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Record W3133784268 · doi:10.1002/cjce.24096

<scp>CFD</scp> analysis of blade coating from a reservoir onto a horizontal substrate using a homogeneous two‐phase model

2021· article· en· W3133784268 on OpenAlexafffundvenue
Arpan R. Singh, Scott J. Ormiston

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Thin Films
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMechanicsLaminar flowMaterials scienceSurface tensionNewtonian fluidComputational fluid dynamicsNon-Newtonian fluidDragViscosityPhase (matter)Volume of fluid methodFlow (mathematics)ChemistryComposite materialThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract A two‐phase numerical analysis is performed of two‐dimensional, laminar, liquid flow from a reservoir onto a horizontal, rigid, moving substrate. The homogeneous two‐phase model in commercial CFD code CFX is used to model the liquid plus a region containing both liquid and air near the phase interface and downstream of the blade region. Slow convergence due to surface tension modelling required initialization from intermediate results omitting surface tension. Comparisons made with two previous numerical analyses demonstrate the performance of this approach. For a particular upstream reservoir geometry, the effects of changing the substrate speed and the liquid properties from a Newtonian fluid to a Carreau‐Yasuda non‐Newtonian fluid on the pressure field and the downstream film height are studied. The details of the liquid recirculation in the reservoir and the overall pressure distribution are discussed. New results are presented for the meniscus position and contact angle, which are fully predicted by the two‐phase approach. The meniscus position was strongly influenced by substrate speed and liquid properties, whereas the contact angle did not change significantly with changes in substrate speed and dynamic viscosity for the Newtonian fluid nor with changes in the functional parameters for the non‐Newtonian fluid.

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 score0.284
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.214
Teacher spread0.200 · 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
Published2021
Admission routes3
Has abstractyes

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