An Experimental Study of Turbulent Non-Newtonian Fluid Flow in Concentric Annuli Using Particle Image Velocimetry Technique
Bibliographic record
Abstract
Turbulent flow of a Non-Newtonian polymer fluid through concentric annuli was studied using 9 m long horizontal flow loop (inner to outer pipe radius ratio = 0.4) and Particle Image Velocimetry (PIV) technique. A high molecular weight, anionic, water soluble, acrylamide-based copolymer was used as a viscosifier. The aqueous polymer solution exhibited power law rheology with strong shear thinning behavior. Experiments with aqueous polymer solutions have been conducted at the same bulk velocity as water experiments. Mean bulk velocity values changed from 0.827 to 1.164 m/s, corresponding to solvent (water) Reynolds number from 46000 to 68000. Mean axial velocity and Reynolds stress distribution in the near wall region (considering both inner and outer walls) and in the whole annular gap were determined. Axial mean velocity profile was found to be following the universal wall law close to the wall, but it deviated from logarithmic law with an increased slope in the logarithmic zone. Radial locations of the maximum velocity values were also determined and compared to that of water flow. For the range of Reynolds numbers studied, location of maximum velocity was found to be dependent on Reynolds number. As Reynolds number increased, location of maximum velocity moved closer to inner wall. Reynolds and laminar stresses were calculated. Reynolds stresses for polymer fluid flow decreased with increasing polymer concentration and were found to be always smaller than that of water. Laminar stresses, on the other hand, were found to be always higher at higher polymer concentration, reflecting the effect of the fluid viscosity.
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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".