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Record W2946503953 · doi:10.1002/cjce.23499

Analysis of Non‐Isothermal Viscous Flow Coalescence at Micro Scale

2019· article· en· W2946503953 on OpenAlexvenueno aff
Mohammadmahdi Kamyabi, Rahmat Sotudeh‐Gharebagh, Reza Zarghami, Khashayar Saleh

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsCoalescence (physics)Isothermal processDimensionless quantityThermodynamicsReynolds numberMechanicsMaterials scienceViscosityWeber numberThermalThermal conductionCapillary actionPhysicsTurbulence

Abstract

fetched live from OpenAlex

The non‐isothermal coalescence of two spherical bodies caused by capillary‐induced viscous flow was analyzed. Based on this analysis, a new dimensionless number ( K number) was introduced for defining thermal coalescence regimes. Based on the value of this number, coalescence may or may not be affected by thermal effects in different cases. To make this clearer, the conventional coalescence models of Frenkel‐Eshelby and Pokluda were modified by assuming viscosity as a temperature dependent variable. This was conducted by considering the effects of temperature on the viscosity of the involved material through evaluating different expressions including linear and Reynolds and Williams‐Landel‐Ferry (WLF) equations. The results of the modified models for the bridge growth rate show that temperature changes significantly affect the kinetics of coalescence, particularly when the characteristic times of coalescence and heat conduction are in the same order, i.e., moderate K numbers. This analysis is applicable for diverse situations since viscous flow coalescence occurs in various physical and industrial applications of particles or droplets.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score0.423

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 head0.003
GPT teacher head0.159
Teacher spread0.157 · 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 designSimulation or modeling
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

Citations10
Published2019
Admission routes1
Has abstractyes

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