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Record W4404111513 · doi:10.1098/rspa.2024.0206

Impact of temperature-reliant thermal conductivity and viscosity variations on the convection of Jeffrey fluid in a rotating cellular porous layer

2024· article· en· W4404111513 on OpenAlexaff
Dhananjay Yadav, Mukesh Kumar Awasthi, Ravi Ragoju, Krishnendu Bhattacharyya, Raghunath Kodi, Mohammad Hassan, Junye Wang

Bibliographic record

VenueProceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 2024
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsAthabasca University
Fundersnot available
KeywordsThermal conductivityViscosityConvectionPorosityLayer (electronics)MechanicsThermalMaterials sciencePorous mediumThermodynamicsNatural convectionThermal conductionFluid dynamicsComposite materialPhysics

Abstract

fetched live from OpenAlex

In this analysis, the collective impact of temperature-dependent thermal conductivity and viscosity variations on the convective instability of a Jeffrey fluid in a rotating layer of cellular porous material is examined using an improved Jeffrey–Darcy model. This study has significant implications for cellular foams made from plastics, ceramics and metals, in which radiative heat transmission can be taken as a diffusion practice. Utilizing the linear stability concept and Galerkin method, approximate analytical and numerical solutions accurate to one decimal place are offered. The analysis reveals that the effect of the thermal conductivity variation factor and the rotation factor is to postpone the convective wave, whereas the viscosity variation factor and the Jeffrey factor have a dual effect in the form of rotation. The range of the convective cell is reduced with cumulating thermal conductivity variation factor, viscosity variation factor, Jeffrey factor and rotation factor. In the absence of rotation, the range of the convective cell is not dependent on the Jeffrey factor or the viscosity variation factor. Furthermore, the outcomes are matched with the existing literature for the specific case of this investigation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.870
Threshold uncertainty score0.230

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 head0.218
Teacher spread0.208 · 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

Citations19
Published2024
Admission routes1
Has abstractyes

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