior of woven textiles. The paper discusses the effects of varying boundary conditions at fiber ends upon the application of a displacement field, and of varying iteration parameters for particle positions towards lower levels of strain energy. Load-displacement curves were derived from the simulations; displacement fields, locking angles and shear stresses were also quantified. The results of simulations and experiments performed on woven textiles subjected to in-plane shear showed good agreement, and improvements in accuracy over other modelling techniques.

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 categoriesInsufficient payload (model declined to judge)
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 score0.505
Threshold uncertainty score1.000

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 science0.0000.000
Research integrity0.0000.000
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 head0.043
GPT teacher head0.297
Teacher spread0.254 · 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.

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
Published2018
Admission routes1
Has abstractyes

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