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Record W3182916517 · doi:10.1515/ipp-2020-4051

Modeling Short-Range Interactions in Concentrated Newtonian Fiber Bundle Suspensions

2021· article· en· W3182916517 on OpenAlexaff
Nils Meyer, Andrew N. Hrymak, Luise Kärger

Bibliographic record

VenueInternational Polymer Processing · 2021
Typearticle
Languageen
FieldEngineering
TopicComposite Material Mechanics
Canadian institutionsWestern University
Fundersnot available
KeywordsMaterials scienceFiber bundleBundleFiberRheologyLubricationMechanical engineeringMolding (decorative)MechanicsComposite materialEngineering

Abstract

fetched live from OpenAlex

Abstract Sheet Molding Compounds (SMC) offer a cost efficient way to enhance mechanical properties of a polymer with long discontinuous fibers, while maintaining formability to integrate functions, such as ribs, beads or other structural reinforcements. During SMC manufacturing, fibers remain often in a bundled configuration and the resulting fiber architecture determines part properties. Accurate prediction of this architecture by simulation of flow under consideration of the transient rheology and transient fiber orientations can speed up the development process. In particular, the interaction of bundles is of significance to predict molding pressures correctly in a direct simulation approach, which resolves individual fiber bundles. Thus, this work investigates the tangential short-range lubrication forces between fiber bundles with analytical and numerical techniques. A relation between the effective sheared gap between bundles and the bundle separation distance at the contact point is found and compared to experimental results from literature. The result is implemented in an ABAQUS contact subroutine to incorporate short-range interactions in a direct bundle simulation framework.

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 score0.541
Threshold uncertainty score0.712

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)0.0010.000

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 head0.024
GPT teacher head0.273
Teacher spread0.249 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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