Modeling geysers triggered by an air pocket migrating with running water in a pipeline
Bibliographic record
Abstract
Storm sewer systems may experience storm geysers, raising concerns about public safety. A thorough understanding of the influential factors of the geysers is essential yet insufficiently investigated in the literature. A transient three-dimensional (3D) computational fluid dynamics model incorporating the volume of fluid method is used to investigate the geyser formation mechanism and hydrodynamics. An air pocket in a pressurized pipe travels with water past a vertical shaft, producing an air-releasing geyser and, subsequently, a rapid-filling geyser. If the air pocket in the pipe is too small or if it moves too quickly, a hybrid geyser might be set off when the air-releasing and rapid-filling geysers overlap. A hybrid geyser has unique properties since it combines an air-releasing geyser and a rapid-filling geyser. The presence of hybrid geysers lowers the height of air-releasing and rapid-filling geysers. Equations are proposed for predicting the heights of the geysers with errors of about 15%. The height of the air-releasing geyser increases with the water level in the shaft. As the pressure difference between the two ends of the pipe reduces, the height of the rapid-filling geyser increases. The vertical shaft diameter has little influence on rapid-filling geysers, while a small diameter often results in high air-releasing geysers. The effect on the height of both kinds of geysers is negligible when the air pocket volume is large enough. The findings can be used for designing storm geyser mitigation measures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".