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Record W2796448621 · doi:10.11159/enfht18.126

Interfacial Instabilities of Shear-Thinning Fluids in HomogeneousPorous Media

2018· article· en· W2796448621 on OpenAlexafffund
Y.-H. Lee, Jalel Azaiez, Ian D. Gates

Bibliographic record

VenueProceedings of the World Congress on Momentum, Heat and Mass Transfer · 2018
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaWestern Canada Research Grid
KeywordsHomogeneousPorous mediumThinningShear thinningMaterials scienceShear (geology)PorosityMechanicsComposite materialGeologyRheologyPhysicsThermodynamics

Abstract

fetched live from OpenAlex

In this study, radial immiscible flow displacements in homogeneous porous media are modelled numerically in the case where the displacing fluid is Newtonian while the displaced one may be Newtonian or shear-thinning governed by Carreau's rheological model. The governing equations and interfacial jump conditions are solved numerically using the Immersed Interface Method and the Level Set Method to track the two fluids interfaces. The effects of four parameters, namely the mobility ratio and Capillary number for Newtonian flows and the Deborah number De and power-law index n, for shear thinning fluids are examined. Time contours of the interface revealing the finger structures that develop as a result of the viscosity mismatch between the two fluids are obtained to analyse and discuss the effects of the different parameters on the interfacial instability. Even though both characteristics of the shear-thinning fluid; De and n were found to affect the interfacial instability, it is revealed that the latter has a stronger effect, at least for the range of parameters that have been considered in this study.

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 score0.117
Threshold uncertainty score0.588

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 studies0.0000.001
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 head0.217
Teacher spread0.208 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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