Leakage Rate Uncertainty in Water Distribution Systems with Uncertain Demands: Impacts on Delivery Pressures
Bibliographic record
Abstract
Leakage rate has long been known to be related to the internal pressure of the pipe at leak locations. Reducing leakage and excess pressures are two main goals in pressure management activities. Computer model-based leak detection methods that detect leaks by analyzing the pipeline hydraulic state have been widely employed in the industry, but their effectiveness in practical applications is often challenged by real-world uncertainties. This study quantitatively assessed the effects of uncertainties in leakage rates, consumer water demands, and pipes roughness on the delivery pressures in water distribution systems. Variations in leakage rates due to changes in pressure and discharge coefficient are shown. And, the most sensitive uncertain parameters contributing to uncertainty in leakage rates are determined. For this purpose, the mean-centered first-order method is used to estimate the first and second moments (i.e., mean and variance) of leaks. Then, changes in delivery pressures caused by uncertainties in leakage rates, consumer water demands, and pipes roughness are quantified using Monte Carlo simulations. This study provides valuable quantitative results contributing toward a better understanding of how real-world uncertainties affect pressure distributions in water distribution systems and can be helpful in pressure management studies.
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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".