Drag reduction in turbulent pipeline flow of mixed nonionic polymer and cationic surfactant systems
Bibliographic record
Abstract
Abstract Turbulent drag reduction behaviour of a mixed nonionic polymer/cationic surfactant system was studied in a pipeline flow loop to explore the synergistic effects of polymeric and surfactant drag reducing additives. The nonionic polymer used was polyethylene oxide (PEO) at three different concentrations (500, 1000, and 2000 ppm). The surfactant used was cationic octadecyltrimethylammonium chloride (OTAC) at concentration levels of 1000 and 2500 ppm. Sodium salicylate (NaSal) was used as a counter‐ion for the surfactant at a molar ratio of 2 (MR = Salt/OTAC = 2). Relative viscosity and surface tension were measured for different combinations of PEO and OTAC. While the relative viscosities demonstrated a week interaction between the polymer and the surfactant, the surface tension measurements exhibited negligible interaction. The pipeline results show a considerable synergistic effect, that is, the mixed polymer–surfactant system gives a significantly higher drag reduction (lower friction factors) as compared with pure polymer or pure surfactant. The addition of surfactant to the polymer always enhances drag reduction. However, the synergistic effect in mixed system is stronger at low polymer concentrations and high surfactant concentrations. © 2011 Canadian Society for Chemical Engineering
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".