Effects of Surfactant and Water Concentrations on Pipeline Flow of Emulsions
Bibliographic record
Abstract
New experimental results are presented on the effects of surfactant and water concentrations on pipeline flow of emulsions. For a fixed water concentration of 30% by volume, the flow behavior of water-in-oil emulsions containing eight different surfactant concentrations (0, 0.05, 0.1, 0.5, 0.75, 1, 1.5, and 2% by wt based on oil) was investigated in five different diameter horizontal pipes. The surfactant used was oil-soluble Emsorb 2503 (sorbitan trioleate). The influence of water concentration on the pipeline flow behavior of emulsions was determined by varying the water concentration from 30 to 80% by volume in increments of 5%. The emulsions were water-in-oil type until the water concentration was 40% by volume. Upon further increase in the water concentration, inversion of water-in-oil (W/O) emulsion to oil-in-water (O/W) emulsion occurred at a water concentration of 45% by volume. The unstable W/O emulsion without surfactant exhibits drag reduction behavior in turbulent flow; that is, the friction factor data fall well below the single-phase Blasius equation. With the addition of surfactant, the W/O emulsion becomes more homogeneous and the friction factor data fall close to the Blasius equation. However, the surfactant-stabilized W/O emulsions exhibit a significant delay in transition from laminar to turbulent regime. The delay in laminar to turbulent transition depends on the pipe diameter. The O/W emulsions obtained upon phase inversion of surfactant-stabilized W/O emulsion behave as truly homogeneous fluids in that there is no delay in transition and that the friction factor data follow the Blasius equation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".