EFFERVESCENT SUSPENSION SPRAY IN A GASEOUS CROSSFLOW
Bibliographic record
Abstract
A spray of suspension, forms mainly solid particles in a liquid phase from the atomization of two or multiphase flow, mainly solid particles in a liquid phase, and its transport phenomena by a gaseous crossflow have many natural and industrial applications. For example, injection of suspension jet in a high-speed flow is used in the emerging surface engineering process called suspension plasma spray. Typically, submicron ceramic oxide particles are mixed with water or ethanol to form a suspension that is injected in a plasma plume using different types of injectors. Injection parameters such as the type of injector and momentum flux influence the size, velocity, and trajectory of suspension droplets in the plasma and the microstructure of the deposited coatings. Using an effervescent atomizer, due to its capability in transporting flows with various rheological properties is promising for injection of suspension into the gaseous crossflow. In this study, an effervescent atomizer was employed to introduce a suspension radially into the flow of gas at room temperature. The spray of suspensions with different concentrations of glass particles in water was investigated in the crossflow by phase Doppler particle analyzer. The results were validated and supported by studying the spray by shadowgraph and light diffraction techniques. The results of this study provide a better understanding of the suspension spray generated by an effervescent atomizer in a crossflow configuration. It was found that the solid concentration of the suspension (up 10 wt.%) causes a slight decrease in size and brings the penetration of the suspension droplets in the gas flow.
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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 | 0.000 |
| 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 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".