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Record W4289275047 · doi:10.1615/ichmt.2022.conv22.110

Supersonic Nitrogen and Helium Jet Impingement on a Flat Stationary Surface

2022· article· en· W4289275047 on OpenAlexaboutno aff
Joseph M. Conahan, Ozan Ç. Özdemir, M. E. Taslim, Sinan Müftü

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsNusselt numberReynolds numberSupersonic speedMaterials scienceJet (fluid)NozzleMechanicsComposite materialTurbulenceThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Millimeter scale de Laval supersonic nozzles are used in cold spray additive manufacturing (CSAM) for spraying metal powder particles (5-100 µm) on a target surface with impact velocities of 300-1,500 m/s in their solid state. The kinetic impact energy allows particles to plastically deform and adhere to the target surface metallurgically and mechanically, which enables rapid repair of surfaces, deposition of coatings, and additive manufacturing of mechanical components. In CSAM processes, thermal history has significant influence on the final mechanical properties and the stress state of deposited materials with the largest source of thermal energy coming from the supersonic impinging jet with Reynolds numbers on the order 105-106. In this work, a parametric study was performed using CFD and an experimental apparatus for understanding the heat transfer phenomena between flat surfaces and millimeter scale supersonic jets impinging on the surfaces with Reynolds numbers reaching 2×106. An empirical relationship was then developed for estimating area averaged Nusselt numbers for Reynolds numbers ranging between 1×105 and 2×106 and for radii five to fifteen times that of the jet diameter. Within the bounds of the study, the nozzle to substrate standoff distance was observed to have little to no effect on the Nusselt number. Although developed for CSAM, the empirical correlations apply to other fields beyond CSAM.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.751

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 head0.008
GPT teacher head0.191
Teacher spread0.183 · 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
Published2022
Admission routes1
Has abstractyes

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