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Record W1990896278 · doi:10.1002/pen.20267

Fast and efficient simulation of 3D creeping flow in ducts using a space‐marching algorithm

2005· article· en· W1990896278 on OpenAlexaff
Andrés Torres, Andrew N. Hrymak, J. Vlachopoulos, Dan Moran, Z. DaFonseca

Bibliographic record

VenuePolymer Engineering and Science · 2005
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsViscoelasticityFlow (mathematics)MechanicsAlgorithmComputer scienceStokes flowMaterials scienceComputer simulationMechanical engineeringPhysicsEngineeringComposite material

Abstract

fetched live from OpenAlex

Abstract In this paper, an alternative solution algorithm for the creeping, 3D viscoelastic flow simulation is offered. It is based on the Parabolized Navier‐Stokes Equations formulation (PNSE), which reduces the 3D problem to a sequence of 2D problems marched along a preferred direction (the dominant flow in the duct). This algorithm is extended to the limit Re approaching 0 (creeping flow) and the viscoelastic component of the extra‐stress tensor that appears in the momentum equation is treated as a body force. The numerical results obtained with Criminale‐Ericksen‐Filbey (CEF) and Modified Phan‐Thien Tanner (MPTT) models are compared against previous numerical and experimental data. As an application of the code, prediction of viscoelastic developing flow in channels is studied, since it is a common feature in many polymer processing applications, such as extrusion coating, film blowing, injection molding, and others in which there are confined flow in ducts after metering in extruder screw. The present algorithm offers a fast and efficient solution approach to the complex problem of 3D viscoelastic flow, allowing the detailed visualization of flow phenomena that would otherwise require significant computational resources. POLYM. ENG. SCI. 45:249–259, 2005. © 2005 Society of Plastics Engineers.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score0.411

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 head0.008
GPT teacher head0.233
Teacher spread0.225 · 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

Citations1
Published2005
Admission routes1
Has abstractyes

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