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Hydrodynamic Interactions Among Bubbles, Drops, and Particles in Non-Newtonian Liquids

2017· article· en· W2574790547 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnual Review of Fluid Mechanics · 2017
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNewtonian fluidMechanicsViscoelasticityRheologyNon-Newtonian fluidIntuitionSurface tensionClassical mechanicsDrop (telecommunication)PhysicsBubbleThermodynamicsComputer science

Abstract

fetched live from OpenAlex

The understanding of hydrodynamic forces around particles, drops, or bubbles moving in Newtonian liquids is modestly mature. It is possible to obtain predictions of the attractive–repulsive interaction for moving ensembles of dispersed particulate objects. There is a certain intuition of what the effects of viscous, inertial, and surface tension forces should be. When the liquid is non-Newtonian, this intuition is gone. In this review, we summarize recent efforts at gaining fundamental understanding of hydrodynamic interactions in non-Newtonian liquids. Due to the complexity of the problem, most investigations rely on experimental observations. However, computations of non-Newtonian fluid flow have made increasingly significant contributions to our understanding of particle, drop, and bubble interactions. We focus on gravity-driven flows: rise or sedimentation of single spheroidal objects, pairs, and dispersions. We identify the effects of two main rheological attributes—viscoelasticity and shear-dependent viscosity—on the interaction and potential aggregation of particles, drops, and bubbles. We end by highlighting the open questions in the subject and by suggesting future directions toward the fundamental physics as well as applications.

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.

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)0.001
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 score0.970
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.262
Teacher spread0.255 · 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