Turbulent flow over wetted and non-wetted superhydrophobic counterparts with random structure
Bibliographic record
Abstract
The turbulent structure of a channel flow over a non-wetted superhydrophobic (SHO) surface is experimentally investigated at Re = 9600 (based on channel width) at the region of y+ > 10 within the buffer and logarithmic layers. The SHO surface has a random pattern produced by spray coating and is compared with a wetted counterpart and also a smooth surface. Two planar particle image velocimetry measurements are carried out in the streamwise/spanwise and streamwise/wall-normal planes. The vector fields are obtained from both ensemble averaging and individual cross-correlations of double-frame images. The results showed a small increase (∼5%) of the mean velocity profile at y+ = 10 over the non-wetted surface in comparison with the wetted and the smooth surfaces. Up to 15% reduction of normal and shear Reynolds stresses is observed in the inner layer over the non-wetted SHO surface. The wetted SHO counterpart demonstrates no effect on the mean velocity and Reynolds stresses in comparison with the smooth surface. The result confirms the comment of Gad-el-Hak [“Experimental study of skin friction drag reduction on superhydrophobic flat plates in high Reynolds number boundary layer flow,” Phys. Fluids 25, 025103 (2013)] that the wetted SHO is hydrodynamically smooth if the surface pores are smaller than the viscous sublayer thickness. A noticeable suppression of the sweep and ejection events, increase of the spanwise spacing of the low and high speed streaks, and attenuation of vortical structures are observed over the non-wetted SHO. These indicate attenuation of the turbulence regeneration cycle due to the slip boundary condition over the non-wetted SHO surfaces with random texture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".