Identification of Factors That Influence Energy Performance in Water Distribution System Mains
Bibliographic record
Abstract
This paper aims at identifying paramount hydraulic factors in energy dynamics of water mains, using Principal Components Analysis (PCA). The proposed method is applied to two large ensembles of leaky and non-leaky pipes comprising over 40,000 pipes selected from 18 North American water distribution systems to guarantee the versatility of pipe characteristics and statistical significance of the explored patterns. PCA mono-plots indicate energy metrics such as Net Energy Efficiency, Energy Lost to Friction and Energy Lost to Leakage serve better in identification of low from high efficiency pipes. In addition, PCA mono-plots and bi-plots reveal relative importance of hydraulic parameters and that average flow rate, hydraulic proximity to major components and average unit headloss can have more tangible effects on energy dynamics of pipes compared to leakage and average pressure. Some factors such as elevation, diameter and CHW are not as influential as expected in distinguishing high-efficiency from low-efficiency pipes. Further, a comparison between the approach used in this paper and a simplified common-practice replacement strategy points out the difference energy considerations can make, if included in a bigger asset management landscape.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".