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Record W2029430791 · doi:10.1122/1.1378026

Rheology of fiber suspensions in viscoelastic media: Experiments and model predictions

2001· article· en· W2029430791 on OpenAlexafffund
Ahmad Ramazani, A. Aı̈t-Kadi, Miroslav Grmela

Bibliographic record

VenueJournal of Rheology · 2001
Typearticle
Languageen
FieldEngineering
TopicComposite Material Mechanics
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRheologyViscoelasticityMaterials scienceFiberComposite materialShear rateShear (geology)MechanicsPhysics

Abstract

fetched live from OpenAlex

The rheological behavior of suspensions of short glass fibers in different fluids has been studied. Transient tests on presheared samples of fiber suspensions in Boger fluids showed that orientation of fibers not only depends on the strain, but also on the rate-of-strain. The experimental results show that upon increasing fiber concentration and/or fiber aspect ratio, the steady shear material functions of fiber suspensions increase at low shear rates, whereas at high shear rates, these material functions approach those of the matrix and become almost independent of fiber characteristics. A rheological model based on the modified Jeffery equation for the fiber motion and a Hookean energy model, formulated within the GENERIC framework, for the matrix has been developed to quantitatively predict experimental data for suspensions of fibers in different fluids. A quantitative comparison of experimental data with model predictions shows the ability of the model to predict the rheological behavior of fiber suspensions in viscoelastic media.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.236
Teacher spread0.223 · 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

Citations63
Published2001
Admission routes2
Has abstractyes

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