Effect of surfactant on the dynamics of a crude oil droplet in water column: Experimental and numerical investigation
Bibliographic record
Abstract
Abstract In this study we have investigated the effect of surfactant, sodium dodecyl sulfate (SDS), on the dynamics of a single crude oil droplet, rising in a quiescent water column. Experiments were conducted in a tank, in which an oil droplet was released into a stagnant water column through a nozzle. The droplets ranging from 0.3 to 0.85 cm were produced from three different sized nozzles. The shape adopted by the emanating droplets varied from spherical to oblate. SDS concentrations were varied from 0 to 750 ppm in water. The adsorption of surfactant reduced the interfacial tension at oil‐water interface which resulted in generation of smaller sized droplets at the nozzle and caused the droplet to flatten. Consequently, the rise velocities of droplets decreased. A numerical model based on finite volume method was developed using commercial CFD package ANSYS Fluent®. The model employed volume of fluid method, suggested by Hirt and Nichols ( Journal of Computational Physics 1981 , 39 , 201), with an interface reconstruction technique based on piecewise linear representation for tracking the oil‐water interface. The influence of surface tension on the droplet dynamics was captured by including Continuum Surface force (CSF) approach suggested by Brackbill, Kothe, and Zemach ( Journal of Computational Physics 1992 , 100 , 335). The shape and rise velocities predicted from model were in good agreement with experimental data. The results from simulations were used, to analyze the wake structure and pressure distribution around the droplet. It was found that the smaller droplets which ascended in rectilinear path were associated with an axisymmetric wakes whereas larger and intermediate sized droplets in high SDS concentration wobbled as they ascended because of asymmetric wakes.
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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".