Drag Reducing Functional Surface With 60 Degree Riblets: Modelling, Microfabrication, and Performance Evaluation
Bibliographic record
Abstract
The current study presents initial progress in the development of a drag-reducing surface featuring a 60-degree triangular riblet-groove functional design. The research encompasses the design process, computational fluid dynamics (CFD) simulations, microfabrication, and performance evaluation. The riblet-groove surface was designed with a lateral spacing of 57.8 µm and a depth of 50 µm, based on optimized parameters found in the literature. CFD simulations were conducted using a Large Eddy Simulation (LES) Wall Adapted Local Eddy-viscosity (WALE) model of the Taylor-Couette flow, revealing a reduction in drag of approximately -6.9%. Two acrylic drums were microfabricated using high-precision multi-axis single point diamond turning technology, ensuring excellent surface quality and form accuracy (e.g., <2 µm). One drum featured a flat surface, while the other incorporated the functional riblet-groove surface. Further evaluation of the functional performance was carried out using a rheometer-based Taylor-Couette measuring system, which recorded torque, position, and force simultaneously. Three key performance characteristics of the Taylor-Couette flow dynamics were calculated using the collected data: torque as a function of angular velocity, shear stress-shear rate relationship, and drag reduction versus s+ value. Notably, a drag reduction of -12.2% was achieved at s+ = 10.5. This research opens new opportunities in full development of textured/structured functional surfaces for a wide range of the life and industrial applications including aerospace, automotive, marine, energy, and biomedical products.
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 | 0.000 | 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 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".