MétaCan
Menu
Back to cohort
Record W2777777167 · doi:10.1139/cjp-2017-0640

The onset of penetrative convection stimulated by internal heating in a magnetic nanofluid saturating a rotating porous medium

2017· article· en· W2777777167 on OpenAlexvenueno aff
Amit Mahajan, Mahesh Kumar Sharma

Bibliographic record

VenueCanadian Journal of Physics · 2017
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsNanofluidLewis numberPhysicsThermophoresisTaylor numberRayleigh numberThermal diffusivityMechanicsConvectionDarcy numberPorous mediumThermodynamicsWork (physics)Natural convectionHeat transferPorosityMaterials science

Abstract

fetched live from OpenAlex

In this work, we investigate the effect of rotation on the onset of penetrative convection stimulated by internal heating in a thin layer of magnetic nanofluid saturating a porous medium. A model that includes the effect of Brownian diffusion, thermophoresis, and magnetophoresis is considered, while the Brinkman model is used for the porous medium. The following three boundary conditions are considered: rigid–rigid, rigid–free, and free–free. We discretized the partial differential equations by applying the Chebyshev pseudospectral method and used the QZ algorithm to solve the resulting eigenvalue problem for water and ester-based magnetic nanofluids. The nature of stability is determined by using the numerical method and is found to be stationary. The results indicate that the onset of convection is advanced with an increase in the Lewis number Le, concentration Rayleigh number Rn, and modified diffusivity ratio N A but the opposite is true in the case with an increase in the width of magnetic nanofluid layer d, Langevin parameter α L , porosity ε, Darcy number Da, modified diffusivity ratio [Formula: see text], and Taylor number T A . Moreover, the parameter N B does not affect the stability of the system significantly.

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 score0.251
Threshold uncertainty score0.996

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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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

Citations16
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of PhysicsSame topicNanofluid Flow and Heat TransferFrench-language works237,207