Drag on superhydrophobic sharkskin inspired surface in a closed channel turbulent flow
Bibliographic record
Abstract
Salvinia leaf and sharkskin are prime examples of nature's marvel. Salvinia leaf‐inspired superhydrophobic surfaces keep themselves clean and reduce drag in fluid flow. Sharkskin also reduces drag in turbulent flow and inhibits biofouling. Therefore, the prospect of having a drag‐reducing surface with both salvinia leaf and sharkskin properties is attractive. However, fabricating such a surface is difficult, and the current fabrication methods require at least two separate steps. In addition, the mechanisms of drag reduction of salvinia leaf and sharkskin are different, and their combined effect on the flow field is not well understood. In this study, we produced a PTFE surface that mimics sharkskin in its surface pattern and copies the superhydrophobic nature of the salvinia leaf in its microstructure. This surface was fabricated by laser machining and tested in a closed channel under turbulent flow conditions. We measured the pressure drop at different Reynolds numbers on this surface both in pre‐wet and non‐pre‐wet conditions and compared the result with pressure drop data on four other PTFE samples: two types of non‐superhydrophobic sharkskin inspired surface (riblets), a superhydrophobic surface, and a non‐machined surface. Both the non‐superhydrophobic riblets and the superhydrophobic sample reduced drag compared to the non‐machined surface. However, we observed a lack of drag reduction by the superhydrophobic riblets sample. We presented a qualitative explanation for the lack of drag reduction and concluded that the modifications of the flow field by the two drag reduction mechanisms are not beneficial for overall drag reduction in our experiment.
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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".