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Record W2157037842 · doi:10.1002/cjce.20356

Numerical study of drop spreading over saturated pores

2010· article· en· W2157037842 on OpenAlexvenueno aff
Siddhartha F. Lunkad, Rishi Maiti, Vivek V. Buwa, K.D.P. Nigam

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsWettingVolume of fluid methodDrop (telecommunication)PorosityPorous mediumSurface tensionMaterials scienceMechanicsContact angleChemistryThermodynamicsComposite materialFlow (mathematics)PhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract Numerical study of the dual effect of pores in liquid spreading over porous surface (flat and spherical) whereby liquid movement is facilitated as well as restricted (visualised by Khanna and Nigam [Khanna and Nigam, Chem. Eng. Sci. 57, 3401–3405 (2002)]) is presented to improve the present understanding of wetting. Using the volume of fluid (VOF) method in a two‐dimensional solution domain, the influence of various parameters viz. drop volume, pore density, surface wettability and liquid properties on the liquid spreading over 2D pellets with saturated pores was numerically investigated. Simulation results were found to capture the key features of the liquid spreading over non‐porous and porous surfaces qualitatively. The variation of spread factor and the apex height of liquid film with time were in a good qualitative agreement with the concept of dual action of pores. The liquid spreading was observed to have a direct proportionality with the pore density, that is, higher the number of pores, better was the liquid spreading. It was also observed that the influence of the pores on the liquid spreading was reduced with decrease in the surface wettability. It is expected that this numerical analysis of the liquid droplet spreading over saturated porous surface will be useful for better understanding of the physics of drop and pore (saturated) interactions.

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 score0.697
Threshold uncertainty score0.392

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 head0.004
GPT teacher head0.180
Teacher spread0.176 · 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

Citations6
Published2010
Admission routes1
Has abstractyes

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