Air-water-sand three-phase jets in crossflow, Part II: Velocity distributions and turbulence characteristics of bubbles and sand particles
Bibliographic record
Abstract
This study examines the velocity and turbulence characteristics of bubbles and sand particles in air-water-sand three-phase jets in crossflow, building upon the phase concentration analysis presented in Part I. Bubble Image Velocimetry (BIV) and Particle Tracking Velocimetry (PTV) were employed to quantify these parameters under varying initial jet conditions, including different sand concentrations, slurry (water-sand) flow rates, and air flow rates. The presence of negatively buoyant sand particles reduced peak bubble rising velocities by up to 15 %, while simultaneously enhancing bubble turbulence intensities by approximately 15 %. Slurry flow rate exhibited a pronounced effect, increasing peak bubble rising velocities by 70 % and sand vertical velocities by 88 %. Higher slurry flow rates also delay the development of streamwise bubble velocity along the jet trajectory and promote a more uniform distribution of bubble turbulence intensity. The transition height of sand particles—from rising to settling—decreased with increasing initial sand concentration but increased with higher slurry flow rates. A predictive model was developed to characterize this transition, demonstrating strong agreement with experimental data (R 2 = 0.97). The turbulence intensity of sand particles peaked near the upstream shear layer and sharply decreased in the settling region, reflecting a steady deposition process. • Velocity and turbulence of bubbles and sand particles were revealed. • Sand particles reduced bubble rising velocity but enhanced bubble turbulence. • Increased slurry flow rate elevated both bubble and sand vertical velocities. • A predictive model was developed for sand transition from rising to settling. • Sand turbulence intensity dropped rapidly from the rising to the settling region.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".