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Record W2038593214 · doi:10.1002/cjce.22148

Slip heating in die drool

2014· article· en· W2038593214 on OpenAlexaffvenue
P. H. Gilbert, A. Jeffrey Giacomin

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2014
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsMechanicsSlip (aerodynamics)Materials scienceSlippingHeat generationIsothermal processThermodynamicsComposite materialMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

When molten plastic is extruded from a die, it can collect on the open die face. Called die drool , this phenomenon costs plastics manufacturers by requiring shutdown for die cleaning. This has been attributed to cohesive failure within the fluid at an internal surface, where the fluid slips on itself; the corresponding isothermal analysis led to an analytical solution for the drool rate (Schmalzer and Giacomin, J. Polym. Eng . 2013 , 33 , 1). In this paper, we account for the frictional heating at the cohesive slip interface, which we call slip heating . We focus on slit flow, which is used in film casting, sheet extrusion, curtain coating, and in many other chemical engineering unit operations. In slit flow, the magnitude of the heat flux from the slipping interface is the product of the shear stress and the slip speed. We present the solution for the temperature rise in pressure‐driven slit flow subject to constant heat generation at the cohesive slip interface. We solve the energy equation in Cartesian coordinates for the temperature rise, for both the transient and steady temperature profiles, in both the drool layer and the bulk polymer. We then evaluate the effect of this temperature rise on the rate of die drool. For this simplest relevant non‐isothermal problem, we neglect viscous dissipation and convective heat transfer in the melt and we model viscosity as an Arrhenius function of temperature. We conclude with three worked examples showing the relevance of slip heating in determining die drool flow rates. We find that slip heating diminishes die drool. We arrive at two sufficient dimensionless conditions for the accurate use of our results: Br ≪1 or Gi ≪1.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.478
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

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 integrity0.0000.001
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 head0.005
GPT teacher head0.181
Teacher spread0.175 · 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 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

Citations5
Published2014
Admission routes2
Has abstractyes

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