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Numerical simulation study of nozzle flow field during cold spraying

2025· article· en· W4407768592 on OpenAlexaboutno aff
Xinrui Wang, Kai Yang, Wenzheng Zhou, Zhihao Xia

Bibliographic record

VenueJournal of Physics Conference Series · 2025
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleMechanicsGas dynamic cold sprayComputer simulationField (mathematics)Materials scienceFlow (mathematics)Environmental sciencePhysicsAerospace engineeringEngineeringMathematicsComposite material

Abstract

fetched live from OpenAlex

Abstract Cold spraying is a high-velocity solid-state coating technology. The supersonic jet flow field, accelerated within the cold spraying nozzle, directly affects the coating results, making the study of the nozzle’s internal flow field crucial. This thesis employs Fluent software to establish a finite element simulation model of the gas-solid flow field within the Laval nozzle in cold spraying. The dynamic simulation analyzes the changes in the flow field of nitrogen gas and 7075 aluminum alloy particles inside the nozzle. The simulation results reveal that the gas phase velocity continuously increases within the nozzle; the turbulent kinetic energy of the gas initially increases and then decreases within the nozzle; the relationship between the spraying velocities of the gas and particles of different sizes within the nozzle is also determined.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.481

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 head0.014
GPT teacher head0.264
Teacher spread0.250 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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