An Experimental Study of the Effect of Drag Reducing Additive on the Structure of Turbulence in Concentric Annular Pipe Flow Using Particle Image Velocimetry Technique
Bibliographic record
Abstract
The effect of drag reducing additive on the structure of turbulence in concentric annular pipe flow was investigated using Particle Image Velocimetry (PIV) technique. Experiments were conducted using a 9m long horizontal flow loop with concentric annular geometry (inner to outer pipe radius ratio = 0.4). The drag reducing additive was a commercially available partially hydrolyzed polyacrylamide (PHPA). The experiments were conducted using 0.1% V/V polymer concentration, giving a drag reduction of 26% at a solvent Reynolds number equal to 56400. Near wall local fluctuating velocity values were determined by analysing the PIV data. The root mean square (RMS) values of radial velocity fluctuations showed a significant decrease with the use of drag reducing additive. The RMS values of axial velocity fluctuations near the wall (Y+<10) were similar for both water and polymer fluid flow; though, higher peaks were obtained during the polymer fluid flow. As compared to water flow, a strong reduction in vorticity was observed during polymer fluid flow. The degree of vorticity reduction on the inner wall was higher than that of the outer wall. Results of the viscous dissipation and the shear production terms in the kinetic energy budget showed that less energy was produced and dissipated by the route of turbulence when using polymer fluid.
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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 | 0.001 |
| 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) | 0.000 | 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".