MétaCan
Menu
Back to cohort
Record W2331313822 · doi:10.1061/41203(425)138

Hydraulic Calibration for a Small Water Distribution Network

2011· article· en· W2331313822 on OpenAlexafffund
Hailiang Shen, Edward A. McBean

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSolverCalibrationSensitivity (control systems)Computer scienceMonte Carlo methodGenetic algorithmSimulationMathematical optimizationControl theory (sociology)EngineeringElectronic engineeringMathematicsStatisticsArtificial intelligence

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.155

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.164
Teacher spread0.142 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2011
Admission routes2
Has abstractyes

Explore more

Same topicWater Systems and OptimizationFrench-language works237,207