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Record W2776620046 · doi:10.1103/physreve.96.063114

Control of viscous fingering by nanoparticles

2017· article· en· W2776620046 on OpenAlexafffund
Nasser Sabet, Hassan Hassanzadeh, Jalal Abedi

Bibliographic record

VenuePhysical review. E · 2017
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Dynamics and Properties
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanoparticleViscosityViscous fingeringMaterials scienceDiffusionMixing (physics)Stability (learning theory)MechanicsInstabilityDynamics (music)Chemical physicsNanotechnologyThermodynamicsComputer sciencePhysicsPorous mediumComposite material

Abstract

fetched live from OpenAlex

A substantial viscosity increase by the addition of a low dose of nanoparticles to the base fluids can well influence the dynamics of viscous fingering. There is a lack of detailed theoretical studies that address the effect of the presence of nanoparticles on unstable miscible displacements. In this study, the impact of nonreactive nanoparticle presence on the stability and subsequent mixing of an originally unstable binary system is examined using linear stability analysis (LSA) and pseudospectral-based direct numerical simulations (DNS). We have parametrized the role of both nondepositing and depositing nanoparticles on the stability of miscible displacements using the developed static and dynamic parametric analyses. Our results show that nanoparticles have the potential to weaken the instabilities of an originally unstable system. Our LSA and DNS results also reveal that nondepositing nanoparticles can be used to fully stabilize an originally unstable front while depositing particles may act as temporary stabilizers whose influence diminishes in the course of time. In addition, we explain the existing inconsistencies concerning the effect of the nanoparticle diffusion coefficient on the dynamics of the system. This study provides a basis for further research on the application of nanoparticles for control of viscosity-driven instabilities.

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 score0.006
Threshold uncertainty score0.223

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 head0.016
GPT teacher head0.305
Teacher spread0.289 · 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

Citations27
Published2017
Admission routes2
Has abstractyes

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