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Record W2954828142 · doi:10.1108/hff-10-2018-0553

Numerical assessment of miniaturized cold spray nozzle for additive manufacturing

2019· article· en· W2954828142 on OpenAlexaffabout
Gus Nasif, R. M. Barron, Ram Balachandar, Julio Villafuerte

Bibliographic record

VenueInternational Journal of Numerical Methods for Heat &amp Fluid Flow · 2019
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsCenterLine (Canada)University of Windsor
Fundersnot available
KeywordsNozzleMechanicsDischarge coefficientMach numberTurbulenceHeat transferCompressible flowReynolds-averaged Navier–Stokes equationsTurbulence kinetic energyCompressibilityMaterials scienceFinite volume methodMechanical engineeringThermodynamicsPhysicsEngineering

Abstract

fetched live from OpenAlex

Purpose Application of cold spray technology may exhibit significant benefits for the additive manufacturing process, particularly for producing intricate objects. To ascertain the feasibility of such an application, this paper aims to present a numerical investigation of the effect of scaling down a convergent-divergent (de Laval) nozzle, which is typically used in the cold spray industry, on the compressible flow parameters and thermal characteristics. Design/methodology/approach The Navier–Stokes equations and energy equation governing compressible flow are numerically solved using a finite volume method with a coupled solver. The conjugate heat transfer technique is used to couple fluid and solid heat transfer domains and predict the local heat transfer coefficient between the solid and fluid. The use of various RANS turbulence models has also been investigated to quantify the effect of the turbulence model on the simulation. Findings The numerical results reveal that the flow and thermal characteristics are altered as the convergent-divergent nozzle is scaled down. The static pressure and temperature profiles at any section in the nozzle are shifted toward higher values, while the Mach number profile at any section in the nozzle is shifted toward a lower Mach number. The turbulent kinetic energy at the nozzle exit increases with the scaling down of the nozzle geometry. This study also provides convincing evidence that the adiabatic approach is still suitable even though the temperature of the nozzle wall is extremely high, as required for industrial application. Results indicate that it is feasible to use the available capabilities of the cold spray technology for additive manufacturing after scaling down the nozzle. Originality/value The idea of adopting cold spray technology for additive manufacturing is new and innovative. To develop this idea into a viable commercial product, a thorough understanding of the flow physics within a cold spray nozzle is required. The simulation results discussed in this paper demonstrate the effect that scaling down of a convergent-divergent nozzle has on the flow characteristics in the nozzle.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.375
Teacher spread0.354 · 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

Citations6
Published2019
Admission routes2
Has abstractyes

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