Bibliographic record
Abstract
Atomization of suspension solutions has a wide range of applications in many industrial processes. The application ranges from spray drug products, such as nasal spray and inhalation solution to spray drying, plasma spraying and coatings. In any atomization process the main spray characteristics are defined as drop size and velocity distributions, the spray path and the distribution of the liquid throughout the spray. These distributions are affected by several parameters including liquid and gas physical properties, nozzle geometry and operating conditions [1]. Thus understanding the behavior of gas and liquid flow through the nozzle is crucial to predict the thickness and momentum of the liquid flow at the outlet of the atomizer. In this work the Multi-Fluid Marker and Cell (MFMAC) technique is used to numerically model the structure of internal two-phase flow inside an aerated-liquid jet. The behavior of liquid film in the discharge passage was investigated using different Gas to Liquid mass Ratio (GLR) and these numerical results were compared with the available experimental data. This work was done as a baseline to validate the simulations and the effect of suspension solid particles on the structure of the two-phase flow at the exit cross section of the nozzle was also studied. The effect of concentration of solid particles on the performance of the atomizer is considered through change in the liquid bulk density and viscosity. By increasing in the amount of aerating gas, the liquid film formed in the discharge orifice, becomes thinner and the gas/liquid velocity ratio and momentum flux at the nozzle exit increases. It was found that the variation of solid particle concentration can have an influence on the internal flow characteristics such as the liquid thickness and the flow pattern inside the nozzle discharge passage.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".