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Record W3159433313 · doi:10.1063/5.0046729

Dynamics of rigid particles in a confined flow of viscoelastic and strongly shear-thinning fluid at very small Reynolds numbers

2021· article· en· W3159433313 on OpenAlexaff
S. Hazra, A. Nath, Sushanta K. Mitra, A. K. Sen

Bibliographic record

VenuePhysics of Fluids · 2021
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversity of Waterloo
FundersMinistry of Education, IndiaIndian Institute of Technology Madras
KeywordsViscoelasticityShear thinningReynolds numberMechanicsPhysicsNewtonian fluidLift (data mining)Shear flowShear rateViscosityRheologyClassical mechanicsShear (geology)Non-Newtonian fluidDynamics (music)Fluid dynamicsThermodynamicsTurbulenceMaterials science

Abstract

fetched live from OpenAlex

Despite growing interest in the focusing and manipulation of particles in non-Newtonian fluids in confined flows, the combined effect of viscoelastic and shear-thinning effects on particle dynamics is not well understood. Herein, we report the dynamics of rigid microparticles in confined flows of strongly shear-thinning viscoelastic (STVE) fluids at very low Reynolds numbers. Our experiments with different STVE fluids reveal five different regimes: original streamline, bimodal, center migration, defocusing, and wall migration (WM), depending upon the fluid properties and flow rates. It is found that the occurrence of the different regimes depends on the STVE parameter (ψ) and average strain rate (γ̇¯). We find that the dynamics of particles in the different regimes is underpinned by the synergy between viscoelastic lift force (FVE) and shear-thinning lift force (FST). Numerical simulation results of strain rate and viscosity profiles at different ψ and γ̇¯ enable estimation of the forces and explaining the dynamics observed. We expect that our study will find relevance in applications involving positioning and manipulation of particles in confined flows of STVE fluids.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.823
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.219
Teacher spread0.209 · 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 designBench or experimental
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

Citations11
Published2021
Admission routes1
Has abstractyes

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