Abstract:A flexible framework for modeling different water conveyance networks is presented. The network is modeled using a linear canal pool model based on the Saint-Venant equations to describe transportation phenomenon occurring in open channels. This model is used as a link to connect different nodes defined by gates or reservoirs. The linear pool model has interesting features namely the pool axis monitoring, the inflow along the pool axis and the ability to consider different boundary conditions. Based on these characteristics canal pool observers for leak detection and localization can be developed. It is shown that based on a finite difference scheme a good performance is obtained for low space resolution. The modeling framework is validated with experimental data from a real canal property of theÉvora University. This is a challenging configuration due to its strong canal pool coupling.
Water is vital for human life.Water is used widespread from agricultural to industrial as well as simple domestic activities. Mostly due to the increase on world population, water is becoming a sparse and valuable resource, pushing a high demand on the design of efficient engineering water distribution control systems. This paper presents a simple yet sufficiently rich and flexible solution to model open-channels. The hydraulic model is based on the Saint-Venant equations which are then linearized and transformed into a state space dynamic model. The resulting model is shown to be able to incorporate different boundary conditions like discharge, water depth or hydraulic structure dynamics, features that are commonly present on any water distribution system. Besides, due its computational simplicity and efficient monitoring capacity, the resulting hydraulic model is easily integrated into safety and fault tolerant control strategies. In this paper the hydraulic model is successfully validated using experimental data from a water canal setup.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.