Parametric Analysis of Drag Reduction Determined by Streamwise Triangular Riblet Microstructures: Effect of Included Angle Variation
Bibliographic record
Abstract
Recent research has focused on the hydrodynamics of turbulent water flow over surfaces structured with various micro-scale features.Previous studies have demonstrated that streamwise triangular riblets (STR), also known as sawtooth riblets, and other riblet designs can achieve drag reduction, selfcleaning, and fouling-resistant effects.This study aimed to numerically simulate and parametrically analyze the effect of the included angle () of STRs on potential drag reduction.The CFD simulations conducted in this study examined the impact of design parameters on turbulent flow hydrodynamics and their influence on drag reduction performance.The analysis considered several included angle values: = 15, 30, and 60, at multiple flow velocities.Flow conditions, particularly the turbulent structures formed around the riblets, were analyzed in detail and compared with published data.CFD simulations utilized the LES WALE model with a prism and hexahedral mesh.The examination of turbulent flow patterns near riblet tips and valleys revealed characteristics consistent with previously published data for 60 STR.Furthermore, as decreases, the range of nondimensional riblet spacing (S + ) exhibiting dragreducing effects narrows, while the range of Reynolds numbers increases.The smaller included angle analyzed was associated with maximum drag reduction (~10%) at smaller S + values.The results of this study are expected to pave the way for developing, optimizing, and controlling advanced hydro-and aerodynamic functional surfaces.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 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".