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Record W2053676790 · doi:10.1115/ht2012-58252

Simulation of Fluid Flow and Heat Transfer Including Phase Change During the Impact of Semi-Molten Particles

2012· article· en· W2053676790 on OpenAlexaff
Sina Alavi, Mohammad Passandideh‐Fard, J. Mostaghimi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVolume of fluid methodHeat transferMaterials scienceMechanicsParticle (ecology)Deposition (geology)ViscosityCore (optical fiber)Computational fluid dynamicsThermodynamicsFluid dynamicsFlow (mathematics)Composite materialPhysics

Abstract

fetched live from OpenAlex

A numerical method is proposed in this paper which is capable of modeling the impact and deposition of semi-molten particles. By attributing a high viscosity to the solid region, simulating the motion of solid cores including solidification/melting is made possible inside the particle. The Navier-Stokes equations are solved in combination with the Volume-of-Fluid (VOF) technique to track the free surface of the particles. In addition, the heat transfer including phase change is modeled using the enthalpy method. The viscous terms are treated implicitly to relax the restrictions associated with small computational time-steps in explicit formulations. Several case studies with operating conditions of a typical thermal spray process are simulated. These cases include the impact of molten and semi-molten nickel particles in an atmospheric plasma spray process (APS). The effects of various parameters such as particle solid-core diameter, initial velocity and temperature are investigated. The simulations show that the size of the solid core has an important effect on the amount of particle splashing during the impact.

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 score0.429
Threshold uncertainty score0.263

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 head0.037
GPT teacher head0.289
Teacher spread0.253 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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