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Shaping Streamflow Using a Real-Time Stormwater Control Network

“Smart” water systems are transforming the field of stormwater management by enabling real-time monitoring and control of previously static infrastructure. While the localized benefits of active control are well-established, the potential for system-scale control of watersheds is poorly understood....

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Detalles Bibliográficos
Autores principales: Mullapudi, Abhiram, Bartos, Matthew, Wong, Brandon, Kerkez, Branko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6068808/
https://www.ncbi.nlm.nih.gov/pubmed/30011820
http://dx.doi.org/10.3390/s18072259
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author Mullapudi, Abhiram
Bartos, Matthew
Wong, Brandon
Kerkez, Branko
author_facet Mullapudi, Abhiram
Bartos, Matthew
Wong, Brandon
Kerkez, Branko
author_sort Mullapudi, Abhiram
collection PubMed
description “Smart” water systems are transforming the field of stormwater management by enabling real-time monitoring and control of previously static infrastructure. While the localized benefits of active control are well-established, the potential for system-scale control of watersheds is poorly understood. This study shows how a real-world smart stormwater system can be leveraged to shape streamflow within an urban watershed. Specifically, we coordinate releases from two internet-controlled stormwater basins to achieve desired control objectives downstream—such as maintaining the flow at a set-point, and generating interleaved waves. In the first part of the study, we describe the construction of the control network using a low-cost, open-source hardware stack and a cloud-based controller scheduling application. Next, we characterize the system’s control capabilities by determining the travel times, decay times, and magnitudes of various waves released from the upstream retention basins. With this characterization in hand, we use the system to generate two desired responses at a critical downstream junction. First, we generate a set-point hydrograph, in which flow is maintained at an approximately constant rate. Next, we generate a series of overlapping and interleaved waves using timed releases from both retention basins. We discuss how these control strategies can be used to stabilize flows, thereby mitigating streambed erosion and reducing contaminant loads into downstream waterbodies.
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spelling pubmed-60688082018-08-07 Shaping Streamflow Using a Real-Time Stormwater Control Network Mullapudi, Abhiram Bartos, Matthew Wong, Brandon Kerkez, Branko Sensors (Basel) Article “Smart” water systems are transforming the field of stormwater management by enabling real-time monitoring and control of previously static infrastructure. While the localized benefits of active control are well-established, the potential for system-scale control of watersheds is poorly understood. This study shows how a real-world smart stormwater system can be leveraged to shape streamflow within an urban watershed. Specifically, we coordinate releases from two internet-controlled stormwater basins to achieve desired control objectives downstream—such as maintaining the flow at a set-point, and generating interleaved waves. In the first part of the study, we describe the construction of the control network using a low-cost, open-source hardware stack and a cloud-based controller scheduling application. Next, we characterize the system’s control capabilities by determining the travel times, decay times, and magnitudes of various waves released from the upstream retention basins. With this characterization in hand, we use the system to generate two desired responses at a critical downstream junction. First, we generate a set-point hydrograph, in which flow is maintained at an approximately constant rate. Next, we generate a series of overlapping and interleaved waves using timed releases from both retention basins. We discuss how these control strategies can be used to stabilize flows, thereby mitigating streambed erosion and reducing contaminant loads into downstream waterbodies. MDPI 2018-07-13 /pmc/articles/PMC6068808/ /pubmed/30011820 http://dx.doi.org/10.3390/s18072259 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mullapudi, Abhiram
Bartos, Matthew
Wong, Brandon
Kerkez, Branko
Shaping Streamflow Using a Real-Time Stormwater Control Network
title Shaping Streamflow Using a Real-Time Stormwater Control Network
title_full Shaping Streamflow Using a Real-Time Stormwater Control Network
title_fullStr Shaping Streamflow Using a Real-Time Stormwater Control Network
title_full_unstemmed Shaping Streamflow Using a Real-Time Stormwater Control Network
title_short Shaping Streamflow Using a Real-Time Stormwater Control Network
title_sort shaping streamflow using a real-time stormwater control network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6068808/
https://www.ncbi.nlm.nih.gov/pubmed/30011820
http://dx.doi.org/10.3390/s18072259
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