Internal and External Flow over Laser-Textured Superhydrophobic Polytetrafluoroethylene (PTFE)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 it