Effect of Gas-Liquid Ratio on Droplets Centricity and Velocity of an Effervescent Atomizer
Bibliographic record
Abstract
Effervescent atomization is a twin fluid atomization mechanism which is based on the injection of gas into a liquid stream at an upstream location of the exit nozzle. This atomization mechanism is capable of producing droplets with Sauter Mean Diameter (SMD) comparable to other types of atomizers but at lower injection pressures. For the conditions investigated in this paper, liquid at the nozzle exit is an annular film surrounding the gas phase. A shadowgraph system is used to visualize particles shape and study droplets evolution in the spray field. Measurement of droplet SMD and centricity has been conducted at several axial and radial locations for different Gas-Liquid Ratios (GLRs). The shadowgraphy images reveal some non-spherical droplets which consist of both elliptical and coalescing droplets. Results also demonstrate that higher numbers of non-spherical droplets are observed at the near nozzle region and at higher liquid flow rate. In this work, spatial structure of the liquid phase velocity field has also been studied using a StereoPIV technique. The velocity field from StereoPIV measurements has been compared with the shadowgraphy velocity results averaged over different droplet size classes. This comparison has been conducted for the atomizer operating at different GLRs. Comparison of the results demonstrates that at the far-field, StereoPIV velocity field measurement is biased toward the velocity of droplets size classes which have relatively higher probability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".