Method to minimize polymer degradation in drag-reduced non-Newtonian turbulent boundary layers
Bibliographic record
Abstract
Abstract Polymer solutions are often used to produce drag-reduced fluid flows, in which the drag reduction is achieved due to the solutions’ non-Newtonian shear-thinning and viscoelastic properties. However, experiments using polymer solutions are typically challenging due to the tendency of the polymer to degrade when subjected to intense shearing. The degradation reduces the amount of drag reduction as the experiment progresses, which limits the experiment duration and the accuracy of the results. Here we introduce a method to avoid the degradation of the polymer solution by driving the flow with a paddlewheel instead of a conventional pump. The solution is shown to undergo very little degradation during the paddlewheel’s operation. The method is then applied to perform novel measurements of a drag-reduced turbulent boundary layer at two different Reynolds numbers, both with and without a suspended particle phase. The effects of carrier fluid rheology and Reynolds number on the particle concentration and velocity profiles are explored, as well as the effect on total drag of the flow. For a given fluid type and Reynolds number, the drag is found to be nearly constant with the global particle volume fraction, suggesting that the particles have a limited ability to modulate the drag. Remarkably, the particle velocity fluctuations are greater in the non-Newtonian cases, possibly due to enhanced collisions in the near-wall region.
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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) | 0.001 | 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 | 0.001 |
| 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".