Internal and External Flow over Laser-Textured Superhydrophobic Polytetrafluoroethylene (PTFE)
Bibliographic record
Abstract
In this work, internal and external flows over superhydrophobic (SH) polytetrafluoroethylene (PTFE) were studied. The SH surface was fabricated by a one-step femtosecond laser micromachining process. The drag reduction ability of the textured surface was studied experimentally both in microscale and macroscale internal flows. The slip length, which indicates drag reduction in fluid flow, was determined in microscale fluid flow with a cone-and-plate rheometer, whereas a pressure channel setup was used for macroscale flow experiments. The textured PTFE surface reduced drag in both experiments yielding comparable slip lengths. Moreover, the experimentally obtained slip lengths correspond well to the result obtained applying a semianalytical model, which considers the solid fraction of the textured surface. In addition to the internal flow studies, we fabricated SH PTFE spheres to test their drag reduction abilities in an external flow experiment, where the terminal velocities of the falling spheres were measured. These experiments were conducted at three different Reynolds numbers in both viscous and inertial flow regimes with pure glycerol, a 30% glycerol solution, and water. Surprisingly, the drag on the SH spheres was higher than the measured drag on the non-SH spheres. We hypothesize that the increase in form drag outweighs the decrease in friction drag on the SH sphere. Thus, the overall drag increased. These experiments demonstrate that a superhydrophobic surface that reduces drag in internal flow might not reduce drag in external flow.
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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".