Simulation of collision behaviour between droplet and solid surface in liquid phase environment
Bibliographic record
Abstract
Abstract Processes involving dispersed immiscible fluids occur across the traditional onshore and offshore crude oil development phases, including oil exploitation and production, gathering and transportation, and station and field processing. The stabilities of such processes are deeply influenced by the collision and film drainage between separated phase interfaces. Due to factors such as gravity as well as buoyancy and resistance, droplets usually collide with the wall at a relatively high speed and experience one or several rebounds before gradually stabilizing and staying on the surface. Though work related to this aspect has raised significant attention, further research on the force‐deformation behaviour between microsized ( r ≈50 μm) droplets and solid surface under the influence of the above factors is still lacking and strongly demanded. In this paper, under the comprehensive influence of flow driving, an emulsified water droplet colliding with a solid surface in silicon oil is modelled. The dynamic behaviour of the droplet was analyzed by coupling the Stokes–Reynolds drainage equation, the generalized Young–Laplace equation, and the force situation of the deformable droplet. Furthermore, the effects of interfacial tension, collision velocity, and viscosity of continuous phase on the collision characteristics of droplet were analyzed, and it was found that these factors could significantly change the collision characteristics of droplet.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".