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Record W4407370379 · doi:10.3390/coatings15020215

In-Flight Particle Oxidation Evolution in HVAF: A Numerical Study

2025· article· en· W4407370379 on OpenAlexafffund
S. Diop, Aleksandra Nastic, Ali Dolatabadi, Reza Attarzadeh, Christian Moreau

Bibliographic record

VenueCoatings · 2025
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsUniversity of TorontoConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaConsortium de Recherche et d’innovation en Aérospatiale au QuébecMcGill University
KeywordsParticle (ecology)Environmental scienceMechanicsPhysicsGeologyOceanography

Abstract

fetched live from OpenAlex

Oxygen present in the High Velocity Air-Fuel (HVAF) process can react with the in-flight metallic particles and cause their oxidation. A grown brittle oxide shell on metallic micro-size particles can reduce their deposition efficiency and impair the coating’s final deposited properties/microstructure. In the current study, the oxide growth of MCrAlY particles, where M stands for Nickel (Ni) and Cobalt (Co), during their flight in the HVAF process has been numerically modeled and validated with experimental single-particle depositions. A thorough theoretical oxide layer growth background is also presented. The utilized oxidation development follows the Mott–Cabrera theory for very thin films, which uses the particle surrounding temperature and oxygen partial pressure to track and describe the oxide growth. The obtained results provide a good correlation between the HVAF system design, the operating conditions, and surface oxidation phenomena observed using focus ion beam scanning electron microscope (FIB/SEM) analysis on collected particles. Furthermore, the particle’s degree of oxidation in HVAF is compared to High Velocity Oxy-Fuel (HVOF) to demonstrate the influence of combustion processes on oxidation level.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.251
Teacher spread0.244 · 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 designObservational
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
Published2025
Admission routes2
Has abstractyes

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