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Record W2259046171

저온분사 코팅공정에서 초음속 슬릿노즐 사용시 유동장 해석

2011· article· ko· W2259046171 on OpenAlexaboutno aff
박혜영, 박종인, 정훈제, 장경수, 백의현, 한정환, Hyung Jun Kim

Bibliographic record

Venue대한금속재료학회지 · 2011
Typearticle
Languageko
FieldEngineering
TopicEngineering Applied Research
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleCoatingDischarge coefficientMaterials scienceSpray nozzleMechanicsSupersonic speedGas dynamic cold spraySpray characteristicsParticle (ecology)SlitTuyereParticle velocityComposite materialMechanical engineeringOpticsMetallurgyEngineeringPhysics
DOInot available

Abstract

fetched live from OpenAlex

The cold spray process is an emerging technology that utilizes high velocity metallic particles for surface coating. Metallic powder particles are injected into a converging-diverging de Laval nozzle and accelerated to a high velocity by a supersonic gas flow. The cold spray process normally uses a circular nozzle that has a rather narrow spraying range. To overcome this fault, a slit nozzle was considered in this study. The slit nozzle is anticipated to reduce the coating process time because it has a wider coating width than the circular nozzle. However, the slit nozzle can reduce the coating efficiency because it does not allow as much gas and particle velocity as the circular nozzle. To improve the coating efficiency of a slit nozzle, the shape of the slit nozzle was modified. And the results of gas flow and particle behaviour according to the nozzlers shape were compared by the a numerical analysis. As a results, as Expansion Ratio(ER) of 7.5 was found to be the most optimal condition for enhancing the spraying efficiency when the ER was changed by the variation of nozzle neck and exit size.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.032
GPT teacher head0.227
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2011
Admission routes1
Has abstractyes

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