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Record W2906546655 · doi:10.1063/1.5065388

Viscosity effects in density-stable miscible displacement flows: Experiments and simulations

2018· article· en· W2906546655 on OpenAlexafffund
Ali Etrati, I.A. Frigaard

Bibliographic record

VenuePhysics of Fluids · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTheoretical and Computational Physics
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMechanicsPhysicsViscosityVolume of fluid methodFinite volume methodShear thinningDisplacement (psychology)Density contrastSmoothed-particle hydrodynamicsFlow (mathematics)Thermodynamics

Abstract

fetched live from OpenAlex

We study characteristics of miscible displacement flows in inclined pipes with density-stable configuration, meaning the lighter fluid is pumped to displace the heavier fluid downward along the pipe. Experiments have been completed in a pipe covering a broad range of inclination angles, flow rates, and viscosity configurations. Viscosity contrast between the fluids is obtained by adding xanthan gum to water, while glycerol is used to achieve density difference. Novel instabilities appear in the case of shear-thinning displacements. Numerical simulations are performed using the finite volume package OpenFOAM. The unsteady three-dimensional Navier-Stokes equations are used with the volume of fluid method to capture the interface between the fluids. A number of numerical cases are compared against the experiments to benchmark the model favourably. The code allows us to examine in detail the 3D structure of the propagating front and other secondary 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 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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.266
Teacher spread0.257 · 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

Citations31
Published2018
Admission routes2
Has abstractyes

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