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Record W2902744933 · doi:10.3139/217.3574

Computer Flow Simulation of Moffatt Eddies in Single Screw Extrusion

2018· article· en· W2902744933 on OpenAlexaff
Nickolas D. Polychronopoulos, J. Vlachopoulos

Bibliographic record

VenueInternational Polymer Processing · 2018
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEddyMechanicsFlow (mathematics)Metering modeExtrusionPlastics extrusionRoot (linguistics)TurbulenceGeologyMaterials scienceMechanical engineeringPhysicsEngineeringComposite material

Abstract

fetched live from OpenAlex

Abstract A 3D flow analysis package is used to simulate the flow phenomena in a short section of the metering zone of a single screw extruder. The assumption of stationary screw and rotating barrel is used. For sharp flight root corners, in addition to the primary flow field a secondary flow was determined in front of the root of the pushing flight and behind the root of the trailing flight, akin to what is known in the fluid mechanics literature as Moffatt eddies. Fluid particles caught in Moffatt eddy regions require travel times which are at least two orders of magnitude larger than the average residence time. This implies high probability of degradation and explains the deposits of thin carbonaceous films and gels, which have been discussed in several publications in the technical literature. The computer simulations show the existence of Moffatt eddies in the presence or absence of pressure gradients. When the screw roots have relatively wide angles no Moffatt eddies are predicted, in agreement with recommendations for good screw design practices.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.269
Teacher spread0.254 · 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

Citations19
Published2018
Admission routes1
Has abstractyes

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