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Record W2414682215 · doi:10.36884/jafm.7.01.19575

Parametric Study of Exhaust Pattern in Cold Spray Using CFD and Particle-Wall Impact Analysis

2014· article· en· W2414682215 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Applied Fluid Mechanics · 2014
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputational fluid dynamicsParametric statisticsMechanicsMaterials scienceParticle (ecology)PhysicsGeologyMathematics

Abstract

fetched live from OpenAlex

A numerical simulation of a cold gas dynamic spray process using a computational fluid dynamic (CFD) technique is presented. Distribution of particulate matter in the immediate surroundings of spray application site is of interest. The flow field inside an oval shaped supersonic nozzle and the surroundings of the nozzle is simulated. Particle trajectories along their flight in the nozzle as well as before and after impact with the target plane are calculated. Fluent is used for the purpose of flow field simulation. A discrete-phase Lagrangian particle trajectory model is used for particle trajectory calculation. A model uses the principles of motion and impact dynamics to predict particle behavior upon impacting the substrate. The locations and concentrations of particle exhaust patterns around the impact location are determined and presented graphically. The dependence of these patterns to variations in the jet-target tilting angle, standoff distance, upstream temperature and particle material is investigated.

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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.014
GPT teacher head0.252
Teacher spread0.238 · 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