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Record W2291850472 · doi:10.1115/ht2005-72372

The Effects of the Distance Between Nozzle and Substrate on Cold Gas Dynamic Spray Process

2005· article· en· W2291850472 on OpenAlexaboutno aff
Longjian Li, Wenzhi Cui, Qinghua Chen, Tien‐Chien Jen, Quan Liao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsnot available
Fundersnot available
KeywordsGas dynamic cold sprayNozzleMaterials scienceHeliumShock waveAccelerationParticle (ecology)Substrate (aquarium)Volumetric flow rateMechanicsAtomic physicsComposite materialThermodynamicsPhysicsCoating

Abstract

fetched live from OpenAlex

In this paper, numerical simulations were performed for the gas-particle two phase flow in the cold gas dynamic spray process to investigate the acceleration of micro- and nanoparticles with diameters ranging from 100nm to 50μm. Nitrogen (N2) and Helium (He) were chosen as the carrier gas, respectively. The acceleration of carrier gas to particles in the De-Laval-Type supersonic nozzle was strongly dependent on the characteristics of flow field, as well as the densities and the size of the particles. Two kind of particles Copper (Cu) and Platinum (Pt) were chosen as the spraying materials. The computed results showed that the flow structures of the carrier gas were very different for different gas and different spraying distance, which resulted in consequently different accelerating features. The cone-shape weak shocks (compression waves) occurred at the exit of divergent section, and the bow-shaped strong shock wave was found right before the substrate, which played a resistance role to the particles and prevented the smaller particles from approaching on the substrate.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.256

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 head0.003
GPT teacher head0.212
Teacher spread0.208 · 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 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
Published2005
Admission routes1
Has abstractyes

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