Influence of Water Drag and Headspace Air Pressure on Airflow in Sanitary Sewers
Bibliographic record
Abstract
Odor complaints are widely reported due to hazardous gases being released from sewer systems. Mitigating and controlling the emission of these gases is the key to a better air environment. Existing airflow system models rely on limited, uncontrolled experiments lacking air pressure measurements. Water surface drag and wall friction coefficients are the main parameters used to calibrate these models, and they should be determined precisely for better model efficiency. The key dimensionless parameters influencing these coefficients are identified and listed as the nominal Froude and Reynolds numbers, a dimensionless pressure gradient term, headspace height to diameter ratio, and relative roughness height of the wall and water surface, calculated based on the water surface velocity and pipe diameter. A three-dimensional computational fluid dynamics model is developed to explore the effect of those parameters, assuming the air-water interface as a moving boundary in Couette flows. The resulting airflow regimes are analyzed using the produced air velocity profiles. Three regimes are identified: two are characterized as conduit pressurized flow with minor adjustments at the boundaries, and the last one includes the typical case for air movement in sewer networks. The value of the water drag coefficient is between 0.002 and 0.01, while the wall friction coefficient ranges from 0.015 to 0.045 for typical sewer conditions. This study provides an understanding of airflow dynamics and an accurate way to determine average air velocity, water drag, and wall friction coefficients in practical cases. The error from using a single value for both coefficients at any pressure gradient is assessed and reduced.
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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 | 0.001 | 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".