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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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 |
work_keys_str_mv |
AT idardani algorithmsforoptimizationofbranchinggravitydrivenwaternetworks AT gfjones algorithmsforoptimizationofbranchinggravitydrivenwaternetworks |
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