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Record W2807223375 · doi:10.31399/asm.cp.itsc2018p0286

Numerical Study Using Nano-Particles by Varying the Expansion Ratio and Height of Cold Spray Nozzle to Predict Deposition Efficiency

2018· article· en· W2807223375 on OpenAlexaboutno aff
Chirag Singhal, Qasim Murtaza

Bibliographic record

VenueThermal spray · 2018
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceNozzleGas dynamic cold sprayDeposition (geology)Spray nozzleCoatingComposite materialMetallurgyThermal sprayingPassivationSubstrate (aquarium)NanoparticleLayer (electronics)NanotechnologyMechanical engineering

Abstract

fetched live from OpenAlex

Abstract The coatings of zinc and its alloys are broadly used to prevent the rusting of substrate surfaces such as steel. Cold gas dynamic spray (CGDS) is an innovative coating technique in which the deposition of solid powder particles depends upon the kinetic energy of the particles rather than thermal energy. Therefore, application of cold spray is to provide superior rust resistance by depositing more materials, formation of passivation layer, and cathodic protection. In this study, numerical investigations on zinc micro and nano size particles in CGDS were carried out. The height of the injector, the expansion ratio and the diameter of the inlet of the de-Laval nozzle was varied systematically by optimizing the stand-off distance using the two-dimensional axisymmetric models of CGDS, to study their effects on the velocity and the distribution of the particles. Prediction of the deposition efficiency was carried out using the various critical and erosion velocity models.

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 score0.004
Threshold uncertainty score0.461

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 head0.011
GPT teacher head0.241
Teacher spread0.230 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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