Pipeline Optimization Accounting for Transient Conditions: Exploring the Connections between System Configuration, Operation, and Surge Protection
Bibliographic record
Abstract
The optimal design of a water distribution system under worst-case transient loadings is formulated as a two-step optimization problem. In the first step, particle swarm optimization is used to identify the set of critical nodes that will result in the worst case transient loading condition. In the second step, dual-objective optimization is used to determine the optimal pipe sizes that simultaneously minimize cost and the likelihood of damaging transient events, measured by a parameter called the surge damage potential factor. Nondominated sorting genetic algorithms are combined with transient analysis to produce a set of Pareto-optimal solutions in the search space of pipe cost and surge damage potential factor. The case study tested on the New York tunnel system confirmed that pipe size is a significant factor in controlling transient response. It is concluded that transient consideration in the design phase, in conjunction with conventional least-cost pipe size optimization, will help water utilities achieve a high degree of hydraulic integrity and reliability and extend the life of their distribution systems. Enhancement of water distribution system planning and management is a principal benefit of the proposed methodology.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".