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Record W2605208980 · doi:10.31399/asm.cp.itsc2013p0292

Effect of Liquid Feedstock on Downstream Cold Spray Nozzle

2013· article· en· W2605208980 on OpenAlexaff
E. Farvardin, Munawar Nawab Karimi, Julio Villafuerte, Ali Dolatabadi

Bibliographic record

VenueThermal spray · 2013
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of WindsorCenterLine (Canada)Concordia University
Fundersnot available
KeywordsRaw materialNozzleDownstream (manufacturing)Spray nozzleEnvironmental scienceWaste managementChemistryMaterials scienceEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract In this study, a downstream injection cold spray nozzle is modeled numerically under various loadings. Instead of micron-sized particles, liquid feedstock as a carrier of nanoparticle suspension is fed into the nozzle through a port located 6 mm downstream of the nozzle throat at the diverging section. Water is used as the liquid carrier with a droplet size distribution of 5-100 µm and liquid-to-gas ratio ranging from 5 to 15%. The radial injection of droplets is simulated by Lagrangian particle tracking which includes the effects of heating and evaporation. The effect of the feedstock on downstream flow is analyzed and the optimum solid-to-liquid fraction in the suspension is determined.

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 categoriesInsufficient payload (model declined to judge)
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 score0.032
Threshold uncertainty score1.000

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)0.0010.001

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 head0.003
GPT teacher head0.188
Teacher spread0.185 · 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.

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
Published2013
Admission routes1
Has abstractyes

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