SYNERGISTIC EFFECTS OF ANIONIC SURFACTANT AND NONIONIC POLYMER ADDITIVES ON DRAG REDUCTION
Bibliographic record
Abstract
Turbulent drag reduction (DR) behavior of mixed nonionic polymer and anionic surfactant solutions in water was studied in a pipeline set up to explore the synergic effects of mixed additives on DR. The concentration of polymer polyethylene oxide (PEO) was varied from 0 to 2000 ppm and the concentration of surfactant sodium dodecyl sulfate (SDS) was varied from 0 to 5000 ppm. The critical aggregation concentration (CAC), where the interaction between the polymer and the surfactant begins, and the polymer saturation point (PSP), where the polymer molecules become saturated with the surfactant, were determined using electrical conductivity and surface tension measurements. As the polymer concentration was increased the CAC decreased but the PSP increased. The relative viscosity showed a remarkable increase upon the addition of surfactant to the polymer solution due to extension of polymer chains caused by the formation of micelles on the backbone of the polymer molecules. The data exhibited a considerable increase in DR in the case of mixed polymer/surfactant systems. The percent reduction in friction factor was as high as 79 when 3000 ppm or more surfactant was added to the 500 ppm polymer solution. Furthermore, the drag reduction behavior of the polymer solution changed from so-called Type A to Type B. In Type A drag reduction, a transition from laminar to turbulent regime is observed with a clear-cut onset point. In Type B drag reduction, no transition or onset point is observed; the data fall on a gradual extension of the laminar line.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".