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Record W4392705286 · doi:10.1063/5.0190373

Behavior of small water droplets in a highly viscous flow in a converging and diverging channel

2024· article· en· W4392705286 on OpenAlexafffund
Doston Shayunusov, D. Eskin, Hongbo Zeng, Petr A. Nikrityuk

Bibliographic record

VenuePhysics of Fluids · 2024
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of Alberta
FundersAlliance de recherche numérique du CanadaCanada First Research Excellence FundCanada Research ChairsNatural Sciences and Engineering Research Council of CanadaSyncrudeSuncor Energy IncorporatedCanadian Natural Resources Limited
KeywordsMechanicsCoalescence (physics)Eulerian pathPhysicsCapillary actionContext (archaeology)ThermodynamicsGeology

Abstract

fetched live from OpenAlex

Understanding the evolution of water droplets moving in a highly viscous bulk flow (e.g., bitumen) has attracted increasing attention in the context of numerous separation technologies due to various issues relating to the environment (re-use of water) and engineering failures (corrosion of pipelines). With this in mind, the main objectives of this work are to explore the dynamics of water droplets with a diameter of seven micrometers, moving in highly viscous bitumen flowing through a smoothly converging and diverging 11-micron channel using three-dimensional (3D) and two-dimensional (2D) droplet-resolved simulations and to adjust an existing population balance model (PBM) to predict geometry-driven coalescence for different flow rates. The Eulerian–Eulerian (EE) method coupled with a new PBM is used to predict the behavior of water droplets with a diameter of 7 μm. Numerical simulations were carried out for various capillary numbers (0.1<Ca<3) and compared with the volume of fluid method combined with the level-set function (CLSVOF). Adaptive mesh refinement (up to six levels) was used in 3D and 2D CLSVOF simulations, producing interface cells measuring up to 30 nm. Good agreement was observed between EE-PBM and CLSVOF models. For comparison, we show the results of 2D CLSVOF simulations. This new PBM model can be used to predict water–oil separation in new cascade-formed geometries to enhance the coalescence of water droplets in highly viscous bulk flows.

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 score0.894
Threshold uncertainty score0.453

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)0.0000.000

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 head0.009
GPT teacher head0.204
Teacher spread0.194 · 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

Citations10
Published2024
Admission routes2
Has abstractyes

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