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Record W2159250041 · doi:10.1109/ias.1994.377612

Mathematical modelling of powder paint particle trajectories in electrostatic painting

2002· article· en· W2159250041 on OpenAlexaff
Fateh Ali, T. E. Base, I.I. Inculet

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsWestern University
Fundersnot available
KeywordsParticle (ecology)Range (aeronautics)Point particleElectrostaticsMechanicsCharge (physics)Electric fieldFlow (mathematics)Particle sizeMaterials scienceComputational physicsPhysicsClassical mechanicsEngineeringChemical engineeringComposite material

Abstract

fetched live from OpenAlex

The trajectories of charged powder particles in an electrostatic powder coating system were modelled considering electrical and fluid forces. The mathematical model employed an iterative technique wherein the charge simulation method was used to compute the electric field strength and the method of characteristics was used to compute the charge density in the gun-to-target region. The fluid flow between the electrostatic gun and the target was modelled using interpolated experimental data assuming stagnation point flow. Particle trajectories were simulated for size range 10-40 /spl mu/m and charge-to-mass ratios of 0.1-1 (-)/spl mu/C/gram. The simulation results showed good agreement with experimental data (charge and mass measurements) at several collection points on the painting target and provided valuable information concerning particle deposition.>

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.210
Teacher spread0.182 · 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 source (direct Gemma or distilled Codex), 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

Citations19
Published2002
Admission routes1
Has abstractyes

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