MétaCan
Menu
Back to cohort
Record W3178349704 · doi:10.1063/5.0054659

Dynamics of buoyancy driven miscible iso-viscous flows in heterogeneous layered porous media

2021· article· en· W3178349704 on OpenAlexafffund
Youssef Elgahawy, Jalel Azaiez

Bibliographic record

VenuePhysics of Fluids · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBuoyancyPorous mediumPermeability (electromagnetism)HomogeneousMechanicsInstabilityPhysicsStratified flowsPorosityStratified flowThermodynamicsMaterials scienceTurbulenceChemistryComposite material

Abstract

fetched live from OpenAlex

Buoyancy-driven instabilities in horizontally layered heterogeneous porous media are investigated using numerical simulations. The analysis is conducted for two different permeability distributions, where the permeability attains its maximum (minimum) at the initial interface. The effects of the frequency of layers (q) and variance of the permeability distribution (s) under different scenarios of density mismatches were analyzed and characterized both qualitatively and quantitatively. Results revealed that heterogeneity induces undulated more diffuse finger structures compared to the homogeneous case. In cases where the permeability at the initial interface is maximum, it is found that the larger the q, the less unstable the flow. It is shown that the onset time of the instability increases with increasing number of layers and decreases with increasing heterogeneity variance. Moreover, it is revealed that flow mixing increases (decreases) with increasing heterogeneity variance before (after) a critical flow time. The trends observed are, however, reversed in the case of shifted permeability heterogeneity where the smallest permeability is at the initial interface. Interestingly, it was found that for the shifted permeability distribution, an unstable flow in a homogeneous medium can be fully stabilized when a small number of layers are used in the heterogeneous case.

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 score0.221
Threshold uncertainty score0.672

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 head0.010
GPT teacher head0.286
Teacher spread0.276 · 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

Citations9
Published2021
Admission routes2
Has abstractyes

Explore more

Same venuePhysics of FluidsSame topicNMR spectroscopy and applicationsFrench-language works237,207