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Record W2023868011 · doi:10.3390/coatings4030465

A Study of a Powder Coating Gun Near Field: Particle Flow in an Isothermal Staggered Concentric Air Jet

2014· article· en· W2023868011 on OpenAlexaff
Graham Payne, Darko Matovic, E. W. Grandmaison

Bibliographic record

VenueCoatings · 2014
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsQueen's University
Fundersnot available
KeywordsMaterials scienceJet (fluid)NozzleParticle (ecology)MechanicsIsothermal processOpticsPhysicsThermodynamics

Abstract

fetched live from OpenAlex

An experimental study of inert particle dispersion in an isothermal concentric air jet near field was conducted for cases of standard non-staggered and alternative staggered jet nozzles, each taken from a polymer powder flame deposition gun. The experimental work consisted of analysis of high speed digital images of the inert two phase isothermal jet flow, illuminated by a laser light sheet along and across the jet axis. The analysis of particle spread in the jet, represented by families of particle density distributions, clearly showed that the staggered nozzles resulted in a better-focused flow, with narrower distributions in the near field, and in the elimination of the recirculation zone that disrupted the particle flow in a non-staggered nozzle arrangement. In all cases, histograms of the cross-sectional particle area density were found to be approximately Gaussian. It was also found that there was a wide variation in the size and shape of the ground polymer particles used and these two characteristics caused a wide variation in the radial and axial velocities of the particles. Despite the differences between single-phase numerical simulations and experimental results, reported in Payne et al. [1], the introduction of particles into a numerical model produced satisfactory agreement with the particle velocities found experimentally.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score0.688

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 head0.013
GPT teacher head0.248
Teacher spread0.234 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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