Effect of Lagrangian time scales on the statistical simulations of droplet penetration through turbulent pipe flows
Bibliographic record
Abstract
Penetration of droplets in fully-developed turbulent pipe flows (vertical configuration) was studied numerically. Two Reynolds numbers ( R e D = 37,700 and 11,700) based on the pipe diameter were used in the simulations. Statistics used in the single-phase flow characterization (mean velocities, root mean square fluctuation velocities , and turbulence dissipation rate) were obtained from the law of the wall relationships in addition to curve-fitting from direct numerical simulation (DNS) data found in the literature. The droplet phase was simulated using a one-way coupling Lagrangian random-walk eddy interaction model (EIM). Monodispersed droplets, ranging from 1.78 to 26 . 83 μ m , were released separately in the pipe-flow computational domain. A modified eddy lifetime, based on local turbulent Reynolds numbers ( R e λ T ) and velocity fluctuations perpendicular to the walls, is proposed. Simulation results of droplet penetration show relatively good agreement against experimental data obtained from the literature.
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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".