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Record W3015068370 · doi:10.3390/w12041037

A Direct Approach for the Near-Optimal Design of Water Distribution Networks Based on Power Use

2020· article· en· W3015068370 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWater · 2020
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsQueen's University
Fundersnot available
KeywordsBenchmark (surveying)Mathematical optimizationComputer scienceOptimal designEngineering design processPower flowProcess (computing)Hydraulic machineryPower (physics)MetaheuristicDistribution (mathematics)Electric power systemMathematicsEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

In recent years, iterative computational techniques have been considered as the most effective methods to tackle the problem of Water Distribution System (WDS) minimum-cost design. Given their stochastic nature, these approaches involve a large number of hydraulic simulations in order to obtain suitable results. Herein, a WDS design methodology based entirely on hydraulic principles is presented. This methodology, named Optimal Power Use Surface (OPUS), focuses on both reaching low-cost designs and diminishing the number of hydraulic executions (iterations), by establishing efficient ways in which energy is dissipated and flow is distributed throughout the system. The algorithm was tested in four well known benchmark networks, previously reported in the literature. OPUS proved that following hydraulic principles is a fair choice to design WDS, showing plenty of potential in other water distribution mathematical modeling applications and offering an alternative for the extensive search process undertaken by metaheuristics.

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.

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 score0.991
Threshold uncertainty score0.205

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 head0.020
GPT teacher head0.177
Teacher spread0.156 · 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