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    Topology Optimization of Robust District Heating Networks

    Source: Journal of Energy Resources Technology:;2018:;volume 140:;issue 002::page 20905
    Author:
    Pizzolato, Alberto
    ,
    Sciacovelli, Adriano
    ,
    Verda, Vittorio
    DOI: 10.1115/1.4038312
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Large district heating networks greatly benefit from topological changes brought by the construction of loops. The overall effects of malfunctions are smoothed, making existing networks intrinsically robust. In this paper, we demonstrate the use of topology optimization to find the network layout that maximizes robustness under an investment constraint. The optimized design stems from a large ground structure that includes all the possible looping elements. The objective is an original robustness measure, that neither requires any probabilistic analysis of the input uncertainty nor the identification of bounds on stochastic variables. Our case study on the Turin district heating network confirms that robustness and cost are antagonist objectives: the optimized designs obtained by systematically relaxing the investment constraint lay on a smooth Pareto front. A sudden steepness variation divides the front in two different regions. For small investments topological modifications are observed, i.e., new branches appear continuously in the optimized layout as the investment increases. Here, large robustness improvements are possible. However, at high investments no topological modifications are visible and only limited robustness gains are obtained.
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      Topology Optimization of Robust District Heating Networks

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4250934
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    • Journal of Energy Resources Technology

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    contributor authorPizzolato, Alberto
    contributor authorSciacovelli, Adriano
    contributor authorVerda, Vittorio
    date accessioned2019-02-28T10:56:03Z
    date available2019-02-28T10:56:03Z
    date copyright11/14/2017 12:00:00 AM
    date issued2018
    identifier issn0195-0738
    identifier otherjert_140_02_020905.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4250934
    description abstractLarge district heating networks greatly benefit from topological changes brought by the construction of loops. The overall effects of malfunctions are smoothed, making existing networks intrinsically robust. In this paper, we demonstrate the use of topology optimization to find the network layout that maximizes robustness under an investment constraint. The optimized design stems from a large ground structure that includes all the possible looping elements. The objective is an original robustness measure, that neither requires any probabilistic analysis of the input uncertainty nor the identification of bounds on stochastic variables. Our case study on the Turin district heating network confirms that robustness and cost are antagonist objectives: the optimized designs obtained by systematically relaxing the investment constraint lay on a smooth Pareto front. A sudden steepness variation divides the front in two different regions. For small investments topological modifications are observed, i.e., new branches appear continuously in the optimized layout as the investment increases. Here, large robustness improvements are possible. However, at high investments no topological modifications are visible and only limited robustness gains are obtained.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleTopology Optimization of Robust District Heating Networks
    typeJournal Paper
    journal volume140
    journal issue2
    journal titleJournal of Energy Resources Technology
    identifier doi10.1115/1.4038312
    journal fristpage20905
    journal lastpage020905-9
    treeJournal of Energy Resources Technology:;2018:;volume 140:;issue 002
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian