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Record W1980049243 · doi:10.1002/fld.630

A low‐dimensional description of transient shear‐thinning free‐surface flow in thin cavities, as applied to injection molding

2003· article· en· W1980049243 on OpenAlexaff
Sheng X. Zhang, Roger E. Khayat

Bibliographic record

VenueInternational Journal for Numerical Methods in Fluids · 2003
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Thin Films
Canadian institutionsWestern University
Fundersnot available
KeywordsMechanicsFree surfaceFlow (mathematics)Laplace transformMaterials scienceMathematicsGeometryMathematical analysisPhysics

Abstract

fetched live from OpenAlex

Abstract A spectral methodology is proposed to examine the influence of shear thinning on the transient free‐surface flow inside a three‐dimensional thin cavity. The problem is closely related to the filling stage during the injection molding process. The moving domain is mapped onto a rectangular domain at each time step of the computation. A modified pressure is introduced that is governed by the Laplace's equation. The flow field is expanded in Fourier series along the lateral direction in the mapped domain, and the Galerkin projection is used to derive the equations that govern the expansion coefficients, which are solved using a variable‐step finite‐difference scheme. This approach is valid for simple and complex cavities as illustrated for the cases of a flat plate with variable and constant thickness. It is shown that, even for highly non‐linear shear‐thinning flow, only a few modes are needed for convergence. Shear thinning generally influences the flow behaviour. However, shear thinning may enhance or prohibit the flow, depending whether the flow rate at the entrance of the cavity is fast or slow, respectively. Copyright © 2004 John Wiley & Sons, Ltd.

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)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.183
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.022
GPT teacher head0.320
Teacher spread0.298 · 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
GenreMethods

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
Published2003
Admission routes1
Has abstractyes

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