Wettability Effects of Curved Superhydrophobic Surfaces on Drag Reduction in Taylor–Couette Flows of Water and Oil
Classification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".
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
Abstract This study examines the effects of surface wettability on the drag-reducing performance of three hydrophobic coatings, namely, flouropel coating (FPC-800M), superhydrophobic binary coating (SHBC), and ultra-ever dry (UED)—when applied to curved aluminum surfaces. The wettability and flow characteristics were characterized using three liquids of different viscosities: de-ionized water and silicone oils of 5 and 10 cSt. Static and dynamic contact angles on the surfaces were measured, and the drag reduction was evaluated using a Taylor–Couette flow cell in a rheometer. The static contact angle (SCA) measurements indicated that the coated surfaces were superhydrophobic for water, with a maximum static contact angle of 158 deg, but oleophilic for the 10 cSt silicone oil, with a static contact angle of 13 deg. The rheometer measurements using water showed a maximum drag reduction of 18% for the UED-coated surfaces. Interestingly, the oleophilic surfaces (which have low SCA) showed a maximum drag reduction of 6% and 7% in the silicone oils. The observed drag reduction is due to an increase in the plastron thickness, which is caused by an increase in the Reynolds number and dynamic pressure coupled with a decrease in the static pressure normal to the superhydrophobic wall.
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 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) | 0.000 | 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 it