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Record W4206072296 · doi:10.3390/app12010343

Numerical Simulation of Non-Spherical Submicron Particle Acceleration and Focusing in a Converging–Diverging Micronozzle

2021· article· en· W4206072296 on OpenAlexaboutno aff
Yanru Wang, Jiaxin Shen, Zhaoqin Yin, Fubing Bao

Bibliographic record

VenueApplied Sciences · 2021
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsnot available
Fundersnot available
KeywordsParticle (ecology)MechanicsNozzleAerodynamicsAccelerationMaterials scienceAerospace engineeringPhysicsClassical mechanicsMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Submicron particles transported by a Laval-type micronozzle are widely used in micro- and nano-electromechanical systems for the aerodynamic scheme of particle acceleration and focusing. In this paper, the Euler–Lagrangian method is utilized to numerically study non-spherical submicron particle diffusion in a converging–diverging micronozzle flow field. The influence of particle density and shape factor on the focusing process is discussed. The numerical simulation shows how submicron particle transporting with varying shape factors and particle density results in different particle velocities, trajectories and focusing in a micronozzle flow field. The particle with a larger shape factor or larger density exhibits a stronger aerodynamic focusing effect in a supersonic flow field through the nozzle. In the intersection process, as the particle size increases, the position of the particle trajectory intersection moves towards the throat at first and then it moves towards the nozzle outlet. Moreover, the influence of the thermophoretic force of the submicron particle on the aerodynamic focusing can be ignored. The results will be beneficial in technological applications, such as micro-thrusters, microfabrication and micro cold spray.

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 score0.290
Threshold uncertainty score0.332

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 head0.017
GPT teacher head0.256
Teacher spread0.239 · 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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