Impacts of Leaks on Energy Consumption in Pumped Systems with Storage
Bibliographic record
Abstract
A conceptual examination of the energy impact of leaks in systems with storage is undertaken. Consideration of how leakage is experienced at the pump is followed by an analysis of how different leakage levels alter energy costs for a rudimentary system with three topological configurations: two with a storage tank located at different points, and one without storage. Additionally, two friction regimes are subsumed in the analysis. EPANET 2 simulations are used to determine system pressures, storage tank levels, energy costs, power consumption, and leakage volumes for all scenarios at five levels of leakage. Leaks increase operating costs in terms of lost water and extra energy consumption for all systems, and when a price pattern is implemented, the financial cost of energy can sometimes be traded off with actual energy consumption. Storage in a system does not guarantee lower energy use relative to direct pumping, and in some cases it may promote higher leakage due to elevated system pressures. The results, though system specific, suggest that the importance of leaks in a system with storage depends on a number of factors, especially the relative locations of system components and the pumping strategy. Thoughtful consideration of the latter can be instrumental in achieving operation that balances financial and energy conservation objectives. To further test key relationships, a representative network is briefly considered. In all cases, the percentage increase in energy cost is greater than percentage leakage when the same pressure requirements are met.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".