Impacts of flow velocity and microbubbles on water flushing in a horizontal pipeline
Bibliographic record
Abstract
Water flushing to remove particle sediment is essential for safe and continuous transport of many industrial slurries through pipelines. Efficient flushing strategy may reduce water consumption and the cost associated with water usage, and help water conservation for sustainability. In this study, a computational fluid dynamics (CFD) model coupled with the kinetic theory of granular flow for the flushing process is presented. The CFD models were validated against field data collected from a coal slurry pipeline of 128 km in length, 0.575 m in diameter, achieving an average error of less than 15 % for outlet solid concentration over time. A parametric study evaluated the effects of water velocity (1.88–5.88 m /s ), bubble size (50 µm, 150 µm, and 1000 µm) and bubble volume fraction (0.05–0.2) on flushing performance including pipeline cleanness, cleanness efficiency, and water consumption. The obtained outcomes indicate that higher water velocity is preferred and an increase in water velocity from 1 . 88 m /s to 5 . 88 m /s reduces the water consumption by 28 %. Large bubbles may hinder the flushing process and increase the water consumption by 23 %. Remarkably, small bubbles facilitates the flushing process and lead to 35 % reduction in water consumption. These effects are attributed to the turbulent characteristics in the pipelines in presence of microbubbles. The study provides valuable insights into optimizing flushing operations by rationally leveraging microbubbles to improve water resource efficiency and operational reliability in industrial applications.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".