PARTICLE-TURBULENCE INTERACTIONS IN THE PRESENCE OF A ROUGH WALL
Bibliographic record
Abstract
Experiments were conducted over smooth and rough walls in a low Reynolds number horizontal turbulent channel flow laden with small (64 µm) glass particles. A particle image velocimetry technique was used to measure velocities of both the carrier fluid and particles. Various turbulent characteristics were examined to investigate the impact of wall roughness on the particle-turbulence interactions. The results show that particles increased the turbulent intensities near the wall, and reduced them in the outer layer, but these effects were dampened for the rough wall. On the contrary, particles increased the peak value of the Reynolds shear stress in the presence of the rough wall when compared to the unladen flow. Particle velocity fluctuation intensities matched those of the unladen fluid for the smooth wall, but the peak velocity fluctuation intensities were enhanced in the presence of wall roughness due to particle-wall collisions. The effect is larger for the streamwise velocity fluctuation intensity than the wall-normal velocity fluctuation intensity. The present results indicate that the particle motion is more responsive to the presence of the rough wall than the particle-laden fluid.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".