Does intermittent supply result in hydraulic transients? Mixed evidence from two systems
Bibliographic record
Abstract
Abstract Pressure transients can cause severe damage in continuous water supply pipe networks, but little is known about pressure transients in intermittent networks. Published examples of high-frequency pressure monitoring in intermittent networks are lacking. Intermittent supply can be caused by poor network condition and is associated with delivering less water, less frequently, and with poorer quality than continuous supply. Given the frequency with which intermittent systems drain, fill, and change supply regimes, pressure transients have been hypothesized to be common and to be one mechanism by which intermittent supply further degrades network condition. We present supply start-up data from two very different intermittent systems: a low-pressure, intermittent network in Delhi, India, and a higher-pressure intermittent network in Arraiján, Panama. Across monitoring locations at both sites, we did not detect substantial pressure transients due to pipe filling. In Arraiján, pump start-ups, pump shutdowns, and pipe bursts were associated with potentially problematic transients. We conclude that pipe filling in intermittent supply does not always result in concerning pressure transients. The largest risks to pipe conditions we observed were due to pumping changes in close succession; hence, we recommend that utilities operating intermittent (and continuous) systems leave adequate dissipation time between changes in pump operation.
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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".