MétaCan
Menu
Back to cohort
Record W2469743525 · doi:10.1063/1.4954232

Investigation on dip coating process by mathematical modeling of non-Newtonian fluid coating on cylindrical substrate

2016· article· en· W2469743525 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePhysics of Fluids · 2016
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Thin Films
Canadian institutionsUniversity of WaterlooWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCoatingNewtonian fluidRheologyNon-Newtonian fluidViscositySurface tensionMechanicsSubstrate (aquarium)PhysicsComposite materialOpticsThermodynamicsMaterials science

Abstract

fetched live from OpenAlex

A mathematical model for the dip coating process has been developed for cylindrical geometries with non-Newtonian fluids. This investigation explores the effects of the substrate radius and hydrodynamic behavior of the non-Newtonian viscous fluid on the resulting thin film on the substrate. The coating fluid studied, Dymax 1186-MT, is a resin for fiber optics and used as a matrix to suspend 1 vol. % titanium dioxide particles. The coating substrate is a 100 μm diameter fiber optic diffuser. Ellis viscosity model is applied as a non-Newtonian viscous model for coating thickness prediction, including the influence of viscosity in low shear rates that occurs near the surface of the withdrawal film. In addition, the results of the Newtonian and power law models are compared with the Ellis model outcomes. The rheological properties and surface tension of fluids were analyzed and applied in the models and a good agreement between experimental and analytical solutions was obtained for Ellis model.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.713

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 head0.022
GPT teacher head0.233
Teacher spread0.211 · 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