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Record W3210848206 · doi:10.1063/5.0070653

The role of a porous wall on the solute dispersion in a concentric annulus

2021· article· en· W3210848206 on OpenAlexafffund
Morteza Dejam, Hassan Hassanzadeh

Bibliographic record

VenuePhysics of Fluids · 2021
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsUniversity of Calgary
FundersUniversity of CalgaryUniversity of Wyoming
KeywordsAnnulus (botany)Porous mediumMechanicsPéclet numberAdvectionPhysicsDispersion (optics)PorosityMaterials scienceThermodynamicsOpticsComposite material

Abstract

fetched live from OpenAlex

We studied the role of a porous wall on the dispersion of a solute in an annular space in the presence of a pressure-driven flow. The continuity of concentration and mass flux at the annulus–porous medium interface is used to handle the interaction between the two media. The Reynolds decomposition technique and the cross-sectional averaging method are used to derive a reduced-order advective–dispersive transport model with the associated equivalent diffusion and advection terms. The resultant dispersion and advection coefficients for an annulus with a porous wall are fully characterized as a function of the inner solid core radius of the annulus. The findings reveal that dispersion is retarded in the presence of the inner core in an annulus for both porous and non-porous outer walls. The results also indicate that the transition to a fully advective regime occurs at higher Peclet numbers for an annulus with a porous outer wall. The results suggest that dispersion and advection in an annulus can be controlled by proper selection of the inner core diameter. We also identified the inner core size where the solute dispersion in an annulus with a porous wall is minimum compared to a non-porous boundary. The developed model and insights find applications in many engineering processes where a fluid containing a solute in an annulus interacts with a surrounding porous medium.

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 score0.300
Threshold uncertainty score0.205

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 head0.008
GPT teacher head0.202
Teacher spread0.194 · 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

Citations10
Published2021
Admission routes2
Has abstractyes

Explore more

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