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Record W2560132904 · doi:10.1063/1.4968826

Non-isothermal extrudate swell

2016· article· en· W2560132904 on OpenAlexafffund
Vinod Kumar Konaganti, Maziar Derakhshandeh, Marzieh Ebrahimi, Evan Mitsoulis, Savvas G. Hatzikiriakos

Bibliographic record

VenuePhysics of Fluids · 2016
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsDie swellIsothermal processHigh-density polyethyleneSwellCapillary actionCrystallizationMechanicsThermodynamicsExtrusionMaterials scienceComposite materialDie (integrated circuit)PhysicsPolyethylene

Abstract

fetched live from OpenAlex

The non-isothermal extrudate swell of a high molecular weight high-density polyethylene (HDPE) in long capillary and slit dies is studied numerically (ANSYS POLYFLOW®) using an integral K-BKZ constitutive model including crystallization kinetics, determined experimentally. The Nakamura model is used for crystallization of the HDPE, where the crystallization rate parameter is evaluated by using the well-known Ziabicki equation. This non-isothermal extrudate swell phenomenon is simulated using the pseudo-time integral K-BKZ model with the Wagner damping function along with the differential form of the Nakamura model to account for the crystallization of the extrudate. The swell measurements were carried out under non-isothermal conditions by extruding the polymer melt at 200 °C through long capillary and slit dies to ambient air at 25 °C, 110 °C, and 200 °C. The numerical results are found to be in excellent agreement with experimental observations.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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 designBench or experimental
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

Citations30
Published2016
Admission routes2
Has abstractyes

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