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Record W4396939615 · doi:10.1063/5.0211406

Wire coating and melt elasticity

2024· article· en· W4396939615 on OpenAlexfundno aff
P. Poungthong, Chaimongkol Saengow, Chanyut Kolitawong, A. Jeffrey Giacomin

Bibliographic record

VenuePhysics of Fluids · 2024
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaRoyal Golden Jubilee (RGJ) Ph.D. ProgrammeKing Mongkut's University of Technology North Bangkok
KeywordsDragCoatingDie swellExtrusionEccentricity (behavior)Newtonian fluidPhysicsElasticity (physics)MechanicsComposite materialPolymerDie (integrated circuit)Mechanical engineeringMaterials scienceEngineering

Abstract

fetched live from OpenAlex

In modern wire coating, the polymer is dragged through a round cylindrical die. Onto this drag flow, we superpose pressure-driven extrusion. We devote this paper to analyzing this extrusion in eccentric cylindrical coordinates. We find that, when the molten polymer is an elastic liquid, a recentring force, Fx, is exerted on the wire. This is how the wire is then coated concentrically. The lateral force acting on the wire thus matters. This also explains why the wire cannot be coated with Newtonian or nearly Newtonian polymer. The axial force on the wire, Fz, is always positive, and we find that the die eccentricity decreases Fz. This determines the required pulling force. Thus, the axial force acting on the wire also matters. We follow the method of Jones (1964) called polymer process partitioning, to obtain the coating velocity profile, v⌣z(ξ,θ), from which we get the coating thickness profile. We integrate this profile to get the flow rate, and thus, the average thickness. We also obtain the stresses in the extrudate. We include one detailed dimensional worked example to help engineers design coating dies.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.952
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.237
Teacher spread0.227 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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