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Record W2549169019 · doi:10.1515/polyeng-2015-0382

Temperature rise in a verging annular die

2015· article· en· W2549169019 on OpenAlexaff
Georges R. Younes, A. Jeffrey Giacomin, P. H. Gilbert

Bibliographic record

VenueJournal of Polymer Engineering · 2015
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsDie (integrated circuit)Conical surfaceAnnulus (botany)MechanicsCoaxialFlow (mathematics)Plastics extrusionMaterials scienceMechanical engineeringComposite materialPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract Plastic pipes, tubes or catheters are extruded by pressure-driven flows through annular dies. Whereas die lands are straight, the section connecting the die land to the extruder either converges or diverges, converging when the product is smaller than the extruder barrel, and diverging when larger. In this paper, we carefully consider the converging or diverging connecting flows, in spherical coordinates, for the most common configuration: the Newtonian pressure-driven flow through the annulus between two coapical coaxial cones. We derive the exact analytical solution for the velocity profile, and then use this to arrive at the exact analytical solution for the temperature rise caused by viscous heating. We care about this rise because it often governs maximum throughput, since pipe makers must protect the melt from thermal degradation. We find that both the velocity profile, and the temperature profile, peak over the same conical surface and this surface is nearer the inner die wall. We also provide analytical expressions for the nonlinear pressure profile and the die cooling requirement. We find that this cooling requirement is always higher on the inner cone.

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 score0.004

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 communication0.0010.000
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 head0.007
GPT teacher head0.212
Teacher spread0.205 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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