CFD Simulation of the Interaction Between a Macrobubble and a Dilute Dispersion of Oil Droplets in Quiescent Water
Bibliographic record
Abstract
Wastewater generation is a growing concern in the preliminary treatment of heavy crude oil and tar sand. The separation of fine oil droplets from water by flotation is a critical process in the production of bitumen from tar sand. The flow structure from a high-resolution simulation of a single air macrobubble (>3 mm diameter) rising through water in the presence of a very dilute dispersion of mono-sized oil microdroplets (30 μm) under quiescent conditions is presented. A combined model of computational fluid dynamics (CFD), a volume of fluid (VOF) multiphase approach, and the discrete phase method (DPM) was developed to simulate bubble dynamics, the trajectories of the dispersed oil droplet, and the interaction between the air bubble and the oil droplet in quiescent water. The CFD–VOF–DPM combined model reproduced the interacting dynamics of the bubble and oil droplets in water at the bubble–droplet scale. With an extremely large diameter ratio between the bubble and the dispersed oil droplet, this model clearly demonstrated that the dominant mechanism for the interaction was the hydrodynamic capture of oil droplets in the wake of a rising air macrobubble. The entrainment of the oil droplets into the wake of the rising bubbles was strongly influenced by the bubble’s shape.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".