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Record W2754683047 · doi:10.1002/pc.24580

Two dimensional long‐flexible fiber orientation simulation in squeeze flow

2017· article· en· W2754683047 on OpenAlexafffund
Gleb Meirson, Andrew N. Hrymak

Bibliographic record

VenuePolymer Composites · 2017
Typearticle
Languageen
FieldEngineering
TopicComposite Material Mechanics
Canadian institutionsWestern University
FundersGeneral Motors of Canada
KeywordsMaterials scienceCompression moldingFlow (mathematics)FiberSimple shearOrientation (vector space)Shear (geology)Shear flowComposite materialMolding (decorative)MechanicsGeometryMathematicsPhysics

Abstract

fetched live from OpenAlex

Long‐flexible fiber orientation under the influence of a squeeze flow field is an important engineering problem. Fiber orientation models have been constructed for a short fiber in simple shear. This article uses the long fiber model previously proposed by the authors (Meirson and Hrymak, Polymer Composites, 37, 2425 (2015)). This new model accounts for flow velocity gradient along the fiber and allows the fiber to exhibit flexibility. In this study, this model is applied to solve a two‐dimensional squeeze flow case that approximates compression molding flow. It was found that unlike the simple shear flow case in the squeeze flow case the relative dimensions of the system has a great effect on fiber orientation. In addition, several rules of thumb for compression molding are proposed from the results. POLYM. COMPOS., 39:4656–4665, 2018. © 2017 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 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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.266
Teacher spread0.252 · 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

Citations7
Published2017
Admission routes2
Has abstractyes

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