CFD simulation of the behavior of droplet rising in water column with the effect of surfactant
Bibliographic record
Abstract
Surfactant decreases the oil-water interfacial tension and thus oil droplets breakup into smaller ones and are dispersed into the water column. This paper presents the analysis of surfactant (premixed with oil droplet and bulk separately) on the oil droplet behavior in the water column. We implemented additional equations to account for the oil-water interfacial tension due to the presence of surfactant and those equations were incorporated into FLUENT by using UDF (User Defined Function). The streamlines around the oil droplet show major recirculation both inside and outside the oil droplet. It is noticed that the surfactant concentration is higher at the bottom and edges of oil droplet due to the shear effect. The presence of surfactant affects the deformation process of oil droplets of different diameters: Oil droplet with larger diameter (e.g., 4 mm in diameter) deforms into flat shape and breaks up. Tip-streaming is noticed and the droplet continue to rise up in jelly-fish like shape. However, much smaller droplets (e.g., 20 μm in diameter) only deform into oval shape and do not continue to breakup under the condition of same initial surfactant concentration. The presence of surfactant also deceases the rising speed of oil droplet compared to the terminal velocity of clean oil droplet. The results may be valuable to help us to apply surfactant to oil spill in a more efficient way.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".