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Record W2172474815 · doi:10.1115/fedsm2007-37283

Dense Particulate Flow in a Cold Gas Dynamic Spray System

2007· article· en· W2172474815 on OpenAlexaff
B. Samareh, Ali Dolatabadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsConcordia University
Fundersnot available
KeywordsMechanicsGas dynamic cold sprayParticle (ecology)NozzleMaterials scienceParticle velocityDeposition (geology)Shock waveVolume fractionVolume (thermodynamics)Compression (physics)Shock (circulatory)Flow (mathematics)Substrate (aquarium)ThermodynamicsComposite materialPhysicsGeology

Abstract

fetched live from OpenAlex

The effect of particle-gas and particle-particle interactions in a cold spray process is studied when the particle loading is high. To examine the effect of the presence of a dense particulate flow on the supersonic gas, an Eulerian-Eulerian approach is used. It is found that when the volume fraction of the injected particles is increased, the turbulence of the gas phase will be augmented by the motion of particles and consequently, the shape, strength and location of the compression and expansion waves will be altered. Shock-particle interactions are demonstrated for various volume fractions. Another important parameter which will affect the spraying deposition efficiency is the substrate stand-off distance. It is found that the stagnation pressure alternates for different stand-off distances because of the formation of compression and expansion waves outside the nozzle exit. Particle normal velocity upon impact is a strong function of the stagnation pressure on the substrate as particles must pierce through the bow shock formed on that region. The substrate stand-off distance can be optimized to provide minimal pressure build-up on the substrate. This will result in a higher normal velocity upon impact and thus a higher deposition efficiency.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.005
GPT teacher head0.211
Teacher spread0.206 · 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

Citations2
Published2007
Admission routes1
Has abstractyes

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