Bibliographic record
Abstract
The performance of grooves capable of reducing shear drag in laminar channel flow driven by a pressure gradient has been analyzed numerically. Only grooves with shapes that are easy to manufacture have been considered. Four classes of grooves have been studied: triangular grooves, trapezoidal grooves, rectangular grooves, and circular-segment grooves. Two types of groove placements have been considered: grooves that are cut into the surface (they can be created using material removal techniques) and grooves that are deposited on the surface (they can be created using material deposition techniques). It has been shown that the best performance is achieved when the grooves are aligned with the flow direction and are symmetric. For each class of grooves, there exists an optimal groove spacing, which results in the largest drag reduction. The largest drag reduction results from the use of trapezoidal grooves and the smallest results from the use of triangular grooves for the range of parameters considered in this work. Placing the same grooves on both walls increases the drag reduction by up to four times when comparing with grooves on one wall only. The predictions remain valid for any Reynolds number as long as the flow remains laminar.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".