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Record W2093565183 · doi:10.1615/jpormedia.v17.i4.70

NUMERICAL SIMULATIONS OF REVERSIBLE REACTIVE FLOWS IN HOMOGENEOUS POROUS MEDIA

2014· article· en· W2093565183 on OpenAlexaff
Hesham Alhumade, Jalel Azaiez

Bibliographic record

VenueJournal of Porous Media · 2014
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsInstabilityPorous mediumChemical reactionViscous fingeringHomogeneousDiffusionConvectionMechanicsMaterials scienceConvective instabilityThermodynamicsChemical speciesViscous liquidPorosityChemistryPhysicsComposite material

Abstract

fetched live from OpenAlex

The effects of reversibility on the viscous fingering of miscible reactive flow displacements in homogeneous porous media are examined through numerical simulations. A model in which the viscosities mismatch between the reactants and the chemical product triggers the instability is adopted. The problem is governed by the continuity equation, Darcy's law, and the convection-diffusion-reaction equations, which are solved using a pseudo-spectral method. It was found that in general, chemical reversibility tends to attenuate the instability at the fronts, resulting in less complex fingers than in the nonreversible case. However, stronger chemical reversibility also leads to less diffuse and thinner finger structures. Furthermore, the chemical product was found to be homogeneously distributed in the porous medium in the case of the reversible reaction, while strong concentration gradients are observed in the nonreversible case. The study has also revealed that chemical reversibility is capable of enhancing the instability of a stable reactive front. It is also found that the rate of production can be the same for different cases of frontal instability for a period of time that increases with the increase in the magnitude of chemical reversibility.

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)0.001
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 score0.394
Threshold uncertainty score0.638

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.246
Teacher spread0.230 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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