Computational Investigation of Liquid Spray Dispersion Modification by Conical Nozzle Attachments
Bibliographic record
Abstract
Liquid spray characteristics such as the droplet size and dispersion angle are determined by the atomizer design and the physical properties of the liquid and surrounding gas. One of the options to change these characteristics is to attach an especially designed piece to the nozzle exit. While these attachments can have a variety of shapes, we chose a conical geometry to exploit its axial symmetry and, at the same time, obtain the results that can be generalized to other configurations. Thus, we investigate an addition of the conically shaped attachment to the premixed gas-assisted high-pressure atomizer with the previously developed numerical model. This is a two-fluid Eulerian-Eulerian model with a catastrophic phase inversion that was developed for compressible gas-liquid mixtures and can be applied to both the flow through the nozzle-atomizer and to the dispersion of the spray. The model also accounts for the break-up and coalescence effects of bubbles and droplets. Our investigation reveals that the conical nozzle attachments act as spray limiters by reducing the natural expansion angle of a spray. Also, the droplets produced by the nozzle with a conical addition tend to be larger than the ones obtained with a stand alone nozzle. The largest droplets are generated by the smallest attachment angle considered, 10 deg. With the increase of the angle, the spraying characteristics become closer to those of the stand alone nozzle. It can be concluded that the conical shape of the attachments with a relatively small angle may be used when higher jet penetration and lower dispersion are desirable. The attachments with larger angles do not offer a substantial difference from the stand alone nozzle. Another important conclusion is that the dispersion of the jet is determined by the radial momentum transferred to the liquid before or immediately after the phase inversion takes place. Thus, for improved dispersion, the area where the atomization is taking place should not be restricted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".