Effect of bubbles and additives on friction factor in pipeline flow
Bibliographic record
Abstract
Abstract This work investigated drag reduction in turbulent flow of water using air bubbles and other additives. The first phase of the experimental program dealt with the effects of small air bubbles on wall shear stress and friction factor. The friction factor versus Reynolds number data exhibited different trends at low and high Reynolds numbers. At high Reynolds number, the two‐phase mixture behaved as a pseudo‐homogeneous system and the friction factor followed the single‐phase behaviour. At low Reynolds number, the observed friction factor was significantly higher than that of single‐phase fluids. The observed trends were interpreted in terms of the average bubble sizes predicted from a well‐known empirical equation. The second phase of the experimental program investigated the effects of additive/air‐bubble combinations on drag reduction in water flow where the additives used were frother and polymer. The addition of frother made the flow more homogeneous and the mixture followed the single‐phase behaviour more closely. In the absence of polymer, the injection of air bubbles generally increased the wall shear stress and friction factor compared to single‐phase flow, especially at low Reynolds number. However, the addition of polymer to two‐phase water/bubbles mixture induced drag reduction up to as high as 60%. The friction factor data for polymer‐solution/bubbles combination followed the polymer‐solution line, regardless of the presence of frother.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".