Algorithms for optimization of branching gravity-driven water networks

The design of a water network involves the selection of pipe diameters that satisfy pressure and flow requirements while considering cost. A variety of design approaches can be used to optimize for hydraulic performance or reduce costs. To help designers select an appropriate approach in the cont...

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Main Authors: I. Dardani, G. F. Jones
Format: Article
Language:English
Published: Copernicus Publications 2018-05-01
Series:Drinking Water Engineering and Science
Online Access:https://www.drink-water-eng-sci.net/11/67/2018/dwes-11-67-2018.pdf
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spelling doaj-37848e298fc84ae59f8b3ca9a002a9262021-03-02T09:59:50ZengCopernicus PublicationsDrinking Water Engineering and Science1996-94571996-94652018-05-0111678510.5194/dwes-11-67-2018Algorithms for optimization of branching gravity-driven water networksI. Dardani0G. F. Jones1College of Engineering, Villanova University, Villanova, PA 19085, USACollege of Engineering, Villanova University, Villanova, PA 19085, USAThe design of a water network involves the selection of pipe diameters that satisfy pressure and flow requirements while considering cost. A variety of design approaches can be used to optimize for hydraulic performance or reduce costs. To help designers select an appropriate approach in the context of gravity-driven water networks (GDWNs), this work assesses three cost-minimization algorithms on six moderate-scale GDWN test cases. Two algorithms, a backtracking algorithm and a genetic algorithm, use a set of discrete pipe diameters, while a new calculus-based algorithm produces a continuous-diameter solution which is mapped onto a discrete-diameter set. The backtracking algorithm finds the global optimum for all but the largest of cases tested, for which its long runtime makes it an infeasible option. The calculus-based algorithm's discrete-diameter solution produced slightly higher-cost results but was more scalable to larger network cases. Furthermore, the new calculus-based algorithm's continuous-diameter and mapped solutions provided lower and upper bounds, respectively, on the discrete-diameter global optimum cost, where the mapped solutions were typically within one diameter size of the global optimum. The genetic algorithm produced solutions even closer to the global optimum with consistently short run times, although slightly higher solution costs were seen for the larger network cases tested. The results of this study highlight the advantages and weaknesses of each GDWN design method including closeness to the global optimum, the ability to prune the solution space of infeasible and suboptimal candidates without missing the global optimum, and algorithm run time. We also extend an existing closed-form model of Jones (2011) to include minor losses and a more comprehensive two-part cost model, which realistically applies to pipe sizes that span a broad range typical of GDWNs of interest in this work, and for smooth and commercial steel roughness values.https://www.drink-water-eng-sci.net/11/67/2018/dwes-11-67-2018.pdf
collection DOAJ
language English
format Article
sources DOAJ
author I. Dardani
G. F. Jones
spellingShingle I. Dardani
G. F. Jones
Algorithms for optimization of branching gravity-driven water networks
Drinking Water Engineering and Science
author_facet I. Dardani
G. F. Jones
author_sort I. Dardani
title Algorithms for optimization of branching gravity-driven water networks
title_short Algorithms for optimization of branching gravity-driven water networks
title_full Algorithms for optimization of branching gravity-driven water networks
title_fullStr Algorithms for optimization of branching gravity-driven water networks
title_full_unstemmed Algorithms for optimization of branching gravity-driven water networks
title_sort algorithms for optimization of branching gravity-driven water networks
publisher Copernicus Publications
series Drinking Water Engineering and Science
issn 1996-9457
1996-9465
publishDate 2018-05-01
description The design of a water network involves the selection of pipe diameters that satisfy pressure and flow requirements while considering cost. A variety of design approaches can be used to optimize for hydraulic performance or reduce costs. To help designers select an appropriate approach in the context of gravity-driven water networks (GDWNs), this work assesses three cost-minimization algorithms on six moderate-scale GDWN test cases. Two algorithms, a backtracking algorithm and a genetic algorithm, use a set of discrete pipe diameters, while a new calculus-based algorithm produces a continuous-diameter solution which is mapped onto a discrete-diameter set. The backtracking algorithm finds the global optimum for all but the largest of cases tested, for which its long runtime makes it an infeasible option. The calculus-based algorithm's discrete-diameter solution produced slightly higher-cost results but was more scalable to larger network cases. Furthermore, the new calculus-based algorithm's continuous-diameter and mapped solutions provided lower and upper bounds, respectively, on the discrete-diameter global optimum cost, where the mapped solutions were typically within one diameter size of the global optimum. The genetic algorithm produced solutions even closer to the global optimum with consistently short run times, although slightly higher solution costs were seen for the larger network cases tested. The results of this study highlight the advantages and weaknesses of each GDWN design method including closeness to the global optimum, the ability to prune the solution space of infeasible and suboptimal candidates without missing the global optimum, and algorithm run time. We also extend an existing closed-form model of Jones (2011) to include minor losses and a more comprehensive two-part cost model, which realistically applies to pipe sizes that span a broad range typical of GDWNs of interest in this work, and for smooth and commercial steel roughness values.
url https://www.drink-water-eng-sci.net/11/67/2018/dwes-11-67-2018.pdf
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