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Record W2327427422 · doi:10.1115/icnmm2012-73216

Simulation of Constant Pressure and Flow Rate Through Mini-Channels Inserted Into Distribution Systems (WDS)

2012· article· en· W2327427422 on OpenAlexaff
Muinul H. Banna, Homayoun Najjaran, Rehan Sadiq, Manuel J. Rodríguez, Syed Imran, Mina Hoorfar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsUniversité LavalUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsConstant (computer programming)Volumetric flow rateFlow (mathematics)MechanicsSteady state (chemistry)Water flowEnvironmental scienceControl theory (sociology)Computer scienceSimulationEnvironmental engineeringPhysicsChemistry

Abstract

fetched live from OpenAlex

The miniaturised online sensors that were developed in the laboratories were for atmospheric pressure and steady state flow, but in the water distribution network neither the pressure nor the flow is steady. Many of the state of the art drinking water quality monitoring sensors can be operated well below the drinking Water Distribution System (WDS) pressure. Moreover, each of the sensors requires different flow rates. This paper discusses simulation and design of an affordable constant flow and constant outlet pressure system and shows an easy way to provide different flow rates for different sensors. The other criterion which should be met is the flow rate of the water bled (leakage) from WDS which must also be low. To meet the above criteria a 2-D model was developed to represent the constant pressure constant flow system for online water quality monitoring (WQM) sensors. Different configuration of the system is considered and the optimum design includes 1.044 m/s flow velocity which is low enough for the flow to be steady.

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 score0.905
Threshold uncertainty score0.324

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 head0.013
GPT teacher head0.213
Teacher spread0.200 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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