An Experimental Investigation of the Impact of Anisotropic Slip Length on Turbulent Flow Over Superhydrophobic Surfaces Within an Open Channel
Bibliographic record
Abstract
Abstract This study investigates the sustainability and applicability of commercial superhydrophobic (SH) coatings for reducing skin friction drag. Three different SH surfaces were applied to flat plates using a spray coating technique, with static contact angles of 145 deg, 147 deg, and 155 deg, respectively. Turbulent flow measurements were conducted using a two-dimensional laser Doppler velocimetry (LDV) system in an open channel flow facility at a Reynolds number of 34200. The novelty of this work lies in characterizing drag reduction from the leading edge to the trailing edge of the fabricated surface in the streamwise direction rather than one measurement plane. Velocity measurements were performed in a spanwise direction at selected planes. The study also evaluated the correlation between slip velocity and slip length, showing that slip length becomes equivalent to the coating thickness as the plastron depletes. The fabricated SH surfaces increased turbulence intensity and Reynolds normal stress, primarily near the wall, with diminishing effects further away. This confirms the existence of an interference region of air/water near the wall induced by SH surfaces. Overall, the results demonstrated average drag reductions of 11%, 7%, and 18% for the tested surfaces. The study provides strong evidence for the effectiveness of SH surfaces in consistently reducing viscous drag across the entire plate span, from the leading edge to the trailing edge.
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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) | 0.001 | 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".