Time‐series and extended Karhunen–Loève analysis of turbulent drag reduction in polymer solutions
Bibliographic record
Abstract
Direct numerical simulations and statistical analysis techniques are used to study the drag‐reducing effect of polymer additives on turbulent channel flow in minimal domains. Additionally, a new formulation of Karhunen–Loève decomposition for viscoelastic flows is introduced, allowing the dominant features of the polymer stress fields to be characterized. In minimal channels, there are intervals of “active” and “hibernating” turbulence that display very different structural and energetic characteristics; the present work illustrates how the statistics of these intervals evolve over the entire range of drag reduction (DR) levels. The effect of viscoelasticity on minimal channel turbulence is twofold: first, it strongly suppresses the active turbulent dynamics that predominate in Newtonian flow and second, at sufficiently high Weissenberg number it stabilizes the dynamics of hibernating turbulence, allowing it to predominate in the maximum drag reduction regime. In this regime, the stress fluctuations become delocalized from the wall region, encompassing the entire flow domain. © 2014 American Institute of Chemical Engineers AIChE J , 60: 1460–1475, 2014
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".