Numerical Simulation of Non-Spherical Submicron Particle Acceleration and Focusing in a Converging–Diverging Micronozzle
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".