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Record W4308624755 · doi:10.1063/5.0126381

Numerical simulation of electrokinetic control of miscible viscous fingering

2022· article· en· W4308624755 on OpenAlexafffund
Benedicta N. Nwani, C. Merhaben, Ian D. Gates, Anne M. Benneker

Bibliographic record

VenuePhysics of Fluids · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsUniversity of Calgary
FundersCanada First Research Excellence Fund
KeywordsElectrokinetic phenomenaElectric fieldMechanicsViscosityPhysicsInstabilityField strengthField (mathematics)MicrofluidicsFluid dynamicsMagnetic fieldThermodynamicsMaterials scienceNanotechnology

Abstract

fetched live from OpenAlex

Active control of viscous fingering (VF) is of critical importance for many industrial and experimental systems. Here, we numerically study the electrokinetic control of miscible VF using an externally applied electric field. Simulations for three intrinsically hydrodynamically unstable mobility ratios are carried out using two different configurations for each: case I where the high-viscosity resident fluid has higher electroosmotic mobility than the invading low-viscosity fluid and case II where the resident fluid has a lower electroosmotic mobility than the invading fluid. For both cases, the theoretical critical electric field value required to (de)-stabilize the interface is computed and electric fields around this value are applied in simulations. Qualitative results show that VF can be fully suppressed if an electric field is applied with an absolute value above the critical field strength. For case I, this means an electric field in the direction of the pressure-driven flow, while for case II, a field in opposite direction is required. Our quantitative analysis using interfacial and mixing lengths was used to support the qualitative findings. Even though any field strength applied in the right direction will reduce the instability, full suppression is only achieved if the absolute field strength is higher than the required critical field strength. The results from this work provide useful insights that can be applied to electrokinetically enhanced oil recovery, spreading of pollution zones in aquifers, band broadening in liquid chromatography, and electrokinetic soil remediation.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.369

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 head0.014
GPT teacher head0.245
Teacher spread0.231 · 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

Citations7
Published2022
Admission routes2
Has abstractyes

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