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Record W4205856149 · doi:10.21272/jes.2021.8(2).f2

Dimet Laval Nozzle Expansion Section Analysis and Optimization

2021· article· en· W4205856149 on OpenAlexaboutno aff
Han Jie, Sergii Markovych, Y. Wang

Bibliographic record

VenueJournal of Engineering Sciences · 2021
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsnot available
FundersChina Scholarship Council
KeywordsNozzleMaterials scienceDischarge coefficientRocket engine nozzleMechanicsCoatingDeposition (geology)AccelerationSpray nozzleCross section (physics)InletSection (typography)Composite materialMechanical engineeringEngineeringPhysicsGeologyComputer scienceClassical mechanics

Abstract

fetched live from OpenAlex

The cold spray technology mainly accelerates the powder in the Laval nozzle by gas, ensuring that the powder has a greater velocity at the exit of the Laval nozzle, and achieving high-efficiency deposition on the substrate, thereby obtaining a better performance of the deposition coating. The article uses numerical simulation to study the influence of the length of the expansion section of the Dimet Laval nozzle on the acceleration effect of Al powder. The results show that the length of the expansion section of the nozzle is an essential factor affecting the velocity of the Al powder at the nozzle outlet. Through analysis, it can be known that the pressure inlet range of the Dimet Laval nozzle is 1.0 MPa, and the length of the expansion section is about 210 mm, which can ensure that the Al powder has a better acceleration effect in the nozzle and has a better velocity at the nozzle outlet. It is recommended that the joints between the small sections of the nozzle expansion section should be kept as smooth as possible so that the accelerating effect of the accelerating gas on the Al powder is more uniform and stable.

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 score0.238
Threshold uncertainty score0.249

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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