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Record W2976762349 · doi:10.1139/cjp-2019-0120

The investigation of the viscous fingering phenomenon of immiscible fluids displacement by the Lattice Boltzmann method

2019· article· en· W2976762349 on OpenAlexvenueno aff
Peisheng Li, Chengyu Peng, Peng Du, Ying Zhang, Boheng Dong, Ming Ma

Bibliographic record

VenueCanadian Journal of Physics · 2019
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsnot available
FundersNatural Science Foundation of Jiangxi ProvinceNational Natural Science Foundation of China
KeywordsViscous fingeringLattice Boltzmann methodsViscosityWettingSurface tensionPhysicsMicrochannelReynolds numberCapillary numberMechanicsViscous liquidDisplacement (psychology)ThermodynamicsCapillary actionMaterials sciencePorous mediumComposite materialPorosity

Abstract

fetched live from OpenAlex

In this paper, the viscous fingering phenomena of two immiscible fluids with a large viscosity ratio was simulated by the Lattice Boltzmann method. The Rothman–Keller Lattice Boltzmann model was applied to study the viscous fingering phenomena in a microchannel where the high viscosity fluids were displaced by low viscosity fluids. We have investigated the influences of parameters such as viscosity ratio (M), surface wettability, capillary number (Ca), and Reynolds number (Re) on finger structures, breakthrough time (Ts), and areal sweep efficiency (Se). In particular, the effects of surface tension and large viscosity ratio on the phenomenon of fluid accumulation were intensively studied. The simulation results showed that the fluid accumulation became more obvious gradually with the increase of M, which led to more serious displacement effects. Moreover, Se increased as the contact angle increased. Besides, as the viscous fingering phenomenon weakened, the phenomenon of fluid accumulation became more evident. Furthermore, the finger pattern had a tendency to increase as the value of Ca and Re increased, and the phenomenon of fluid accumulation decreased with the decrease of Ts and Se.

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)0.001
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 score0.532
Threshold uncertainty score0.251

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 communication0.0000.000
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 head0.013
GPT teacher head0.233
Teacher spread0.220 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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