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Record W3087402786 · doi:10.1063/5.0016534

Cross-stream migration of droplets in a confined shear-thinning viscoelastic flow: Role of shear-thinning induced lift

2020· article· en· W3087402786 on OpenAlexaff
S. Hazra, Sushanta K. Mitra, A. K. Sen

Bibliographic record

VenuePhysics of Fluids · 2020
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsUniversity of Waterloo
FundersIndian Institute of Technology Madras
KeywordsMechanicsViscoelasticityPhysicsShear thinningReynolds numberLift (data mining)Shear rateDrop (telecommunication)RheologyStreamlines, streaklines, and pathlinesWeissenberg numberShear flowNewtonian fluidTurbulenceThermodynamics

Abstract

fetched live from OpenAlex

Shear-thinning viscoelastic (STVE) flows exhibit intriguing phenomena owing to their complex rheology and the coupling of various forces involved. Here, we present an understanding of the cross-stream migration of droplets in a confined STVE flow and unravel the role of a shear-thinning induced lift force (FSM) in their dynamical behavior. We perform experiments with popular STVE liquids of different molecular weights and concentrations (c) for Reynolds numbers Re < 1 and Weissenberg numbers Wi = 0.01–7.4. Our results reveal larger droplets (of drop-to-channel ratio β ≥ 0.28) that follow their original streamlines, whereas smaller droplets (β ≤ 0.2) exhibit center ward migration and the migration rates depend upon the drop-to-medium viscosity (k) and elasticity (ξ) ratios. The lateral displacement of droplets is tracked using high-speed imaging that is used to estimate the relevant forces using suitable correlations. We find that the migration dynamics of droplets is underpinned by the non-inertial lift (FNIL), viscoelastic lift (FVM, FVD), and shear-thinning induced lift (FSM) forces. We provide experimental evidence of the proposed FSM and, from analytical scaling and empirical modeling, develop an expression for FSM ∼ ΔμΔγ̇D3.7/h1.7 (with R2 = 0.95) for an object at a distance h from the wall and with a drop in viscosity Δμ and strain rate Δγ̇ across its diameter D. Our study sheds light on the underlying dynamics on droplets in an STVE medium and opens up avenues for sorting and focusing of drops in an STVE medium at low Re.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.254
Teacher spread0.237 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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