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Record W4247408697 · doi:10.32920/ryerson.14649312.v1

Natural Convection and Soret Effect in a Multi-Layered Liquid and Porous System

2021· preprint· en· W4247408697 on OpenAlex
Hussam K. Jawad

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicField-Flow Fractionation Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsThermophoresisPorosityMaterials scienceMixing (physics)ConvectionTemperature gradientPorous mediumNatural convectionThermodynamicsHydrocarbonLayer (electronics)ChemistryComposite materialNanoparticleNanotechnologyOrganic chemistryMeteorology

Abstract

fetched live from OpenAlex

We investigated the onset of natural convection and thermodiffusion in an initially quiescent multi-layer system consisting of a porous layer sandwiched between two layers of a binary mixture, while the whole system is being heated from above. Two different water-alcohol mixtures were used with Soret coefficients of opposite sign. Then in similar situation a hydrocarbon mixture were investigated. It was found that when the Soret coefficient is negative, the lighter species migrates towards the colder surface while the denser species migrates towards the hotter surface. When the Soret coefficient is positive, the lighter species migrates towards the hotter surface while the denser species migrates towards the colder surface. Also, increasing the temperature difference leads to a greater separation of the mixture components because of the increase in the density gradient. In addition, increasing the porosity reduces the separation ratio due to the increased fluid mixing in the pores.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.881

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 integrity0.0000.001
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 head0.230
Teacher spread0.222 · 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

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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