Hydraulic Calibration for a Small Water Distribution Network
Bibliographic record
Abstract
Procedures utilized for hydraulic model calibration for the C-Town network, which is the first step prior to a model being useful for operation and maintenance, and water quality model construction, is described. For the C-Town network as provided by BWCN, seven groups of parameters are identified, namely, pipe roughness, elevation, leakage coefficient, pump curve, base demand, pattern value, and pump and valve control. The calibration is formulated as an optimization problem, aimed at minimizing the discrepancy between observed and simulated data. A toolkit is developed within VC++ 2008 Express to solve the optimization problem, by employing a flexible genetic algorithm library GAlib as the optimization engine, and EPANET as the hydraulic solver. Monte Carlo simulation is applied for sensitivity analyses to identify sensitive parameters to feed the calibration process. It is shown the seven groups of parameters have similar sensitivity and all feed to calibration process. The tank levels are relatively well calibrated, comparing with the pump station flow rates.
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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".