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Record W3189408373 · doi:10.1139/cjce-2020-0604

Regulation response of pilot-operated diaphragm type pressure-reducing valves: laboratory testing and impact on the performance of pressure control modes in water distribution systems

2021· article· en· W3189408373 on OpenAlexaffvenueabout
Mouna Doghri, Sophie Duchesne, Annie Poulin, J. P. Villeneuve

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à MontréalInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsPressure regulatorControl valvesPressure controlRelief valveLeakage (economics)Diaphragm (acoustics)Pressure sensorLimit (mathematics)Control theory (sociology)EngineeringEnvironmental scienceControl (management)Computer scienceControl engineeringMechanical engineeringMathematics

Abstract

fetched live from OpenAlex

Pressure control is recognized as an efficient measure for reducing leaks from water distribution systems. We have evaluated the effectiveness of various pressure control modes by means of pilot-operated diaphragm type pressure-reducing valves (PRVs), taking into account the sensitivity of the valves to various settings. First, the response of a PRV to consecutive pressure settings variations was experimentally evaluated in the hydraulic simulation laboratory of National Institute for Scientific Research. These experiments revealed that the studied valve only reacts when variation in the pressure setting corresponds to at least a 1/6 turn of the pilot valve. Second, an actual case study from Quebec City, Canada, was simulated to evaluate the impact of the PRV response on three pressure control modes: fixed control, time-based control, and real-time control (RTC). The results show that RTC of pressure leads to reductions in leakage rate for the studied network but that the PRV operational constraints limit the expected performance of RTC.

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)0.001
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: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.171
Teacher spread0.164 · 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

Citations3
Published2021
Admission routes3
Has abstractyes

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