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Record W303623744 · doi:10.7494/cmms.2008.3.0195

Rectilinear viscoelastic flows in ducts

2008· article· en· W303623744 on OpenAlexfundno aff
Michel Beaulne, Evan Mitsoulis

Bibliographic record

VenueComputer Methods in Materials Science. · 2008
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Technical University of AthensNational and Kapodistrian University of Athens
KeywordsViscoelasticityMechanicsGeologyPhysicsMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Axial flows in generalized ducts are studied for viscoelastic materials including a linear low-density polyethylene (LLDPE) melt. Viscoelasticity is described by an integral constitutive equation of the K-BKZ type with a spectrum of relaxation times, which fits well experimental data for the shear and elongational viscosities and the normal stresses as measured in shear flow. The K-BKZ model can be reduced to the Newtonian and Maxwell models with appropriate choice of the parameters. A new technique is developed where the Finger and Cauchy-Green tensors are simplified for axial flows, since particle tracking is only required in the flow z-direction (not required in the x-y coordinate plane). Numerical solutions are presented in two-dimensional cross-sectional geometries, namely square, concave square, and eccentric annulus, for different flow rate and pressure drop changes. For the Maxwell model, the dimensionless pressure drop is independent of the Weissenberg number and a function only of the geometry. For the K-BKZ model representing the LLDPE melt, the dimensionless pressure drop is reduced with increasing flow rate, hence Weissenberg number. The present results are offered as benchmark solutions for the imposition of entry velocity and stress profiles in three-dimensional ducts, when secondary flows are not present.

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)0.002
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: Methods · Consensus signal: none
Teacher disagreement score0.796
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.034
GPT teacher head0.337
Teacher spread0.303 · 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
GenreMethods

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
Published2008
Admission routes1
Has abstractyes

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