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Record W2092639814 · doi:10.3139/217.2450

Three-dimensional Numerical Study of the Mixing Behaviour of Twin-screw Elements

2012· article· en· W2092639814 on OpenAlexaff
F. Ilinca, J.‐F. Hétu

Bibliographic record

VenueInternational Polymer Processing · 2012
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsResidence time distributionMixing (physics)MechanicsMaterials scienceResidence time (fluid dynamics)Flow (mathematics)Isothermal processViscosityNewtonian fluidPlastics extrusionFinite element methodNon-Newtonian fluidParticle (ecology)Shear rateTracking (education)Rotation (mathematics)Volumetric flow rateThermodynamicsMathematicsGeometryComposite materialPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract In this work the numerical modeling of the flow inside co-rotating twin-screw extruders is performed and solutions are analyzed to determine the mixing behavior of two screw elements: conveying and mixing elements. The flow around intermeshing screws is computed using an immersed boundary finite element method capable of dealing with complex moving solid boundaries. The flow is considered isothermal and the material behaves as a generalized non-Newtonian fluid. Because the viscosity depends on the shear rate, solutions will be shown for various rotation velocities of the screw. The 3D solutions are then analyzed in order to determine various parameters characterizing the flow mixing such as the residence time and the linear stretch. Residence time distribution inside the twin-screw extruder is first computed by using a particle tracking algorithm based on a fourth order Runge-Kutta method. A large number of particles are tracked inside the extruder and the resulting particle data is used to determine the distribution of the residence time and of the linear stretch. The spatial distribution of the residence time is also computed by solving a transport equation tracking the injection time of the polymer melt. The methodology shows important differences in the mixing behavior of the screw elements considered.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.373

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 head0.026
GPT teacher head0.293
Teacher spread0.267 · 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 designObservational
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

Citations8
Published2012
Admission routes1
Has abstractyes

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