Bibliographic record
Abstract
Continuous evolution in nature has created optimum solutions for creature survival that have inspired many innovative engineering designs. Riblet geometries, passive flow control devices, have been studied, which were inspired by the skin of fast-swimming sharks. Turbulent boundary layer research reveals the positive effect of riblets in reducing drag by manipulating turbulent structures. Reducing drag is an important topic for the aviation industry, as it directly relates to fuel savings and reductions in carbon footprints. Aircraft noise represents another significant area of concern. When riblet designs modify turbulent structures, they can also impact pressure sources within the boundary layer, consequently influencing the generation of self-noise. Earlier research studies have demonstrated the favorable outcomes of riblet configurations on the variations in wall pressure, resulting in reduced levels of noise propagation. The current review paper is mainly devoted to the application of riblets in the aviation industry, focusing on studies that are performed in wind tunnels, flight tests, and using numerical techniques. Proving the desired performance of micro-grooves, their method of fabrication and implementation on aircraft surfaces are important topics that are also discussed. In addition, the effect of durability on the performance and required maintenance intervals was previously investigated and is also presented. Finally, recommendations for future activities in the relevant fields of study are provided.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".