Experimental Investigations and CFD Modeling of Contaminant Intrusion in a Water Network during Transient Events
Bibliographic record
Abstract
Intrusion in drinking water networks (DWN) can be triggered by transient low-pressure events. This intrusion may result in the contamination of drinking water supplied to consumers, which may have major health impacts. This research aims to investigate the influence of a DWN’s operating parameters on the intrusion and progression of the contaminant in a pilot drinking water network setup. Results show that the minimum time required for the contaminant to begin entering the system was influenced by the system operating pressure. Faster initial intrusion times were observed under low operating pressure. In addition, the crack size influenced the time required for the contaminant to fully intrude the system. Similarly, the time required for the contaminant to reach certain points in the DWN was influenced by the operating pressure. These results were verified using two additional tools; a high-speed camera was used to monitor the contaminant transient progression through the DWN under different pressures; and computational fluid dynamics modeling was used to calculate the corresponding contaminant concentration. The results suggest that the ability of the system to quickly stabilize its operating pressure would reduce the probability of a contaminant intrusion into the DWN.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".