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Record W2511646187 · doi:10.1002/cjce.22620

Simulation of concentrated suspensions in free surface systems

2016· article· en· W2511646187 on OpenAlexaffvenue
Mahyar Javidi, Andrew N. Hrymak

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsWestern University
Fundersnot available
KeywordsVolume of fluid methodVolume fractionMechanicsMaterials scienceFinite volume methodParticle (ecology)CylinderFree surfaceCoatingVolume (thermodynamics)Work (physics)Two-phase flowDiffusionComputer simulationSuspension (topology)Range (aeronautics)Boundary layerFlow (mathematics)Composite materialThermodynamicsPhysicsMechanical engineeringEngineeringMathematicsGeology

Abstract

fetched live from OpenAlex

Abstract This study deals with the numerical simulation of free‐surface concentrated suspensions using the finite volume method. The numerical procedure, based on the particle diffusion‐flux model, is implemented in a computational fluid dynamic platform for estimating the particle volume fraction in three‐dimensional flows with arbitrary geometry and boundary conditions. The Volume of Fluid (VOF) method has been applied to track the flow interface between liquid and gas, with the solid particles dispersed in the liquid phase, in the free coating process. A finite length cylinder is dip coated, where the substrate is pulled out of a concentrated suspension bath. In the current work, the initial solid particle volume fraction range is 0.1–0.4 and the withdrawal velocity varies in the range of 0.05–0.15 m/s. Comparisons are made between numerical simulation predictions and experimental results for coating layer thickness, with close agreement achieved.

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 score0.142
Threshold uncertainty score0.188

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 head0.008
GPT teacher head0.177
Teacher spread0.169 · 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

Citations3
Published2016
Admission routes2
Has abstractyes

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