Drag Reduction Effects Associated with Streamwise Triangular Riblet (STR) Microstructures
Bibliographic record
Abstract
Significant research efforts were recently made in studying hydrodynamics of a turbulent water flow over surfaces structured with various micro-scale features.Past studies have demonstrated drag reduction, self-cleaning, and fouling-resistant effects of streamwise triangular riblets (STR), also known as sawtooth riblets, and other riblet designs.The goal of this study was to numerically simulate and parametrically analyse the effect of included angle (α) of STRs on potential drag reduction effects.The CFD simulations performed within the scope of the current study were focused on the effect of design parameters on turbulent flow hydrodynamics as well as their effect on drag reduction performance.The analysis considered several different values of the included angle, namely α = 15°, 30°, and 60° at multiple flow velocities.The flow conditions -particularly the turbulent structures formed around the riblets -have been analysed in detail and have been compared with published data.CFD simulations were run by means of the LES WALE model encompassing a prism and hexahedral mesh.The examination of the turbulent flow patterns near riblet tips and valleys revealed that the flow exhibits characteristics consistent with those published in the past for 60° STR.Moreover, as α decreases, the range of nondimensional riblet spacing (S+) exhibiting drag reducing effects reduces while the range of Reynolds number increases.The smaller value of the analysed included angle seemed to be associated with maximum drag reduction (~10%) for smaller S+ values.The results of this study are expected to open new avenues towards the development, optimization, and control of advanced hydro-and aerodynamic functional surfaces.
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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) | 0.001 | 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".