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Record W4403089766 · doi:10.1016/j.jnnfm.2024.105328

Stokes layers in complex fluids

2024· article· en· W4403089766 on OpenAlexaff
Duncan R. Hewitt, Neil J. Balmforth

Bibliographic record

VenueJournal of Non-Newtonian Fluid Mechanics · 2024
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversity of British Columbia
FundersDivision of Mathematical SciencesEngineering and Physical Sciences Research Council
KeywordsComputer scienceGeology

Abstract

fetched live from OpenAlex

Stokes’s second problem is reconsidered for three models of complex fluids: an elasto-viscoplastic fluid, a thixotropic viscoplastic fluid and a discontinuously shear-thickening fluid. In each case, the Stokes-layer dynamics is interrogated with a view to examining the signatures of the detailed rheology. Significant deformations are possible below the yield stress for elasto-viscoplastic fluids as a result of the excitation of elastic waves, particularly near resonances. Thixotropic fluids with viscosity bifurcations layer internally, but surface-speed signatures mostly appear similar to those for simple yield-stress fluids. Stokes-layer oscillations of discontinuous shear thickening fluids can prompt abrupt increases in viscosity, introducing sudden jumps in surface speed. Pre-existing experimental results for layers of kaolin slurries in a motorized, oscillating tray are reconsidered and compared with the results for elasto-viscoplastic and thixotropic fluids. • Stokes layers of three model fluids are explored. • Elasto-viscoplastic fluids deform significantly if elastic waves become excited. • Thixotropic fluids layer internally through viscosity bifurcations. • Discontinuous shear-thickening fluids jam during Stokes-layer oscillations

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.002
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 head0.259
Teacher spread0.245 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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