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Mixing analysis of reactive polymer flow in conveying elements of a co-rotating twin screw extruder

2000· article· en· W1997217610 on OpenAlexaff
David Strutt, Costas Tzoganakis, Thomas A. Duever

Bibliographic record

VenueAdvances in Polymer Technology · 2000
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceMixing (physics)DragPolypropyleneFlow (mathematics)Plastics extrusionMechanicsIsothermal processPeroxideVolumetric flow rateIsothermal flowPolymerComposite materialThermodynamicsOpen-channel flowChemistryPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

In this study, the finite element method was used to investigate the effects of screw speed, entering peroxide distribution, and pressure-to-drag flow ratio on the mixing characteristics of steady non-isothermal reactive flows in a forward conveying element of a self-wiping twin screw extruder. The reaction considered was the peroxide-initiated degradation of a commodity polypropylene resin. The predicted average degree-of-freedom profiles from the simulations largely conformed to expectations. The average flow efficiencies for all runs were found to remain at values close to that for two-dimensional flow, with fluctuations being observed in the channel intermeshing regions. No significant effect of either screw speed or peroxide distribution was found on the flow efficiencies; however, the pressure-to-drag flow ratio was found to have a significant influence. © 2000 John Wiley & Sons, Inc. Adv Polym Techn 19: 22–33, 2000

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)0.000
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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.006
GPT teacher head0.271
Teacher spread0.265 · 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

Citations18
Published2000
Admission routes1
Has abstractyes

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