Abstract
Abstract Recently, smart water application has gained worldwide attention, but there is a lack of understanding of how to construct smart water networks. This is partly because of the limited investigation into how to combine physical experiments with model simulations. This study aimed to investigate the process of connecting micro-smart water test bed (MWTB) and a ‘two-loop’ ENAPENT hydraulic model, which involves experimental set-up, real-time data acquisition, hydraulic simulation, and system performance demonstration. In this study, a MWTB was established based on the flow sensing technology. The data generated by the MWTB were stored in Observation Data Model (ODM) database for visualization in RStudio environment and also archived as the input of EPANET hydraulic simulation. The data visualization fitted the operation scenarios of the MWTB well. Additionally, the fitting degree between the experimental measurements and modeling outputs indicates the ‘two-loop’ ENAPNET model can represent the operation of MWTB for better understanding of hydraulic analysis.
Highlights
Smart systems, which were first introduced in the field of electricity, have reached the drinking water sector to realize real-time data acquisition, organization, and analysis (Abu-Mahfouz et al 2012)
smart water system (SWS) was the product of the integration of automated control technology, information communication technology (ICT), and traditional water systems, aiming to solve water issues more efficiently
To investigate how micro-smart water test bed (MWTB) can contribute to communities, this paper explored the potential of connecting the smart water test bed and hydraulic model in experimental practice
Summary
Smart systems, which were first introduced in the field of electricity, have reached the drinking water sector to realize real-time data acquisition, organization, and analysis (Abu-Mahfouz et al 2012). The smart water system (SWS) has received much attention in recent years due to its intelligent functions. One way to clear up this confusion is to implement the SWS in water networks, and extensive research has been undertaken to discuss how to apply the SWS in field cases. Boulos (2017) developed a cyber-physical concept-based smart flood information system called Dayu SWS. This intelligent water system created an on-site monitoring network and integrated it into rapid flood modeling to provide updated information. This intelligent water system created an on-site monitoring network and integrated it into rapid flood modeling to provide updated information. Bartos et al (2018) demonstrated a dynamic system-level smart stormwater system which was implemented in Ann Arbor, Michigan, USA
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