Drag Reduction by Fish-Scale Inspired Transverse Asymmetric Triangular Riblets: Modelling, Preliminary Experimental Analysis and Potential for Fouling Control
Bibliographic record
Abstract
The natural surfaces of many plants and animals provide examples of textures and structures that remain clean despite the presence of environmental fouling contaminants. A biomimetic approach to deciphering the mechanisms used by nature will facilitate the development and application of fouling-resistant surfaces to a range of engineering challenges. This study investigated the mechanism underlying the drag reduction phenomenon that was shown to be responsible for fouling resistance for underwater surfaces. For this purpose, a novel fish-scale-inspired microstructure was shown to exhibit a drag reduction effect similar to that of its natural replica. The primary mechanism through which this occurs is a delayed transition to turbulence. To investigate this mechanism, a Large Eddy simulation was performed at several Reynolds numbers (Re). This analysis demonstrated a peak drag reduction performance of 6.7% at Re = 1750. The numerical data were then experimentally validated through pressure drop measurements performed by means of a custom-built micro-channel. In this case, a peak drag reduction of 4.8% was obtained at Re = 1000. These results suggest a relative agreement between the experimental and numerical data. Taken together, this study advocates that, for the analyzed conditions, drag reduction occurs at low Reynolds numbers. Nonetheless, once flow conditions become more turbulent, the decline in drag reduction performance becomes apparent.
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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".