MétaCan
Menu
Retour à la cohorte
Enregistrement W4392420726 · doi:10.4995/wdsa-ccwi2022.2022.14040

Experimental study of mixing phenomenon in water distribution networks under real-world conditions

2022· article· en· W4392420726 sur OpenAlexaffabout
Reza Yousefian, Sophie Duchesne

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineEngineering
ThématiqueWater Systems and Optimization
Établissements canadiensInstitut National de la Recherche Scientifique
Organismes subventionnairesnon disponible
Mots-clésMixing (physics)PhenomenonDistribution (mathematics)Computer sciencePhysicsMathematicsQuantum mechanicsMathematical analysis

Résumé

récupéré en direct d'OpenAlex

Various studies have shown that mixing at the junction of water distribution networks (WDNs) can be neither complete nor instantaneous. Many studies have also been carried out to find the parameters that have a significant effect on the mixing phenomenon by experimental and numerical investigations. The Reynolds ratio of inlet flows, the Reynold ratio of outlet flows, the pipe size of junction's legs (junction configurations), and the type of junction (cross or double-t) are among the most important factors mentioned in these studies. Other studies also focused on developing mixing models. However, these studies and models were based on experimental conditions in which the pipe size was about 25 mm, and the pressure was about 30 kPa. Despite the fact that these models provide acceptable results on the laboratory scale and all of them have been validated based on laboratory results, many researchers still acknowledge that studies on the effects of real conditions of urban WDNs on the mixing phenomenon are lacking [1], [2] and [3]. The pipe size of junctions and the pressure inside the network are distinguishing features of real urban WDNs and laboratory setups that were used in previous studies. Therefore, this research investigates the effect of pipe size and pressure on the mixing phenomenon in a cross junction with the same pipe sizes in all four legs under real-world conditions. Flow rates and pressures were selected based on a statistical study conducted on the Quebec City WDN and previous research work, in which the pressures were 5, 140, 320 and 430 kPa, and the flow rates were a combination of 1.50, 2.00, 2.25, 2.50 and 3.00 LPS in each inlet and outlet pipes. In other words, the Reynolds number in the experiments was between 21,000 and 43,000, and the Reynolds ratio of inlet flows as well as the Reynolds ratio of outlet flows were 0.5, 0.8, 1, 1.25 and 2. In this network, two cross junctions with the same pipe size in each junction's legs of 100 and 150 mm were investigated. Salt was used as a traceable solute, and conductivity meters were used to measure the salt concentration in pipes. In this series of experiments, only one inlet had salty water. The mixing was characterized by the dimensionless concentration in outlets, defined as the observed conductivity in one of the outlet flow divided by the inlet salty water conductivity (after subtracting the conductivity of tap water for both measurements). The results showed that the pressure had only a little effect on mixing, since the dimensionless concentration changed by about 0.05 when considering the low pressure of 5 kPa as compared to the experiments with the other pressure values, while this variation dropped to about 0.02 for all pressures above 140 kPa. Between 100 and 150 mm, the pipe size modified the dimensionless concentration by about 0.035. Taking into account the uncertainty of the experiments, it can be concluded that pressure and pipe diameters (100 and 150 mm) have an insignificant impact on the mixing phenomenon.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,058
Score d'incertitude au seuil0,371

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,013
Tête enseignante GPT0,229
Écart entre enseignants0,216 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2022
Routes d'admission2
Résumé présentoui

Explorer davantage

Même sujetWater Systems and OptimizationTravaux en français237 207