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    Parallel Computing Enables Whole Trip Train Dynamics Optimizations

    Source: Journal of Computational and Nonlinear Dynamics:;2016:;volume( 011 ):;issue: 004::page 44503
    Author:
    Wu, Qing
    ,
    Cole, Colin
    ,
    Spiryagin, Maksym
    DOI: 10.1115/1.4032075
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Due to the high computing demand of wholetrip train dynamics simulations and the iterative nature of optimizations, wholetrip train dynamics optimizations using sequential computing schemes are practically impossible. This paper reports advancements in wholetrip train dynamics optimizations enabled by using the parallel computing technique. A parallel computing scheme for wholetrip train dynamics optimizations is presented and discussed. Two case studies using parallel multiobjective particle swarm optimization (pMOPSO) and parallel multiobjective genetic algorithm (pMOGA), respectively, were performed to optimize a friction draft gear design. Linear speedup was achieved by using parallel computing to cut down the computing time from 18 months to just 11 days. Optimized results using pMOPSO and pMOGA were in agreement with each other; Pareto fronts were identified to provide technical evidence for railway manufacturers and operators.
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      Parallel Computing Enables Whole Trip Train Dynamics Optimizations

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    http://yetl.yabesh.ir/yetl1/handle/yetl/160517
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    contributor authorWu, Qing
    contributor authorCole, Colin
    contributor authorSpiryagin, Maksym
    date accessioned2017-05-09T01:26:31Z
    date available2017-05-09T01:26:31Z
    date issued2016
    identifier issn1555-1415
    identifier othercnd_011_04_044503.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/160517
    description abstractDue to the high computing demand of wholetrip train dynamics simulations and the iterative nature of optimizations, wholetrip train dynamics optimizations using sequential computing schemes are practically impossible. This paper reports advancements in wholetrip train dynamics optimizations enabled by using the parallel computing technique. A parallel computing scheme for wholetrip train dynamics optimizations is presented and discussed. Two case studies using parallel multiobjective particle swarm optimization (pMOPSO) and parallel multiobjective genetic algorithm (pMOGA), respectively, were performed to optimize a friction draft gear design. Linear speedup was achieved by using parallel computing to cut down the computing time from 18 months to just 11 days. Optimized results using pMOPSO and pMOGA were in agreement with each other; Pareto fronts were identified to provide technical evidence for railway manufacturers and operators.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleParallel Computing Enables Whole Trip Train Dynamics Optimizations
    typeJournal Paper
    journal volume11
    journal issue4
    journal titleJournal of Computational and Nonlinear Dynamics
    identifier doi10.1115/1.4032075
    journal fristpage44503
    journal lastpage44503
    identifier eissn1555-1423
    treeJournal of Computational and Nonlinear Dynamics:;2016:;volume( 011 ):;issue: 004
    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