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Record W3036600024 · doi:10.26637/mjm0803/0019

Marangoni convection in superposed fluid and anisotropic porous layers with throughflow

2020· article· en· W3036600024 on OpenAlexaff
Y. H. Gangadharaiah, K. Ananda, H. Nagarathnamm

Bibliographic record

VenueMalaya Journal of Matematik · 2020
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsThroughflowMarangoni effectConvectionMechanicsGeologyPorous mediumPorosityFluid dynamicsMaterials scienceGeophysicsPhysicsGeotechnical engineering

Abstract

fetched live from OpenAlex

Marangoni convective flow of fluid layer overlying a porous layer with anisotropic permeability and thermal diffusivity is addressed. Flow analysis has been carried out in presence of throughflow. Beavers-Joseph's slip condition is applied to the fluid-porous layer interface. The boundaries are known to be rigid, but permeable, and insulated to fluctuations in temperature. The problem of own value resulting from the stability analysis is solved through regular perturbation technique. Flow pattern with the influence of pertinent parameters namely the throughflow parameter, mechanical, thermal anisotropy parameters Prandtl number and depth ratio is investigated. Expression of critical Marangoni number is computed and analyzed. It is found that the depth of the relative layers, the direction of throughflow and mechanical and thermal anisotropy parameters deeply affect system stability. Reducing the parameter of mechanical anisotropy and increasing the parameter of thermal anisotropy contributes to process stabilization. In addition, the probability of regulating surface driven convection is discussed in detail through the appropriate choice of physical parameters.

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 score0.290
Threshold uncertainty score0.419

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 head0.172
Teacher spread0.164 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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