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Record W2564820738 · doi:10.1002/aic.15626

Stability of gravitationally unstable double diffusive transient boundary layers with variable viscosity in porous media

2016· article· en· W2564820738 on OpenAlexaff
Nasser Sabet, Hassan Hassanzadeh, Jalal Abedi

Bibliographic record

VenueAIChE Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsViscosityTransient (computer programming)Porous mediumInstabilityLewis numberMechanicsBoundary (topology)ConvectionBoundary layerRayleigh numberBoundary value problemThermodynamicsStability (learning theory)PhysicsPorosityNatural convectionChemistryMathematical analysisMathematicsMass transfer

Abstract

fetched live from OpenAlex

We study the double diffusive convection (DDC) in porous media through linear stability analysis (LSA) and direct numerical simulations (DNS). Unlike the previous studies that assume static solutal or thermal fields, the developed model is able to capture the transient behavior of both fields. We show that under the assumption of static field, the role of Lewis number cannot be distinguished. Under transient fields, we conclude that higher Lewis numbers result in earlier instability of the boundary layers. Moreover, the effect of viscosity contrast is explained in terms of the mobility of the boundary layer. The DNS results confirm the validity of LSA predictions. We also obtain the critical Rayleigh number and show that in the presence of viscosity contrast, it can be much smaller than the conventional limit of 4π 2 . This study provides a better understanding of the transient nature of DDC in the presence of viscosity variations. © 2017 American Institute of Chemical Engineers AIChE J , 63: 2471–2482, 2017

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score0.437

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 head0.011
GPT teacher head0.207
Teacher spread0.196 · 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

Citations14
Published2016
Admission routes1
Has abstractyes

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