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Record W4313825349 · doi:10.31399/asm.cp.itsc2012p0237

Gas Substrate Heat Exchange during Cold-Gas Dynamic Spraying

2012· article· en· W4313825349 on OpenAlexaff
A. McDonald, A. N. Ryabinin, Éric Irissou, J.-G. Legoux

Bibliographic record

VenueThermal spray · 2012
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsNational Research Council CanadaUniversity of Alberta
Fundersnot available
KeywordsJet (fluid)Materials scienceNozzleSubstrate (aquarium)Gas dynamic cold sprayRADIUSMechanicsThermalNitrogen gasCompressed airThermal conductivityComposite materialNitrogenThermodynamicsChemistry

Abstract

fetched live from OpenAlex

Abstract In this study, the temperature distribution of the surfaces of several substrates under an impinging gas jet from a cold spray nozzle was determined. A low-pressure cold-gas dynamic spraying unit was used to generate a jet of hot compressed nitrogen that impinged upon flat substrates. Computer codes based on a finite differences method were used to solve a simplified 2-D temperature distribution equation for the substrate to produce non-dimensional relationships between the surface temperature and the radius of the impinging fluid jet, the substrate thickness, and the heating time. It was found that a single profile of the transient non-dimensional maximum surface temperature could be used to estimate the dimensional maximum surface temperature, regardless of the value of the compressed gas temperature. It was found further that as the thermal conductance of the substrate increased, the maximum surface temperature of the substrate beneath the gas jet decreased. The close agreement of the numerical results with the experimental results suggests that the non-dimensionalized results may be applied to a wide range of conditions and materials.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
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)0.0010.000

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 head0.009
GPT teacher head0.202
Teacher spread0.193 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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