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Record W4294830894 · doi:10.31399/asm.cp.itsc2006p1143

Formation of Pores in Thermal Spray Coatings due to Incomplete Filling of Voids under Solid Particles

2006· article· en· W4294830894 on OpenAlexaff
Maohan Xue, J. Mostaghimi, S. Chandra

Bibliographic record

VenueThermal spray · 2006
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials sciencePorosityLaminar flowHeat transferMechanicsThermodynamicsFinite volume methodAdiabatic processParticle (ecology)CompressibilityVoid (composites)Particle sizeComposite materialChemistryPhysics

Abstract

fetched live from OpenAlex

Abstract A three-dimensional, time-dependent numerical model of free-surface flows and heat transfer including phase change has been used to simulate the impact of a liquid droplet on a solid particle and to predict the size of the void under the solid particle caused by incomplete filling by liquid landing on top of it. The solid particle was considered to be a protrusion on the substrate. Fluid flow in the impacting liquid droplet was modeled using a finite difference solution of the Navier-Stokes equations in a 3D Cartesian coordinates assuming laminar, incompressible flow. Heat transfer in the liquid droplet was modeled by solving the energy equation, assuming densities of liquid and solid to be constant and equal to each other. The free surface of the liquid droplet was assumed to be adiabatic. The porosity in this simulation was defined as the volume of the incompletely filled void under the solid particle to the volume of the solid particle. The simulation was repeated with different process parameters, and the results showed that process parameters play significant roles in determining the amount of porosity. A correlation is found to express the porosity as a function of the process parameters.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.231
Threshold uncertainty score0.469

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 head0.010
GPT teacher head0.212
Teacher spread0.202 · 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 designBench or experimental
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

Citations0
Published2006
Admission routes1
Has abstractyes

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