Average Droplet Diameter Spatial Distribution in a Spray After Gas-Assisted Atomization
Bibliographic record
Abstract
Gas-assisted atomization is used in many industries to produce finely dispersed droplets. Knowledge about the droplet size distribution guides the design and development of the nozzle as well as of the whole processes. This knowledge can be obtained through experimental and modeling works that usually complement each another. In this paper we present an application of the mathematical model previously developed for gas-assisted atomization to the prediction of the average droplet diameter distribution in a spray and compare the results with experimental findings. The model is based on a two-fluid Eulerian-Eulerian treatment of the motion of the phases with a catastrophic phase inversion (atomization). It also includes the compressibility effects for the gaseous phase and can be applied to both the flow through the nozzle-atomizer and to the dispersion of the spray. The model accounts for the break-up and coalescence of bubbles and droplets due to interfacial shear and collisions. The diameter of the particle (bubble or droplet) is represented by its local mean average value that varies throughout the flow field. Simulations are conducted for the flow of air and water through the convergent-divergent nozzle, which is similar to the one used in commercial fluid cokers, a bitumen upgrading apparatus, for steam-assisted atomization of bitumen. It is found that while there are wide experimentally observed local distributions of the particle diameter, the concept of the average diameter still allows for satisfactory predictions of its average values and spatial variations. In agreement with the experiments, the numerical model demonstrated that the largest droplet diameter is located in the axial area, and the diameter values reduce towards the periphery of the jet. In addition, the average diameter increases slightly and its radial variation becomes more uniform as the distance from the nozzle orifice increases.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".