Drag Reduction Due to Streamwise Grooves in Turbulent Channel Flow
Bibliographic record
Abstract
Drag reduction in turbulent channel flows has significant practical relevance for energy savings. Various methods have been proposed to reduce turbulent skin friction, including microscale surface modifications such as riblets or superhydrophobic surfaces. More recently, macroscale surface modifications in the form of longitudinal grooves have been shown to reduce drag in laminar channel flows. The purpose of this study is to show that these grooves also reduce drag in turbulent channel flows and to quantify the drag reduction as a function of the groove parameters. Results are obtained using computational fluid dynamics (CFD) simulations with turbulence modeled by the k–ω shear-stress transport (SST) model, which is first validated with direct numerical simulations (DNS). Based on the CFD results, a reduced geometry model is proposed which shows that the approximate drag reduction can be quantified by evaluating the drag reduction of the geometry given by the first Fourier mode of an arbitrary groove geometry. Results are presented to show the drag reducing potential of grooves as a function of Reynolds number as well as groove wave number, amplitude, and shape. The mechanism of drag reduction is discussed, which is found to be due to a rearrangement of the bulk fluid motion into high-velocity streamtubes in the widest portion of the channel opening, resulting in a change in the wall shear stress profile.
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 | 0.000 | 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 | 0.001 |
| 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".