Behavior of small water droplets in a highly viscous flow in a converging and diverging channel
Bibliographic record
Abstract
Understanding the evolution of water droplets moving in a highly viscous bulk flow (e.g., bitumen) has attracted increasing attention in the context of numerous separation technologies due to various issues relating to the environment (re-use of water) and engineering failures (corrosion of pipelines). With this in mind, the main objectives of this work are to explore the dynamics of water droplets with a diameter of seven micrometers, moving in highly viscous bitumen flowing through a smoothly converging and diverging 11-micron channel using three-dimensional (3D) and two-dimensional (2D) droplet-resolved simulations and to adjust an existing population balance model (PBM) to predict geometry-driven coalescence for different flow rates. The Eulerian–Eulerian (EE) method coupled with a new PBM is used to predict the behavior of water droplets with a diameter of 7 μm. Numerical simulations were carried out for various capillary numbers (0.1<Ca<3) and compared with the volume of fluid method combined with the level-set function (CLSVOF). Adaptive mesh refinement (up to six levels) was used in 3D and 2D CLSVOF simulations, producing interface cells measuring up to 30 nm. Good agreement was observed between EE-PBM and CLSVOF models. For comparison, we show the results of 2D CLSVOF simulations. This new PBM model can be used to predict water–oil separation in new cascade-formed geometries to enhance the coalescence of water droplets in highly viscous bulk flows.
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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 | 0.001 |
| 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".