Effect of Near-Wall Turbulence on Selective Removal of Particles From Sand Beds Deposited in Pipelines
Bibliographic record
Abstract
This paper investigates the effect of near-wall turbulence on selective removal of small-size particulate matter from sand beds deposited in pipelines. In an effort to develop effective strategies for in-line fines separation, experimental data on selective particle removal by burst-sweep turbulent structures have been gathered. A 3¾″ (0.095 m) diameter—15 m long flow loop together with a particle image velocimetry (PIV) system has been commissioned and used for observations of turbulent burst activities. The flow loop was also equipped with bottom extractors to allow real time sampling of deposited particles which are then analyzed for determining particle size distribution changes with time. In this work, the alteration of size-composition during turbulent transportation of moving (sand) bed was assumed to be the effect of burst-sweep activity (coherent structures). The frequency of coherent burst structures was measured at various distances from the pipe wall, during the radial dissipation, and results were compared with existing literature. The experimental results indicated that when a bed of particles with 0.1–50 μm size range is exposed to burst-sweep activities during turbulent pipe flow, the concentration of fine particles within the bed increases with time (i.e., coarser particles are preferentially removed).
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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 | 0.000 |
| 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 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".