Household Behaviour and Energy Loss in Intermittent Water Supply Networks
Bibliographic record
Abstract
Intermittent water supply (IWS) networks are a problematic reality for over one billion people. Despite their drawbacks, IWS networks persist, and projects to convert them to continuous supply often fail. Here we explore one reason such projects might fail: the energy loss associated with IWS. Under IWS, water is delivered over a shorter period, increasing flow rates and thereby increasing energy losses. Could this energy loss prevent utilities from increasing their supply durations?To explore this question, we built two IWS versions of the Modena water network in EPANET. All households were assumed to withdraw water either i) as hastily as possible or ii) as patiently as possible. Artificial tanks and emitters modelled household storage and network leakage, respectively. Artificial tanks filled quickly, mimicking hasty withdrawals. To model patient withdrawals, a flow control valve was installed upstream of the tank, distributing withdrawals evenly throughout the duration of water supply.Simulations showed that when households withdraw hastily, energy losses strictly increase as the supply continuity of the network increases. Conversely, when households withdraw water patiently, energy losses increase initially, reach at least one maximum, and then decrease as supply continuity increases. Our results suggest that since energy losses often increase as utilities increase continuity, energy loss could obstruct some utilities from increasing supply continuity and from achieving continuous supply. We also found clear evidence that network behaviour strongly depends on the hastiness of household withdrawals. We also found that when networks with patient households are supplied with ample continuity, leakage can substantially influence the energy loss. We recommend additional theoretical and field research on IWS investigate the pace at which household withdrawals occur.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".