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Record W4294791124 · doi:10.31399/asm.cp.itsc2007p0190

Numerical Simulation of the Solution Precursor Plasma Spraying Process

2007· article· en· W4294791124 on OpenAlexaff
Y. Shan, Thomas W. Coyle, J. Mostaghimi

Bibliographic record

VenueThermal spray · 2007
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSolution precursor plasma sprayMaterials scienceJet (fluid)CeramicPlasmaThermal sprayingParticle (ecology)Atmospheric-pressure plasmaGas dynamic cold sprayPrecipitationSubstrate (aquarium)Process (computing)CoatingMechanicsChemical engineeringComposite materialMeteorologyPhysicsEngineeringComputer science

Abstract

fetched live from OpenAlex

Abstract Solution precursor plasma spraying has been used to produce finely structured ceramic coatings with nano- and sub-micrometric features. This process involves the injection of a solution spray of ceramic salts into a DC plasma jet under atmospheric condition. During the process, the solvent vaporizes as the droplet travel downstream. Solid particles are finally formed due to the precipitation of the solute, and the particle are heated up and accelerated to the substrate to generate the coating. This paper describes a 3D model to simulate the transport phenomena and the trajectory and heating of the solution spray in the process. The jet-spray two-way interactions are considered. A simplified model is employed to simulate the evolution process and the formation of the solid particle from the solution droplet in the plasma jet. The temperature and velocity fields of the jet are obtained and validated. The particle size, velocity, temperature and position distribution on the substrate are predicted.

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: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.011
GPT teacher head0.252
Teacher spread0.240 · 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

Citations1
Published2007
Admission routes1
Has abstractyes

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