Wavelet analysis on the drag-reducing characteristics of turbulent channel flow with surfactant additive based on experimental data
Bibliographic record
Abstract
Two-dimensional velocity fields for turbulent channel flows of water and 50 ppm CTAC/NaSal (cetyltrimethyl ammonium chloride/sodium salicylate) solution were experimentally obtained by particle image velocimetry. Multi-scale decompositions of the fluctuating velocity signals were performed by two-dimensional binary orthogonal discrete wavelet to analyze the influence of surfactant additives on the multi-scale characteristics of turbulent channel flow. From the results of wavelet multi-scale decompositions, it can be observed that the quantity of coherent structures near the wall in CTAC solution flow is decreased obviously. The results of the flatness factor show that the addition of drag-reducing additives inhibits the intermittency in turbulence. By analyzing the distribution of local intermittence measure and local Reynolds shear measure, it is found that the intermittency mainly concentrates near the wall, and the intermittent region distinctly reduces in CTAC solution flow compared with that in water flow. By combining the analysis of the motion of coherent structures in turbulent channel flow, it is shown that surfactant additives mainly and distinctly impact the coherent structures near the wall, especially in the viscous layer.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".