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Design of Ultrasonic Ejecting Gun Used for Surface Nanocrystallization Based on CFD

2008· article· en· W1977384287 on OpenAlex
X.M. Wang, Shi Ning, Cheng-Yeh Li, Jia Wu He, Xi‐Qiao Feng

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueKey engineering materials · 2008
Typearticle
Languageen
FieldEngineering
TopicSurface Treatment and Residual Stress
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleComputational fluid dynamicsMaterials scienceFluentInletSupersonic speedMechanical engineeringMechanicsMuzzleComposite materialStructural engineeringEngineeringPhysicsBarrel (horology)

Abstract

fetched live from OpenAlex

Supersonic fine particles bombarding (SFPB) is an important way to perform surface nanocrystallization. Harder the material is, higher the bombarding particles speed is needed. The gun with excellent features is premise to obtain the nano-structured layer.This paper analysed the advantages of rectangle Laval nozzle compared to annular-shape one and calculated critical structured parameters of the nozzle.Numerical simulation analysis of flow field of the nozzle at inlet temperature 300K and inlet pressure 0.25 MP, 1.0 MP, 1.7 MP and 2.5MP and velocity field of the gun with divergent-angle extended-barrel conduced by a commercial finite volume code FLUENT software of CFD; Consequently, the overall structure of the gun was optimized and determined finally. The sample of 38CrSi steel was treated by this gun. And the nano-structure layer on the surface was observed by TEM.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.865

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)0.0000.000

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 head0.021
GPT teacher head0.208
Teacher spread0.187 · 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