Study on the adhesion behaviour of oil droplets in water on solid surfaces with different wettability and inclination
Bibliographic record
Abstract
Abstract Microscopic adhesion of oil droplets is a hot research topic within multiphase flow systems in the petrochemical field in recent years, and it is also the key to revealing the mechanism of oil droplet–wall interaction. In this paper, a high‐speed camera was used to capture the dynamic process of oil droplets impacting stainless steel, polyethylene, brass, and other materials in water, deeply analyze the influence mechanism of material wettability, droplet size, and inclination on adhesion behaviour in this process, and draw the Re~ θ eq phase diagram. The experimental results show that properly enhancing the lipophilicity of the material can prolong the drainage time and effectively inhibit adhesion. Too much lipophilicity can dramatically shorten the drainage time instead, which easily leads to the rapid adhesion of oil droplets. An increase in droplet size can also prolong the drainage time; at Re ≥46.31, the excessive initial kinetic energy is consumed in the form of oil droplet rebound, which is not conducive to adhesion, and the more lipophilic the material is, the more significant the rebound effect. In addition, although an increase in the inclination can inhibit oil droplet bounce off, it also decreases the wall restraint to oil droplets, resulting in lateral and normal displacements, which greatly increases the drainage time. The findings can facilitate the understanding of oil droplet–wall interaction and provide a scientific basis for the design and development of efficient separation equipment and the optimization of the low‐temperature gathering and transportation of high water‐bearing crude oil.
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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".