An Experimental Investigation of Turbulent Drag Reduction in Concentric Annulus Using Particle Image Velocimetry Technique
Bibliographic record
Abstract
Polymer drag reduction is investigated using the Particle Image Velocimetry (PIV) technique in fully developed turbulent flow through a horizontal flow loop with concentric annular geometry (inner to outer pipe radius ratio = 0.4). The polymer used was a commercially available partially hydrolyzed polyacrylamide (PHPA). The polymer concentration was varied from 0.07 to 0.12% V/V. The drag reduction is enhanced by increasing polymer concentration until the concentration reaches an optimum value. After that, the drag reduction is decreased with the increasing polymer concentration. Optimum concentration value of PHPA was found to be around 0.1% V/V. Experiments were conducted at solvent Reynolds numbers of 38700, 46700 and 56400. The percent drag reduction was found to be increasing with the increasing Reynolds number. The study was also focused on analyzing the mean flow and turbulence statistics for fully-turbulent flow using the velocity measurements acquired by PIV. Axial mean velocity profile was found to be following the universal wall law close to the wall (i.e., y+ <10), but it deviated from log law results with an increased slope in the logarithmic zone (i.e., y+ >30). In all cases of polymer application, the viscous sublayer (i.e., y+ <10) thickness was found to be higher than that of the water flow. Reynolds shear stress in the core flow region was found to be decreasing with the increase in polymer concentration.
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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) | 0.000 | 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".