Energy Dissipation in Intermittent Water Supplies Depends on Household Behavior
Bibliographic record
Abstract
Intermittent water supply (IWS) is a persistent reality for 1 billion people. Support from water users can be vital to the success of IWS improvement projects. It is often presumed that users’ experiences will improve during the transition from intermittent to continuous supply. This paper explores an unstudied factor related to user experience during changes in intermittent supply: friction-induced energy dissipation, which can compromise pressure at user connections, increase user costs, and reduce user support for utility actions. We modeled how energy dissipated to friction (1) changes throughout the transition from intermittent to continuous supply, and (2) depends on how hastily (versus patiently) users attempt to withdraw water. We constructed and employed an analytic model of a highly simplified IWS (one household) and hydraulic simulations of a city-scale intermittent system. The analytic model and hydraulic simulations combine to provide strong theoretical evidence that (1) energy dissipation increases during some of the transition from intermittent to continuous supply, and that (2) energy dissipation is highly dependent on user behavior. Energy dissipation increases with water supply continuity until some users get as much water as they demand. Thereafter, energy dissipation can reduce with supply continuity, but only if users slow their withdrawals. Hasty withdrawals cause energy dissipation to increase throughout the transition to continuous supply. Simulated user withdrawal paces affected energy dissipation more than a ninefold increase in leakage. We recommend additional theoretical and field research on energy dissipation, user withdrawal paces under intermittent supply, and user support for continuous supply improvement projects.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".