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Record W2162815230 · doi:10.14796/jwmm.r241-01

Numeric Modeling of Water Mains Filling Considering Air Pressurization

2011· article· en· W2162815230 on OpenAlexvenueno aff
Gabriel M. Leite, José G. Vasconcelos

Bibliographic record

VenueJournal of Water Management Modeling · 2011
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsCabin pressurizationMains electricityEnvironmental scienceMechanicsMaterials scienceEngineeringComposite materialElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

The filling of water mains represents a type of unsteady, two phase flow that normally follows maintenance procedures which require emptying the mains.This is performed carefully to avoid the formation of air pockets, which can cause high pressures in conduits and also reduce their conveyance capacity.Numerical modeling is an essential tool for the study of this kind of problem as a means of anticipating operational issues.Among the models proposed for this application are those of Liou and Hunt (1996), Izquierdo et al. (1999) and Vasconcelos (2007).Such models' applicability is limited due to the assumptions made in their development, which include simplified shapes of water filling fronts, and non-consideration of ventilation systems or air pressurization effects.This chapter will present a numerical model for the simulation of gradual water mains filling that allows for both a more realistic inflow front and the development of air pressurization.The model solves a system of ordinary differential equations which represent the advance of the inflow front, the development of air pressurization, and conservation of the air phase.The model uses Object Pascal and has a friendly and interactive user interface, and future tests will present a comparison between model predictions and laboratory measurements.

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 score0.740
Threshold uncertainty score0.511

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 communication0.0000.001
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 head0.027
GPT teacher head0.185
Teacher spread0.158 · 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
Published2011
Admission routes1
Has abstractyes

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