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Record W2048874811 · doi:10.1002/pen.10916

Relationship between local residence time and distributive mixing in sections of a twin‐screw extruder

2001· article· en· W2048874811 on OpenAlexafffund
Gifford Shearer, Costas Tzoganakis

Bibliographic record

VenuePolymer Engineering and Science · 2001
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMixing (physics)Materials scienceDistributive propertyResidence time distributionPlastics extrusionResidence time (fluid dynamics)TRACERFlow (mathematics)MechanicsComposite materialMathematicsGeologyGeotechnical engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Local residence time and distributive mixing were measured in conveying sections and kneading blocks of a twin screw‐extruder. The residence time measurements were completed using carbon black as the tracer and an infrared temperature probe to detect the temperature decrease caused by the changing surface emissivity. The validity of this experimental technique was extensively evaluated. A mixing limited interfacial reaction between polymer tracers was used to directly measure the distributive mixing in the twin‐screw extruder. Possible relationships between mixing and residence time in the sections of the twin‐screw extruder were investigated by combining these two measurements. Distributive mixing in conveying sections was related to the local average residence time and the fill. In contrast, distributive mixing in kneading blocks was related to the local average number of screw revolutions experienced by the polymer. Forward stagger kneading discs achieved the greatest amount of distributive mixing, which was attributed to a combination of local stagnant flow regions and more frequent interfacial reorientation.

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 score0.468
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.235
Teacher spread0.222 · 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

Citations17
Published2001
Admission routes2
Has abstractyes

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